tensorflow@2.4.2 vulnerabilities

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Integer Overflow or Wraparound due to the array_ops.upper_bound function. An attacker can cause a denial of service by providing input that is not a rank 2 tensor.

Upgrade tensorflow to version 2.12.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to a malicious invalid input with zero dimension, which crashes a TensorFlow model (Check Failed).

Note: An attacker must have privilege to provide input to a Convolution3DTranspose call.

Upgrade tensorflow to version 2.11.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Heap-based Buffer Overflow. Attackers can access heap memory which is not in the control of user, leading to a crash or remote code execution. The fix will be included in TensorFlow version 2.12.0 and will also cherrypick this commit on TensorFlow version 2.11.1.

Upgrade tensorflow to version 2.11.1, 2.12.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference when SparseSparseMaximum is given invalid sparse tensors as inputs.

Upgrade tensorflow to version 2.11.1, 2.12.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when running with XLA, tf.raw_ops.ParallelConcat segfaults with a nullptr dereference when given a parameter shape with rank that is not greater than zero.

Upgrade tensorflow to version 2.11.1, 2.12.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference due to a null pointer error in RandomShuffle with XLA enabled.

Upgrade tensorflow to version 2.11.1, 2.12.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to a floating point exception in TensorListSplit with XLA.

Upgrade tensorflow to version 2.11.1, 2.12.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference. The function tf.raw_ops.LookupTableImportV2 cannot handle scalars in the values parameter and gives a null pointer exception.

Upgrade tensorflow to version 2.11.1, 2.12.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Incorrect Comparison. Constructing a tflite model with a paramater filter_input_channel of less than 1 gives a float pointer exception.

Upgrade tensorflow to version 2.11.1, 2.12.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). When running with XLA, tf.raw_ops.Bincount segfaults when given a parameter weights that is neither the same shape as parameter arr nor a length-0 tensor.

Upgrade tensorflow to version 2.11.1, 2.12.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Buffer Overflow in TAvgPoolGrad.

Upgrade tensorflow to version 2.11.1, 2.12.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Integer Overflow to Buffer Overflow when 2^31 <= num_frames * height * width * channels < 2^32, for example Full HD screencast of at least 346 frames.

Upgrade tensorflow to version 2.11.1, 2.12.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Integer Overflow or Wraparound in EditDistance. A fix is included in TensorFlow version 2.12.0 and version 2.11.1.

Upgrade tensorflow to version 2.11.1, 2.12.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-Bounds due to mismatched integer type sizes in ValueMap::Manager::GetValueOrCreatePlaceholder, because there is a bug with the tfg-translate call to InitMlir.

Upgrade tensorflow to version 2.11.1, 2.12.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to a floating point exception if the stride and window size are not positive for tf.raw_ops.AvgPoolGrad.

Upgrade tensorflow to version 2.11.1, 2.12.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference. When ctx->step_containter() is a null ptr, the Lookup function will be executed with a null pointer.

Upgrade tensorflow to version 2.11.1, 2.12.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). When the parameter summarize of tf.raw_ops.Print is zero, the new method SummarizeArray<bool> will reference to a nullptr, leading to a seg fault.

Upgrade tensorflow to version 2.11.1, 2.12.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Double Free. The nn_ops.fractional_avg_pool_v2 and nn_ops.fractional_max_pool_v2 functions require the first and fourth elements of their parameter pooling_ratio to be equal to 1.0, as pooling on batch and channel dimensions is not supported.

Upgrade tensorflow to version 2.11.1, 2.12.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference in QuantizedMatMulWithBiasAndDequantize with MKL enabled.

Upgrade tensorflow to version 2.11.1, 2.12.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to a floating point exception in AudioSpectrogram.

Upgrade tensorflow to version 2.11.1, 2.12.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-bounds Read if the parameter indices for DynamicStitch does not match the shape of the parameter data.

Upgrade tensorflow to version 2.11.1, 2.12.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-bounds Read in GRUBlockCellGrad.

Upgrade tensorflow to version 2.11.1, 2.12.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to another discovered instance of CVE-2022-35991, in TensorListScatter and TensorListScatterV2 via non scalar inputs.

Upgrade tensorflow to version 2.8.4, 2.9.3, 2.10.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Heap-based Buffer Overflow in QuantizeAndDequantizeV2, via the MakeGrapplerFunctionItem function, if the inputs are greater than or equal to the sizes of outputs.

Upgrade tensorflow to version 2.8.4, 2.9.3, 2.10.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to another discovered instance of CVE-2022-35935 in SobolSample via assumed scalar inputs.

Upgrade tensorflow to version 2.8.4, 2.9.3, 2.10.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-bounds Write via the MakeGrapplerFunctionItem function, if the inputs given are greater than or equal to the sizes of the outputs.

Upgrade tensorflow to version 2.8.4, 2.9.3, 2.10.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-Bounds in DynamicStitch due to missing validation when it receives a differing number of inputs, such as when it is called with an indices size 1 and a data size 2.

Upgrade tensorflow to version 2.10.1, 2.11.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Always-Incorrect Control Flow Implementation when a numpy array is created with a shape such that one element is zero and the sum of others is a large number.

Upgrade tensorflow to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Buffer Overflow via tf.raw_ops.ImageProjectiveTransformV2 when a large output shape is given.

Upgrade tensorflow to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Incorrect Calculation of Buffer Size via tf.keras.losses.poisson which receives a y_pred and y_true that are passed through functor::mul in BinaryOp. If the resulting dimensions overflow an int32, TensorFlow will crash due to a size mismatch during broadcast assignment.

Upgrade tensorflow to version 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-bounds Read when the BaseCandidateSamplerOp function receives a value in true_classes larger than range_max.

Upgrade tensorflow to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Buffer Overflow via tf.raw_ops.FusedResizeAndPadConv2D when a large tensor shape is given.

Upgrade tensorflow to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Incorrect Calculation of Buffer Size when tf.raw_ops.ResizeNearestNeighborGrad is given a large size input.

Upgrade tensorflow to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Improper Input Validation due to a missing check of tf.image.generate_bounding_box_proposals that receives a scores input that must be of rank 4 when running on GPU.

Upgrade tensorflow to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) because the conversions from char to bool are undefined if the char is not 0 or 1. This can happen when printing a tensor: the data is got as a const char* array and then it is typecasted to the element type.

Upgrade tensorflow to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when ThreadUnsafeUnigramCandidateSampler is given input filterbank_channel_count greater than the allowed max size.

Upgrade tensorflow to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Reachable Assertion when tf.raw_ops.TensorListResize is given a nonscalar value for input size. It will results in a CHECK fail which can be used to trigger a denial of service attack.

Upgrade tensorflow to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-bounds Read. This is If MirrorPadGrad is given outsize input paddings.

Upgrade tensorflow to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when FractionMaxPoolGrad is given outsize inputs row_pooling_sequence and col_pooling_sequence.

Upgrade tensorflow to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when tf.raw_ops.TensorListConcat is given element_shape=[].

Upgrade tensorflow to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when BCast::ToShape is given input larger than an int32, even if it is being supposed to handle up to an int64.

Upgrade tensorflow to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference because the pywrap code fails to parse the tensor and returns a nullptr if a list of quantized tensors is assigned to an attribute.

Upgrade tensorflow to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Buffer Overflow. The reference kernel of the CONV_3D_TRANSPOSE TensorFlow Lite operator wrongly increments the data_ptr when adding the bias to the result. Instead of data_ptr += num_channels; it should be data_ptr += output_num_channels; as if the number of input channels is different than the number of output channels, the wrong result will be returned and a buffer overflow will occur if num_channels > output_num_channels. An attacker can craft a model with a specific number of input channels. It is then possible to write specific values through the bias of the layer outside the bounds of the buffer.

Note: This attack only works if the reference kernel resolver is used in the interpreter.

Upgrade tensorflow to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when an input encoded is not a valid CompositeTensorVariant tensor. This will trigger a segfault in tf.raw_ops.CompositeTensorVariantToComponents.

Upgrade tensorflow to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). This is vulnerable when an input token that is not a UTF-8 bytestring will trigger a CHECK fail in tf.raw_ops.PyFunc.

Upgrade tensorflow to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-bounds Write in FractionalMax(AVG)Pool with illegal pooling_ratio. Attackers can access heap memory that is not in the user's control, leading to a crash or remote code execution.

Upgrade tensorflow to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when SparseFillEmptyRowsGrad is given empty inputs.

Upgrade tensorflow to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when the input sparse_matrix is not a matrix with a shape with rank 0. As a result, a CHECK fail will be triggered in tf.raw_ops.SparseMatrixNNZ.

Upgrade tensorflow to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). This is due to the inputs dense_features or example_state_data not being of rank 2 which will trigger a CHECK fail in SdcaOptimizer.

Upgrade tensorflow to version 2.8.4, 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-bounds Read via the GatherNd function, when the given inputs to the function are greater than or equal to the sizes of the outputs.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference when mlir::tfg::ConvertGenericFunctionToFunctionDef is given empty function attributes.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when FakeQuantWithMinMaxVarsPerChannel is given min or max tensors of a rank other than one, it results in a CHECK fail that can be used to trigger the exploitation.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when mlir::tfg::GraphDefImporter::ConvertNodeDef tries to convert NodeDefs without an op name.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when RandomPoissonV2 receives large input shapes and rates, it gives a CHECK fail that can trigger the exploitation.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when tf.sparse.cross receives an input separator that is not a scalar, it gives a CHECK fail that can be used to trigger exploitation of this vulnerability.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when Conv2DBackpropInput receives empty out_backprop inputs (e.g. [3, 1, 0, 1]).

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when AudioSummaryV2 receives an input sample_rate with more than one element, it gives a CHECK fails that can be used to trigger the exploitation of the vulnerability.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when tensorflow::full_type::SubstituteFromAttrs receives a FullTypeDef& t that is not exactly three args.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when tf.quantization.fake_quant_with_min_max_vars_gradient receives input min or max that is nonscalar, it gives a CHECK fail that can trigger the exploitation of this vulnerability.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when QuantizeAndDequantizeV3 is given a nonscalar num_bits input tensor, it results in a CHECK fail that can be used to trigger the exploitation of this vulnerability.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) in tf.reshape due to a CHECK-failure caused by overflowing the number of elements in a tensor.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when tf.random.gamma receives large input shapes and rates.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-bounds Write via the scatter_nd function in TF Lite, when an input index is greater than the output tensor or less than zero.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) via CHECK-failure caused by assuming input(0), input(1), and input(2) to be scalar.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-bounds Read. The GatherNd function takes arguments that determine the sizes of inputs and outputs. If the inputs given are greater than or equal to the sizes of the outputs, it triggers the exploitation of the vulnerability.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when the AvgPoolOp function takes an argument ksize that must be positive but is not checked. A negative ksize can trigger a CHECK failure and crash the program.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when RaggedTensorToVariant is given a rt_nested_splits list that contains tensors of ranks other than one, it results in a CHECK fail that can be used to trigger the exploitation of this vulnerability.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when DenseBincount assumes its input tensor weights to either have the same shape as its input tensor input or to be length-0. A different weights shape will trigger a CHECK fail that can be used to trigger the exploitation of this vulnerability.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when SparseBincount is given inputs for indices, values, and dense_shape that do not make a valid sparse tensor, it results in a segfault that can be used to trigger exploitation.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when LRNGrad is given an output_image input tensor that is not 4-D.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when the FractionalMaxPoolGrad function validates its inputs with CHECK failures instead of returning errors. If it gets incorrectly sized inputs, the CHECK failure can be used to trigger exploitation.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when Save or SaveSlices is run over tensors of an unsupported dtype, it results in a CHECK fail that can be used to trigger exploitation.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when tf.linalg.matrix_rank receives an empty input a, the GPU kernel gives a CHECK fail that can be used to trigger exploitation.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when tf.quantization.fake_quant_with_min_max_vars_per_channel_gradient receives input min or max of rank other than 1, it gives a CHECK fail that can trigger exploitation.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when QuantizedRelu or QuantizedRelu6 are given nonscalar inputs for min_features or max_features, it results in a segfault that can be used to trigger exploitation.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when the implementation of Conv2DBackpropInput requires input_sizes to be 4-dimensional. Otherwise, it gives a CHECK failure which can be used to trigger exploitation.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when MaxPool receives a window size input array ksize with dimensions greater than its input tensor input, the GPU kernel gives a CHECK fail that can be used to trigger exploitation.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Improper Input Validation due to improper validation of orig_input_shape in AvgPoolGrad.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference when LowerBound or UpperBound is given an empty sorted_inputs input.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when ParameterizedTruncatedNormal assumes shape is of type int32. A valid shape of type int64 results in a mismatched type CHECK fail that can be used to trigger exploitation.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when QuantizeDownAndShrinkRange is given nonscalar inputs for input_min or input_max, it results in a segfault that can be used to trigger an exploitation.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when RaggedBincount is given an empty input tensor splits, it results in a segfault that can be used to trigger the exploitation of this vulnerability.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when TensorListScatter and TensorListScatterV2 receive an element_shape of a rank greater than one, they give a CHECK fail that can trigger the exploitation of the vulnerability.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when TensorListFromTensor receives an element_shape of a rank greater than one.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when the FakeQuantWithMinMaxVars function is given min or max tensors of a nonzero rank.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Integer Overflow or Wraparound. The RaggedRangOp function takes an argument limits that is eventually used to construct a TensorShape as an int64. If limits is a very large float, it can overflow when converted to an int64. This triggers an InvalidArgument but also throws an abort signal that crashes the program.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when QuantizedInstanceNorm is given x_min or x_max tensors of a nonzero rank.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when CollectiveGather receives a scalar input input, which results in a CHECK failure.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when Unbatch receives a nonscalar input id, it results in a CHECK fail.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when QuantizedAdd is given min_input or max_input tensors of a nonzero rank.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to improper input validation of orig_input_tensor_shape in FractionalAvgPoolGrad.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to improper input validation of orig_input_shape in AvgPool3DGradOp.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when QuantizedMatMul is given nonscalar input for min_a, max_a, min_b, and max_b.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when QuantizedAvgPool is given min_input or max_input tensors of a nonzero rank.

import tensorflow as tf

ksize = [1, 2, 2, 1]
strides = [1, 2, 2, 1]
padding = "SAME"
input = tf.constant(1, shape=[1,4,4,2], dtype=tf.quint8)
min_input = tf.constant([], shape=[0], dtype=tf.float32)
max_input = tf.constant(0, shape=[1], dtype=tf.float32)
tf.raw_ops.QuantizedAvgPool(input=input, min_input=min_input, max_input=max_input, ksize=ksize, strides=strides, padding=padding)

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when a nonscalar id is provided to the UnbatchGradOp function, and batch_index is incorrect.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to improper input validation in BlockLSTMGradV2.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when QuantizedBiasAdd is given min_input, max_input, min_bias, max_bias tensors of a nonzero rank, it results in a segfault.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to a CHECK failure in TensorListReserve via missing validation. Exploiting this vulnerability is possible when a num_elements of more than 1 element is provided, then tf.raw_ops.TensorListReserve fails the CHECK_EQ in CheckIsAlignedAndSingleElement.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to a CHECK fail in SetSize, when SetSize receives an input set_shape that is not a 1D tensor.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to a CHECK fail in EmptyTensorList when it receives an input element_shape with more than one dimension.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference when Eig can be fed an incorrect Tout input, resulting in a CHECK fail that can trigger a denial of service attack.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Improper Input Validation when converting transposed convolutions using per-channel weight quantization, the converter segfaults and crashes the Python process.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference through mlir::tfg::TFOp::nameAttr, when it receives null type list attributes.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Improper Input Validation when Requantize is given input_min, input_max, requested_output_min, requested_output_max tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Division by zero when Conv2D is given empty input and the filter and padding sizes are valid, the output is all-zeros.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) when providing an empty function attributes to mlir::tfg::ConvertGenericFunctionToFunctionDef.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Integer Overflow or Wraparound when RangeSize function receives values that do not fit into an int64_t.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to a CHECK failure when DrawBoundingBoxes receives an input boxes that are not of dtype float.

Upgrade tensorflow to version 2.7.2, 2.8.1, 2.9.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). The implementation of depthwise ops in TensorFlow is vulnerable to a denial of service via CHECK-failure (assertion failure) caused by overflowing the number of elements in a tensor:

import tensorflow as tf

input = tf.constant(1, shape=[1, 4, 4, 3], dtype=tf.float32)
filter_sizes = tf.constant(1879048192, shape=[13], dtype=tf.int32)
out_backprop = tf.constant(1, shape=[1, 4, 4, 3], dtype=tf.float32)
tf.raw_ops.DepthwiseConv2dNativeBackpropFilter(
    input=input, filter_sizes=filter_sizes, out_backprop=out_backprop, strides=[1, 1, 1, 1], padding="SAME")

This is due to an incomplete fix for CVE-2021-41197.

Upgrade tensorflow to version 2.8.1, 2.7.2, 2.6.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) via the implementation of tf.histogram_fixed_width, when the values array contains Not a Number (NaN) elements. The implementation assumes that all floating-point operations are defined and then converts a floating-point result to an integer index. If values contains NaN then the result of the division is still NaN and the cast to int32 would result in a crash.

Note: This only occurs on the CPU implementation.

Upgrade tensorflow to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Improper Input Validation because the implementation of tf.raw_ops.QuantizedConv2D does not fully validate the input arguments. In this case, references get bound to nullptr for each argument that is empty.

Upgrade tensorflow to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference when the resource handle is empty.

Upgrade tensorflow to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). Certain TFLite models that were created using the TFLite model converter would crash when loaded in the TFLite interpreter. During quantization, the scale of values could be greater than 1 but the code always assumes sub-unit scaling. Thus, since the code was calling QuantizeMultiplierSmallerThanOneExp, the TFLITE_CHECK_LT assertion would trigger and abort the process.

Upgrade tensorflow to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) because the implementation of tf.raw_ops.StagePeek does not fully validate the input arguments, assuming that the index argument is a scalar when accessing its value.

Upgrade tensorflow to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). The implementation of tf.raw_ops.UnsortedSegmentJoin does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack. The code assumes num_segments is a positive scalar but there is no validation. Since this value is used to allocate the output tensor, a negative value would result in a CHECK-failure (assertion failure), as per TFSA-2021-198.

Upgrade tensorflow to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to the implementation of `tf.ragged.constant not fully validating the input arguments.

Upgrade tensorflow to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Type Confusion because the macros used for writing assertions (e.g., CHECK_LT, CHECK_GT, etc.) have incorrect logic when comparing size_t and int values. Due to type conversion rules, several of the macros would be triggered incorrectly.

Upgrade tensorflow to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference when calling tf.compat.v1.* ops which don't have support for quantized types.

Upgrade tensorflow to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Integer Overflow or Wraparound due to the improper implementation of tf.raw_ops.SpaceToBatchND which can result in a CHECK-failure.

Upgrade tensorflow to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Improper Input Validation because the implementation of tf.raw_ops.SparseTensorDenseAdd does not fully validate the input arguments. In this case, a reference gets bound to a nullptr during kernel execution which is an undefined behavior.

Upgrade tensorflow to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-bounds Write because the implementation of tf.raw_ops.EditDistance have an incomplete validation which allows users to pass negative values for loc.

Upgrade tensorflow to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to improper input validation under certain condition in tf.compat.v1.signal.rfft2d and tf.compat.v1.signal.rfft3d, results in CHECK failures.

Upgrade tensorflow to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) where the implementation of tf.raw_ops.DeleteSessionTensor does not fully validate the input arguments.

Upgrade tensorflow to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Remote Code Execution (RCE) due to improper fix of CVE-2021-41228. Exploiting this vulnerability is possible via the saved_model_cli tool and can be abused to open a reverse shell.

Upgrade tensorflow to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to the implementation of tf.raw_ops.Conv3DBackpropFilterV2 which does not fully validate that filter_sizes argument is a vector.

Upgrade tensorflow to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to the implementation of tf.raw_ops.SparseTensorToCSRSparseMatrix which does not fully validate the input arguments. It assumes that dense_shape is a vector and indices is a matrix (as part of requirements for sparse tensors) without validating it.

Upgrade tensorflow to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to the implementation of tf.raw_ops.UnsortedSegmentJoin which does not fully validate the input arguments, assuming num_segments is a scalar without validating this before accessing its value.

Upgrade tensorflow to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to the implementation of tf.raw_ops.LoadAndRemapMatrix which does not fully validate the input arguments, assuming initializing_values is a vector without validating it before accessing its value.

Upgrade tensorflow to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) where the implementation of tf.raw_ops.LSTMBlockCell does not fully validate the input arguments. The code does not validate the ranks of any of the arguments to this API call. This results in CHECK-failures when the elements of the tensor are accessed.

Upgrade tensorflow to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) where the implementation of tf.raw_ops.TensorSummaryV2 does not fully validate the input arguments.

Upgrade tensorflow to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) where the implementation of tf.raw_ops.DeleteSessionTensor does not fully validate the input arguments.

Upgrade tensorflow to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) where the implementation of tf.raw_ops.GetSessionTensor does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

Upgrade tensorflow to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) where the implementation of tf.raw_ops.QuantizeAndDequantizeV4Grad does not fully validate the input arguments, which could result in a CHECK failure.

Upgrade tensorflow to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Integer Overflow or Wraparound via the Grappler component during cost estimation for crop and resize due to these parameters being user-controlled.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) via an invalidated CHECK assertion based on user controlled arguments. This happens during the decoding of a tensor from a protobuf, when the TensorFlow process encounters such a failed assertion case if the tensors have an invalid dtype and 0 elements or an invalid shape.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference via the implementation of GetInitOp. A malicious actor can change the SaveModel protobuf format on disk before loading, to cause the find function used inside GetInitOp to return a nullptr.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) via a maliciously altered SavedModel such that AttrDef's of some operations are duplicate, causing an assertion failure (CHECK-fail).

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-Bound via a typo in TensorFlow's SpecializeType. A nested loop inside a function initializes a pointer to an argument (arg) by using the outer loop's index (i), instead of the inner loop index (j), allowing to assign to arg from outside the vector of arguments, resulting in the ability to read/write out of bounds.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-bounds Write via the set_output() function in the Grappler component, which writes to an array at a specified index, giving the user the ability to write to a specific location in memory.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) via an assertion failure. If some conditions are met, it's possible for a type to fail to specialize during shape inference. The DCHECK function is supposed to take care of this, but is a no-op in production builds and an assertion failure in debug builds, which leads to assertion failures in both cases.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference during the process of decoding a tensor from protobuf. If attributes of some mutable arguments to some operations are missing from the proto a null pointer dereference occurs. This should be taken care of by a DCHECK, however, DCHECK is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the dereferencing of the null pointer, whereas in the second case it results in a crash due to the assertion failure.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) via a CHECK assertion invalidation based on user controlled arguments during the decoding of a resource handle tensor from protobuf.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) via a crafted TFLite model that would trigger a division by zero in BiasAndClamp function implementation, as the function doesn't check if one of its arguments, bias_size, is a non-zero.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Integer Overflow or Wraparound via a maliciously crafted TFLite model that would cause an integer overflow in the TfLiteIntArrayCreate function. The TfLiteIntArrayGetSizeInBytes function, which is called from TfLiteIntArrayCreate, returns an int instead of a size_t. The user controlled variable computed_size (a variable inside the TfLiteIntArrayGetSizeInBytes function) can overflow the size of the int datatype.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Use of Uninitialized Resource via the implementation of the AssignOp function can result in copying uninitialized data to a new tensor, leading to undefined behaviour.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) via the ImmutableExecutorState::Initialize function implementation, which can result in a memory leak if a graph node is invalid.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Race Condition via the tempfile.mktemp function which is used to create temporary files. This is risky, as a different process can create the file after the check for the filename in mktemp, and the actual creation of the file by the next operation (Time of Check/Time of Use).

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-bounds Write via a crafted TFLite model that would cause a write outside of bounds of an array in TFLite. It is possible to override the linked list used by the memory allocator.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Integer Overflow. An attacker can craft a TFLite model that would cause this issue, due to the fact that both embedding_size and lookup_size are products of values provided by the user. Therefore, a malicious user could trigger overflows in the multiplication. In certain scenarios, this can then result in heap OOB read/write.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-Bounds in TFLite, due to missing validation in the conversion from sparse tensors to dense tensors.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1, 2.8.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Integer Overflow in the Range implementation, which can cause OOM and undefined behaviour.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1, 2.8.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Use After Free in DecodePng kernel. This can happen because the values of decode.width and decode.height are in an unspecified state after png::CommonFreeDecode(&decode) gets called.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) via invalid PNG images that are put through the decoding process. After calling png::CommonInitDecode(..., &decode), the decode value contains allocated buffers which can only be freed by calling png::CommonFreeDecode(&decode). However, the function implementation invokes the OP_REQUIRES macro which immediately terminates the execution of the function, not allowing the memory to be freed.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Buffer Overflow by letting the runtime assume that the GraphDef format does not allow recursive functions. However, a GraphDef containing a crafted fragment with a recursive function can be consumed when loading a SavedModel, which would lead to a stack overflow during execution.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) by allocating a large vector, based on a value from a tensor controlled by the user during shape inference.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). This can be caused by altering a SavedModel such that assertions in function.cc would be falsified and crash the Python interpreter.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) by altering a SavedModel, using the Grappler optimizer, such that SafeToRemoveIdentity would trigger CHECK failures.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) via an altering of a SavedModel such that TensorByteSize would trigger CHECK failures.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference via the Grappler component. This can occur twice for the same malicious alteration of a SavedModel file (fixing the first one would trigger the same dereference in the second place).

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) via the altering of a SavedModel such that any binary op would trigger CHECK failures.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to a CHECK-fail in the Tensor constructor as reference types are not allowed. A malicious user can cause this by altering a SavedModel such that Grappler optimizer would attempt to build a tensor using a reference dtype.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Integer Overflow or Wraparound via the implementation of OpLevelCostEstimator::CalculateTensorSize if an attacker can create an operation that would involve a tensor with a large enough number of elements.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference when BuildXlaCompilationCache is built and if default settings are used.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1, 2.8.0 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) by altering a SavedModel such that IsSimplifiableReshape would trigger CHECK failures.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Integer overflow in OpLevelCostEstimator::CalculateOutputSize, where an attacker can create an operation which would involve tensors with large enough number of elements.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to integer overflow in StringNGrams.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) in ThreadPoolHandle.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Buffer Overflow in shape inference for Dequantize.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-Bounds which does not fully validate the value of axis in the implementation of Dequantize.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-Bounds in FractionalAvgPoolGrad which does not consider cases where the input tensors are invalid.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Integer Overflow in AddManySparseToTensorsMap which results in a CHECK-fail when building new TensorShape objects.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Integer Overflow in sparse component-wise ops implementation. This can be used to trigger large allocations or CHECK-fails when building new TensorShape object.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Improper Input Validation in SparseTensorSliceDataset which can dereference a nullptr value.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) via the implementation of AddManySparseToTensorsMap that is vulnerable to an integer overflow which results in a CHECK-fail when building new TensorShape objects (so, an assert failure based denial of service). There are some missing validation on the shapes of the input tensors as well as directly constructing a large TensorShape with user-provided dimensions.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Type Confusion in shape inference for ConcatV2.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Integer Overflow or Wraparound via the implementation of UnravelIndex() function, which results in a division by zero.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Division by zero when executing convolution operators.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-bounds Read which does not fully validate the value of batch_dim in the implementation of shape inference for ReverseSequence.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) via the implementation of the FractionalMaxPool() function, which can be made to crash a TensorFlow process via a division by 0.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) via the implementation of the MapStage function, in which a CHECK-fail if the key tensor is not a scalar.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) via the implementation of SparseCountSparseOutput() function which can be made to crash a TensorFlow process by an integer overflow whose result is then used in a memory allocation.

###PoC

import tensorflow as tf
import numpy as np
    
tf.raw_ops.SparseCountSparseOutput(
  indices=[[1,1]],
  values=[2],
  dense_shape=[2 ** 31, 2 ** 32],
  weights=[1],
  binary_output=True,
  minlength=-1,
  maxlength=-1,
  name=None)

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) via CHECK-fails (i.e., assertion failures) when building invalid/overflowing tensor shapes, similar to CVE-2021-41197.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference via the implementation of the QuantizedMaxPool() function, which has an undefined behavior where user-controlled inputs can trigger a reference binding to a null pointer.

###PoC

import tensorflow as tf

tf.raw_ops.QuantizedMaxPool(
    input = tf.constant([[[[4]]]], dtype=tf.quint8),
    min_input = [],
    max_input = [1],
    ksize = [1, 1, 1, 1],
    strides = [1, 1, 1, 1],
    padding = "SAME", name=None
)

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) via the implementation of *Bincount() functions. A malicious actor can take advantage of several conditions that inputs must satisfy but are not being checked - by passing in arguments that would trigger a CHECK-fail.

###PoC

import tensorflow as tf

tf.raw_ops.DenseBincount(
  input=[[0], [1], [2]],
  size=[1],
  weights=[3,2,1],
  binary_output=False)

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Heap-based Buffer Overflow via the implementation of SparseCountSparseOutput.

###PoC

import tensorflow as tf
import numpy as np

tf.raw_ops.SparseCountSparseOutput(
  indices=[[-1,-1]],
  values=[2],
  dense_shape=[1, 1],
  weights=[1],
  binary_output=True,
  minlength=-1,
  maxlength=-1,
  name=None)

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Division by zero via a specially crafted TFLite model that would trigger the division in the implementation of depthwise convolutions. The parameters of the convolution can be user-controlled and are also used within a division operation to determine the size of the padding that needs to be added before applying the convolution. There is no check before this division that the divisor is strictly positive.

Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Access of Uninitialized Pointer due to binding a reference to nullptr.

Upgrade tensorflow to version 2.7.0, 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Divide By Zero which is triggered by implementations for convolution operators if passed empty filter tensor arguments.

Upgrade tensorflow to version 2.7.0, 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). If tf.image.resize is called with a large input argument then the TensorFlow process will crash due to a CHECK-failure caused by an overflow.

Upgrade tensorflow to version 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). When tf.tile is called with a large input argument then the TensorFlow process will crash due to a CHECK-failure caused by an overflow.

Upgrade tensorflow to version 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). Implementation of tf.math.segment_* operations results in a CHECK-fail related abort if a segment id in segment_ids is large.

Upgrade tensorflow to version 2.7.0, 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). TensorFlow allows tensor to have a large number of dimensions and each dimension can be as large as desired, this behavior may lead to CHECK-failure.

Upgrade tensorflow to version 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) if tf.summary.create_file_writer is called with non-scalar arguments due to a CHECK-fail.

Upgrade tensorflow to version 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) which can be triggered in affected versions of the Keras pooling layers, if the size of the pool is 0 or if a dimension is negative. This is due to the TensorFlow's implementation of pooling operations where the values in the sliding window are not checked to be strictly positive.

Upgrade tensorflow to version 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-Bounds. The shape inference functions for the QuantizeAndDequantizeV* operations can trigger a read outside of bounds of heap allocated array.

Upgrade tensorflow to version 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Buffer Overflow in the implementation of FusedBatchNorm kernels.

Upgrade tensorflow to version 2.7.0, 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Buffer Overflow. The implementation of SparseFillEmptyRows can be made to trigger a heap OOB access, whenever the size of indices does not match the size of values.

Upgrade tensorflow to version 2.7.0, 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Heap-based Buffer Overflow in the shape inference function for Transpose. This occurs whenever perm contains negative elements, because the shape inference function does not validate that the indices in perm are all valid.

Upgrade tensorflow to version 2.7.0, 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-bounds Read in the shape inference code for QuantizeV2. This occurs whenever axis is a negative value less than -1. In this case, we are accessing data before the start of a heap buffer. The code allows axis to be an optional argument (s would contain an error::NOT_FOUND error code). Otherwise, it assumes that axis is a valid index into the dimensions of the input tensor. If axis is less than -1 then this results in a heap OOB read.

Upgrade tensorflow to version 2.7.0, 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-Bounds in SparseBinCount. This is because of missing validation between the elements of the values argument and the shape of the sparse output.

Upgrade tensorflow to version 2.7.0, 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-bounds Read. The shape inference functions for SparseCountSparseOutput can trigger a read outside of bounds of heap allocated array.

Upgrade tensorflow to version 2.7.0, 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Use of Uninitialized Variable. During execution, EinsumHelper::ParseEquation() is supposed to set the flags in input_has_ellipsis vector and *output_has_ellipsis boolean to indicate whether there is ellipsis in the corresponding inputs and output. The code only changes these flags to true and never assigns false. This results in unitialized variable access if callers assume that EinsumHelper::ParseEquation() always sets these flags.

Upgrade tensorflow to version 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). During TensorFlow's Grappler optimizer phase, constant folding might attempt to deep copy a resource tensor, which will cause a segfault.

Upgrade tensorflow to version 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Access of Uninitialized Pointer via nullptr reference binding in sparse matrix multiplication.

Upgrade tensorflow to version 2.7.0, 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Deserialization of Untrusted Data due to the shape inference code for DeserializeSparse, which can trigger a null pointer dereference.

Upgrade tensorflow to version 2.7.0, 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). The process of building the control flow graph for a TensorFlow model is vulnerable to a null pointer exception when nodes that should be paired are not.

Upgrade tensorflow to version 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-Bounds. The shape inference code for tf.ragged.cross can trigger a read outside of bounds of heap allocated array.

Upgrade tensorflow to version 2.7.0, 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Heap-based Buffer Overflow which could trick the shape inference code for the Cudnn* operations into accessing invalid memory. This occurs because the ranks of the input, input_h and input_c parameters are not validated, but code assumes they have certain values.

Upgrade tensorflow to version 2.7.0, 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) via tf.function API which can be made to deadlock when two tf.function decorated Python functions are mutually recursive. This occurs due to using a non-reentrant Lock Python object. Loading any model which contains mutually recursive functions is vulnerable.

Upgrade tensorflow to version 2.7.0, 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to missing validation for invalid file formats via checkpoints loading infrastructure.

Upgrade tensorflow to version 2.7.0, 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Improper Input Validation. The implementation of ParallelConcat misses some input validation and can produce a division by 0.

Upgrade tensorflow to version 2.7.0, 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-bounds Read via the ImmutableConst operation which can be tricked into reading arbitrary memory contents. This is because the tstring TensorFlow string class has a special case for memory mapped strings but the operation itself does not offer any support for this datatype.

Upgrade tensorflow to version 2.7.0, 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) via SplitV which can trigger a segfault if an attacker supplies negative arguments. This occurs whenever size_splits contains more than one value and at least one value is negative.

Upgrade tensorflow to version 2.7.0, 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to missing validation, the attacker can trigger DoS via dereferencing nullptrs or via CHECK-failures

Upgrade tensorflow to version 2.7.0, 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Buffer Overflow. When calculating the size of the output within the tf.range kernel, there is a conditional statement of type int64 = condition ? int64 : double. Due to C++ implicit conversion rules, both branches of the condition will be cast to double and the result would be truncated before the assignment.

Upgrade tensorflow to version 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Use of Uninitialized Variable in TensorFlow's Grappler optimizer. If the train_nodes vector (obtained from the saved model that gets optimized) does not contain a Dequeue node, then dequeue_node is left unitialized.

Upgrade tensorflow to version 2.7.0, 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Remote Code Execution (RCE) via TensorFlow's saved_model_cli tool as it calls eval on user supplied strings.

Upgrade tensorflow to version 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Remote Code Execution (RCE) via TensorFlow's saved_model_cli tool as it calls eval on user supplied strings.

Upgrade tensorflow to version 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to missing validation for the shapes of the tensor arguments involved in the call.

Upgrade tensorflow to version 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to missing validation for the shapes of the tensor arguments involved in the call.

Upgrade tensorflow to version 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to missing validation for the shapes of the tensor arguments involved in the call.

Upgrade tensorflow to version 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Divide By Zero via AllToAll. This occurs whenever the split_count argument is 0.

Upgrade tensorflow to version 2.7.0, 2.6.1, 2.5.2, 2.4.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). Under certain conditions, Go code can trigger a segfault in string deallocation. For string tensors, C.TF_TString_Dealloc is called during garbage collection within a finalizer function. However, tensor structure isn't checked until encoding to avoid a performance penalty. The current method for dealloc assumes that encoding succeeded, but segfaults when a string tensor is garbage collected whose encoding failed (e.g., due to mismatched dimensions).

Upgrade tensorflow to version 2.5.1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) as, when running shape functions, some functions (such as MutableHashTableShape) produce extra output information in the form of a ShapeAndType struct. The shapes embedded in this struct are owned by an inference context that is cleaned up almost immediately; if the upstream code attempts to access this shape information, it can trigger a segfault.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Arbitrary Code Execution. TensorFlow and Keras can be tricked to perform arbitrary code execution when deserializing a Keras model from YAML format. The implementation uses yaml.unsafe_load which can perform arbitrary code execution on the input.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). An attacker can trigger a denial of service via a CHECK-fail in tf.raw_ops.MapStage. The implementation does not check that the key input is a valid non-empty tensor.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Improper Input Validation as, due to incomplete validation in tf.raw_ops.QuantizeV2, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays. The implementation has some validation but does not check that min_range and max_range both have the same non-zero number of elements. If axis is provided (i.e., not -1), then validation should check that it is a value in range for the rank of input tensor and then the lengths of min_range and max_range inputs match the axis dimension of the input tensor.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Improper Input Validation as, due to incomplete validation in tf.raw_ops.QuantizeV2, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays. The implementation has some validation but does not check that min_range and max_range both have the same non-zero number of elements. If axis is provided (i.e., not -1), then validation should check that it is a value in range for the rank of input tensor and then the lengths of min_range and max_range inputs match the axis dimension of the input tensor.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). An attacker can trigger a denial of service via a segmentation fault in tf.raw_ops.MaxPoolGrad caused by missing validation. The implementation misses some validation for the orig_input and orig_output tensors.

This is related to CVE-2021-29579, where the fixes were incomplete.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). An attacker can cause denial of service in applications serving models using tf.raw_ops.NonMaxSuppressionV5 by triggering a division by 0. The implementation uses a user controlled argument to resize a std::vector. However, as std::vector::resize takes the size argument as a size_t and output_size is an int, there is an implicit conversion to unsigned. If the attacker supplies a negative value, this conversion results in a crash. A similar issue occurs in CombinedNonMaxSuppression.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Improper Input Validation. The implementations of pooling in TFLite are vulnerable to division by 0 errors as there are no checks for divisors not being 0.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Improper Input Validation as, due to incomplete validation in MKL implementation of requantization, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays. The implementation does not validate the dimensions of the input tensor. A similar issue occurs in MklRequantizePerChannelOp. The implementation does not perform full validation for all the input arguments.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-Bounds. An attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to tf.raw_ops.SdcaOptimizerV2. The implementation does not check that the length of example_labels is the same as the number of examples.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). The shape inference code for tf.raw_ops.Dequantize has a vulnerability that could trigger a denial of service via a segfault if an attacker provides invalid arguments. The shape inference implementation uses axis to select between two different values for minmax_rank which is then used to retrieve tensor dimensions. However, code assumes that axis can be either -1 or a value greater than -1, with no validation for the other values.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). An attacker can cause denial of service in applications serving models using tf.raw_ops.UnravelIndex by triggering a division by 0. The implementation does not check that the tensor subsumed by dims is not empty. Hence, if one element of dims is 0, the implementation does a division by 0.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference. An attacker can cause undefined behavior via binding a reference to null pointer in tf.raw_ops.UnicodeEncode. The implementation reads the first dimension of the input_splits tensor before validating that this tensor is not empty.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Improper Input Validation. An attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to tf.raw_ops.UpperBound. The implementation does not validate the rank of sorted_input argument. A similar issue occurs in tf.raw_ops.LowerBound.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). An attacker can craft a TFLite model that would trigger a division by zero error in LSH implementation.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-bounds Read. It is possible to nest a tf.map_fn within another tf.map_fn call. However, if the input tensor is a RaggedTensor and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap. The t and z outputs should be identical, however this is not the case. The last row of t contains data from the heap which can be used to leak other memory information. The bug lies in the conversion from a Variant tensor to a RaggedTensor. The implementation does not check that all inner shapes match and this results in the additional dimensions. The same implementation can result in data loss, if input tensor is tweaked.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference. An attacker can cause undefined behavior via binding a reference to null pointer in tf.raw_ops.Map* and tf.raw_ops.OrderedMap* operations. The implementation has a check in place to ensure that indices is in ascending order, but does not check that indices is not empty.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference. An attacker can cause undefined behavior via binding a reference to null pointer in tf.raw_ops.Map* and tf.raw_ops.OrderedMap* operations. The implementation has a check in place to ensure that indices is in ascending order, but does not check that indices is not empty.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference. An attacker can cause undefined behavior via binding a reference to null pointer in tf.raw_ops.RaggedTensorToVariant. The implementation has an incomplete validation of the splits values, missing the case when the argument would be empty.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). Most implementations of convolution operators in TensorFlow are affected by a division by 0 vulnerability where an attacker can trigger a denial of service via a crash. The shape inference implementation is missing several validations before doing divisions and modulo operations.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Improper Input Validation. An attacker can cause undefined behavior via binding a reference to null pointer in tf.raw_ops.SparseFillEmptyRows. The shape inference implementation does not validate that the input arguments are not empty tensors.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Improper Input Validation. The code for tf.raw_ops.SaveV2 does not properly validate the inputs and an attacker can trigger a null pointer dereference. The implementation uses ValidateInputs to check that the input arguments are valid. This validation would have caught the illegal state represented by the reproducer above. However, the validation uses OP_REQUIRES which translates to setting the Status object of the current OpKernelContext to an error status, followed by an empty return statement which just terminates the execution of the function it is present in. However, this does not mean that the kernel execution is finalized: instead, execution continues from the next line in Compute that follows the call to ValidateInputs. This is equivalent to lacking the validation.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Integer Overflow. The implementation of tf.raw_ops.QuantizeAndDequantizeV4Grad is vulnerable to an integer overflow issue caused by converting a signed integer value to an unsigned one and then allocating memory based on this value. The implementation uses the axis value as the size argument to absl::InlinedVector constructor. But, the constructor uses an unsigned type for the argument, so the implicit conversion transforms the negative value to a large integer.

Upgrade tensorflow to version 2.5.1, 2.4.3 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Improper Input Validation. The implementation for tf.raw_ops.FractionalAvgPoolGrad can be tricked into accessing data outside of bounds of heap allocated buffers. The implementation does not validate that the input tensor is non-empty. Thus, code constructs an empty EigenDoubleMatrixMap and then accesses this buffer with indices that are outside of the empty area.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Integer Overflow. The implementation of tf.raw_ops.StringNGrams is vulnerable to an integer overflow issue caused by converting a signed integer value to an unsigned one and then allocating memory based on this value. The implementation calls reserve on a tstring with a value that sometimes can be negative if user supplies negative ngram_widths. The reserve method calls TF_TString_Reserve which has an unsigned long argument for the size of the buffer. Hence, the implicit conversion transforms the negative value to a large integer.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). An attacker can cause a denial of service in boosted_trees_create_quantile_stream_resource by using negative arguments. The implementation does not validate that num_streams only contains non-negative numbers. In turn, this results in using this value to allocate memory. However, reserve receives an unsigned integer so there is an implicit conversion from a negative value to a large positive unsigned. This results in a crash from the standard library.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Use After Free. The implementation for tf.raw_ops.BoostedTreesCreateEnsemble can result in a use after free error if an attacker supplies specially crafted arguments. The implementation uses a reference counted resource and decrements the refcount if the initialization fails, as it should. However, when the code was written, the resource was represented as a naked pointer but later refactoring has changed it to be a smart pointer. Thus, when the pointer leaves the scope, a subsequent free-ing of the resource occurs, but this fails to take into account that the refcount has already reached 0, thus the resource has been already freed. During this double-free process, members of the resource object are accessed for cleanup but they are invalid as the entire resource has been freed.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Heap-based Buffer Overflow. The implementation for tf.raw_ops.ExperimentalDatasetToTFRecord and tf.raw_ops.DatasetToTFRecord can trigger heap buffer overflow and segmentation fault. The implementation assumes that all records in the dataset are of string type. However, there is no check for that, and the example given above uses numeric types.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Improper Input Validation. An attacker can generate undefined behavior via a reference binding to nullptr in BoostedTreesCalculateBestGainsPerFeature and similar attack can occur in BoostedTreesCalculateBestFeatureSplitV2. The implementation does not validate the input values.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference. An attacker can cause undefined behavior via binding a reference to null pointer in tf.raw_ops.RaggedTensorToSparse. The implementation has an incomplete validation of the splits values: it does not check that they are in increasing order.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference. An attacker can cause undefined behavior via binding a reference to null pointer in all operations of type tf.raw_ops.MatrixSetDiagV*. The implementation has incomplete validation that the value of k is a valid tensor. We have check that this value is either a scalar or a vector, but there is no check for the number of elements. If this is an empty tensor, then code that accesses the first element of the tensor is wrong.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference. An attacker can cause undefined behavior via binding a reference to null pointer in all operations of type tf.raw_ops.MatrixDiagV*. The implementation has incomplete validation that the value of k is a valid tensor. We have check that this value is either a scalar or a vector, but there is no check for the number of elements. If this is an empty tensor, then code that accesses the first element of the tensor is wrong.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-bounds Read as, if the arguments to tf.raw_ops.RaggedGather don't determine a valid ragged tensor, the code can trigger a read from outside of bounds of heap allocated buffers. The implementation directly reads the first dimension of a tensor shape before checking that said tensor has rank of at least 1 (i.e., it is not a scalar). Furthermore, the implementation does not check that the list given by params_nested_splits is not an empty list of tensors.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-bounds Read. An attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to BoostedTreesSparseCalculateBestFeatureSplit. The implementation needs to validate that each value in stats_summary_indices is in range.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Improper Input Validation as providing a negative element to num_elements list argument of tf.raw_ops.TensorListReserve causes the runtime to abort the process due to reallocating a std::vector to have a negative number of elements. The implementation calls std::vector.resize() with the new size controlled by input given by the user, without checking that this input is valid.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference. An attacker can cause undefined behavior via binding a reference to null pointer in all binary cwise operations that don't require broadcasting (e.g., gradients of binary cwise operations). The implementation assumes that the two inputs have exactly the same number of elements but does not check that. Hence, when the eigen functor executes it triggers heap OOB reads and undefined behavior due to binding to nullptr.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-Bounds. An attacker can trigger a crash via a CHECK-fail in debug builds of TensorFlow using tf.raw_ops.ResourceGather or a read from outside the bounds of heap allocated data in the same API in a release build. The implementation does not check that the batch_dims value that the user supplies is less than the rank of the input tensor. Since the implementation uses several for loops over the dimensions of tensor, this results in reading data from outside the bounds of heap allocated buffer backing the tensor.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-Bounds as the implementation of sparse reduction operations in TensorFlow can trigger accesses outside of bounds of heap allocated data. The implementation fails to validate that each reduction group does not overflow and that each corresponding index does not point to outside the bounds of the input tensor.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Heap-based Buffer Overflow. An attacker can trigger a read from outside of bounds of heap allocated data by sending invalid arguments to tf.raw_ops.ResourceScatterUpdate. The implementation has an incomplete validation of the relationship between the shapes of indices and updates: instead of checking that the shape of indices is a prefix of the shape of updates (so that broadcasting can happen), code only checks that the number of elements in these two tensors are in a divisibility relationship.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Heap-based Buffer Overflow. An attacker can trigger a read from outside of bounds of heap allocated data by sending invalid arguments to tf.raw_ops.ResourceScatterUpdate. The implementation has an incomplete validation of the relationship between the shapes of indices and updates: instead of checking that the shape of indices is a prefix of the shape of updates (so that broadcasting can happen), code only checks that the number of elements in these two tensors are in a divisibility relationship.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference. The code for tf.raw_ops.UncompressElement can be made to trigger a null pointer dereference. The implementation obtains a pointer to a CompressedElement from a Variant tensor and then proceeds to dereference it for decompressing. There is no check that the Variant tensor contained a CompressedElement, so the pointer is actually nullptr.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference. It is possible to trigger a null pointer dereference in TensorFlow by passing an invalid input to tf.raw_ops.CompressElement. The implementation was accessing the size of a buffer obtained from the return of a separate function call before validating that said buffer is valid.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference. When a user does not supply arguments that determine a valid sparse tensor, tf.raw_ops.SparseTensorSliceDataset implementation can be made to dereference a null pointer. The implementation has some argument validation but fails to consider the case when either indices or values are provided for an empty sparse tensor when the other is not. If indices is empty, then code that performs validation (i.e., checking that the indices are monotonically increasing) results in a null pointer dereference. If indices as provided by the user is empty, then indices in the C++ code above is backed by an empty std::vector, hence calling indices->dim_size(0) results in null pointer dereferencing (same as calling std::vector::at() on an empty vector).

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference. If a user does not provide a valid padding value to tf.raw_ops.MatrixDiagPartOp, then the code triggers a null pointer dereference (if input is empty) or produces invalid behavior, ignoring all values after the first. The implementation reads the first value from a tensor buffer without first checking that the tensor has values to read from.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-Bounds. When restoring tensors via raw APIs, if the tensor name is not provided, TensorFlow can be tricked into dereferencing a null pointer. Alternatively, attackers can read memory outside the bounds of heap allocated data by providing some tensor names but not enough for a successful restoration. The implementation retrieves the tensor list corresponding to the tensor_name user controlled input and immediately retrieves the tensor at the restoration index (controlled via preferred_shard argument). This occurs without validating that the provided list has enough values. If the list is empty this results in dereferencing a null pointer (undefined behavior). If, however, the list has some elements, if the restoration index is outside the bounds this results in heap OOB read.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to NULL Pointer Dereference. Sending invalid argument for row_partition_types of tf.raw_ops.RaggedTensorToTensor API results in a null pointer dereference and undefined behavior. The implementation accesses the first element of a user supplied list of values without validating that the provided list is not empty.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) as the implementation of tf.raw_ops.ResourceScatterDiv is vulnerable to a division by 0 error. The implementation uses a common class for all binary operations but fails to treat the division by 0 case separately.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS) as the implementation of tf.raw_ops.ResourceScatterDiv is vulnerable to a division by 0 error. The implementation uses a common class for all binary operations but fails to treat the division by 0 case separately.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). An attacker can cause a floating point exception by calling in-place operations with crafted arguments that would result in a division by 0. The implementation has a logic error: it should skip processing if x and v are empty but the code uses || instead of &&.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). An attacker can trigger a crash via a floating point exception in tf.raw_ops.ResourceGather. The implementation computes the value of a value, batch_size, and then divides by it without checking that this value is not 0.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). The implementation of tf.raw_ops.SparseReshape can be made to trigger an integral division by 0 exception. The implementation calls the reshaping functor whenever there is at least an index in the input but does not check that shape of the input or the target shape have both a non-zero number of elements. The reshape functor blindly divides by the dimensions of the target shape. Hence, if this is not checked, code will result in a division by 0.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). The implementation of tf.raw_ops.SparseDenseCwiseDiv is vulnerable to a division by 0 error. The implementation uses a common class for all binary operations but fails to treat the division by 0 case separately.

Upgrade tensorflow to version 2.5.1, 2.4.3, 2.3.4 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Arbitrary File Write via Archive Extraction (Zip Slip) via a crafted archive when tf.keras.utils.get_file is used with extract=True.

NOTE: This CVE is disputed as the vendor's position is that tf.keras.utils.get_file is not intended for untrusted archives. However, we feel this advisory is relevant as at the time of publication, there is no known security notice or documentation warning users of this behavior.

UPDATE: With the addition of a clear warning to the API documentation on Feb 23, 2023, this issue is considered fixed.

Upgrade tensorflow to version 2.12.0rc1 or higher.

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). Incomplete validation in SparseReshape results in a denial of service based on a CHECK-failure.

Upgrade tensorflow to version 2.5.0 or higher.