tensorflow-cpu@2.9.0rc2 vulnerabilities

tensorflow-cpu 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-cpu to version 2.11.1 or higher.

tensorflow-cpu 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-cpu to version 2.11.1, 2.12.0 or higher.

tensorflow-cpu 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-cpu to version 2.11.1, 2.12.0 or higher.

tensorflow-cpu 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-cpu to version 2.11.1, 2.12.0 or higher.

tensorflow-cpu 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-cpu to version 2.11.1, 2.12.0 or higher.

tensorflow-cpu 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-cpu to version 2.11.1, 2.12.0 or higher.

tensorflow-cpu 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-cpu to version 2.11.1, 2.12.0 or higher.

tensorflow-cpu 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-cpu to version 2.11.1, 2.12.0 or higher.

tensorflow-cpu 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-cpu to version 2.11.1, 2.12.0 or higher.

tensorflow-cpu is a machine learning framework.

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

Upgrade tensorflow-cpu to version 2.11.1, 2.12.0 or higher.

tensorflow-cpu 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-cpu to version 2.11.1, 2.12.0 or higher.

tensorflow-cpu 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-cpu to version 2.11.1, 2.12.0 or higher.

tensorflow-cpu 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-cpu to version 2.11.1, 2.12.0 or higher.

tensorflow-cpu 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-cpu to version 2.11.1, 2.12.0 or higher.

tensorflow-cpu 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-cpu to version 2.11.1, 2.12.0 or higher.

tensorflow-cpu 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-cpu to version 2.11.1, 2.12.0 or higher.

tensorflow-cpu 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-cpu to version 2.11.1, 2.12.0 or higher.

tensorflow-cpu is a machine learning framework.

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

Upgrade tensorflow-cpu to version 2.11.1, 2.12.0 or higher.

tensorflow-cpu 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-cpu to version 2.11.1, 2.12.0 or higher.

tensorflow-cpu 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-cpu to version 2.11.1, 2.12.0 or higher.

tensorflow-cpu is a machine learning framework.

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

Upgrade tensorflow-cpu to version 2.11.1, 2.12.0 or higher.

tensorflow-cpu 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-cpu to version 2.10.1, 2.11.0 or higher.

tensorflow-cpu 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-cpu to version 2.9.3, 2.10.1, 2.11.0 or higher.

tensorflow-cpu 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-cpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-cpu 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-cpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-cpu is a machine learning framework.

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

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

tensorflow-cpu 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-cpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-cpu 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-cpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-cpu 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-cpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-cpu 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-cpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-cpu 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-cpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-cpu 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-cpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-cpu is a machine learning framework.

Affected versions of this package are vulnerable to Heap-based Buffer Overflow. The TensorKey hash function used total estimated AllocatedBytes(), which (a) is an estimate per tensor, and (b) is a very poor hash function for constants (e.g. int32_t). It also access individual tensor bytes through tensor.data() of size AllocatedBytes(). This behavior results in ASAN failures because the AllocatedBytes() is an estimate of total bytes allocated by a tensor, including any pointed-to constructs (e.g. strings), and does not refer to contiguous bytes in the .data() buffer.

Upgrade tensorflow-cpu to version 2.8.1, 2.9.0 or higher.

tensorflow-cpu 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-cpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-cpu 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-cpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-cpu 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-cpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-cpu 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-cpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-cpu 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-cpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-cpu 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-cpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-cpu 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-cpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-cpu 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-cpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-cpu 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-cpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-cpu 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-cpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-cpu 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-cpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-cpu 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-cpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-cpu 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-cpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-cpu 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-cpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.

tensorflow-cpu 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-cpu to version 2.6.4, 2.7.2, 2.8.1, 2.9.0 or higher.