Buffer Overflow Affecting tensorflow/tensorflow package, versions [2.3.0,2.3.3)[2.4.0,2.4.2)


Severity

Recommended
0.0
medium
0
10

CVSS assessment made by Snyk's Security Team. Learn more

Threat Intelligence

EPSS
0.04% (15th percentile)

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  • Snyk IDSNYK-UNMANAGED-TENSORFLOWTENSORFLOW-2333522
  • published12 Jan 2022
  • disclosed14 May 2021
  • creditUnknown

Introduced: 14 May 2021

CVE-2021-29521  (opens in a new tab)
CWE-131  (opens in a new tab)

How to fix?

Upgrade tensorflow/tensorflow to version 2.3.3, 2.4.2 or higher.

Overview

Affected versions of this package are vulnerable to Buffer Overflow. TensorFlow is an end-to-end open source platform for machine learning. Specifying a negative dense shape in tf.raw_ops.SparseCountSparseOutput results in a segmentation fault being thrown out from the standard library as std::vector invariants are broken. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L199-L213) assumes the first element of the dense shape is always positive and uses it to initialize a BatchedMap<T> (i.e., std::vector<absl::flat_hash_map<int64,T>>(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L27)) data structure. If the shape tensor has more than one element, num_batches is the first value in shape. Ensuring that the dense_shape argument is a valid tensor shape (that is, all elements are non-negative) solves this issue. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3.

CVSS Scores

version 3.1