Vulnerability Monitor

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CVE-2021-37651

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.FractionalAvgPoolGrad` can be tricked into accessing data outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/fractional_avg_pool_op.cc#L205) 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. We have patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

Published

2021-08-12T21:15:08.170

Last Modified

2024-11-21T06:15:36.860

Status

Modified

Source

[email protected]

Severity

CVSSv3.1: 7.1 (HIGH)

CVSSv2 Vector

AV:L/AC:L/Au:N/C:P/I:P/A:P

  • Access Vector: LOCAL
  • Access Complexity: LOW
  • Authentication: NONE
  • Confidentiality Impact: PARTIAL
  • Integrity Impact: PARTIAL
  • Availability Impact: PARTIAL
Exploitability Score

3.9

Impact Score

6.4

Weaknesses
  • Type: Secondary
    CWE-125
  • Type: Primary
    CWE-787
Affected Vendors & Products
Type Vendor Product Version/Range Vulnerable?
Application google tensorflow < 2.3.4 Yes
Application google tensorflow < 2.4.3 Yes
Application google tensorflow 2.5.0 Yes
Application google tensorflow 2.6.0 Yes
Application google tensorflow 2.6.0 Yes
Application google tensorflow 2.6.0 Yes
References