Out-of-bounds Write Affecting tensorflow/tensorflow package, versions [2.2.0,2.2.1)[2.3.0,2.3.1)


Severity

Recommended
0.0
high
0
10

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

Threat Intelligence

Exploit Maturity
Proof of Concept
EPSS
0.31% (71st percentile)

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  • Snyk IDSNYK-UNMANAGED-TENSORFLOWTENSORFLOW-2333377
  • published12 Jan 2022
  • disclosed25 Sept 2020
  • creditUnknown

Introduced: 25 Sep 2020

CVE-2020-15212  (opens in a new tab)
CWE-787  (opens in a new tab)

How to fix?

Upgrade tensorflow/tensorflow to version 2.2.1, 2.3.1 or higher.

Overview

Affected versions of this package are vulnerable to Out-of-bounds Write. In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to segment_ids_data can alter output_index and then write to outside of output_data buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom Verifier to the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.

CVSS Scores

version 3.1