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CVE-2021-41228PYSEC-2021-835BIT-TENSORFLOW-2021-41228GHSA-3RCW-9P9X-582VPYSEC-2021-420PYSEC-2021-637

Code injection in `saved_model_cli`

Published
Last modified
published
Not listed in CISA KEV
Fix
Fixed in 2.6.1, 2.5.2, 2.4.4, 2.7.0
None on Exploit-DB, Metasploit or GitHub

Summary

TensorFlow is an open source platform for machine learning. In affected versions TensorFlow's `saved_model_cli` tool is vulnerable to a code injection as it calls `eval` on user supplied strings. This can be used by attackers to run arbitrary code on the plaform where the CLI tool runs. However, given that the tool is always run manually, the impact of this is not severe. We have patched this by adding a `safe` flag which defaults to `True` and an explicit warning for users. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

Timeline

  1. CVE published
  2. Record last updated

Affected versions

  • tensorflow tensorflow

    From 2.6.0 · fixed in 2.6.1; From 2.5.0 · fixed in 2.5.2; fixed in 2.4.4

  • tensorflow-gpuPyPI

    fixed in 2.4.4; From 2.5.0 · fixed in 2.5.2; From 2.6.0 · fixed in 2.6.1; From 2.7.0rc0 · fixed in 2.7.0

  • tensorflow-cpuPyPI

    fixed in 2.4.4; From 2.5.0 · fixed in 2.5.2; From 2.6.0 · fixed in 2.6.1; From 2.7.0rc0 · fixed in 2.7.0

  • tensorflowPyPI

    fixed in 2.4.4; From 2.5.0 · fixed in 2.5.2; From 2.6.0 · fixed in 2.6.1; From 2.7.0rc0 · fixed in 2.7.0

  • tensorflowPyPI

    From 2.5.0 · fixed in 2.5.2

  • tensorflowPyPI

    fixed in 2.4.4

  • tensorflowPyPI

    From 2.6.0 · fixed in 2.6.1

  • tensorflow-cpuPyPI

    From 2.5.0 · fixed in 2.5.2

  • tensorflow-cpuPyPI

    fixed in 2.4.4

  • tensorflow-cpuPyPI

    From 2.6.0 · fixed in 2.6.1

  • tensorflow-gpuPyPI

    From 2.5.0 · fixed in 2.5.2

  • tensorflow-gpuPyPI

    fixed in 2.4.4

  • tensorflow-gpuPyPI

    From 2.6.0 · fixed in 2.6.1

References 2

GitHub · tensorflow/tensorflow