CVE-2022-29207

Undefined behavior when users supply invalid resource handles

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Attribution

This vulnerability has been reported by Hong Jin from Singapore Management University.

Multiple TensorFlow operations misbehave in eager mode when the resource handle provided to them is invalid:

import tensorflow as tf

tf.raw_ops.QueueIsClosedV2(handle=[])

import tensorflow as tf

tf.summary.flush(writer=())

In graph mode, it would have been impossible to perform these API calls, but migration to TF 2.x eager mode opened up this vulnerability. If the resource handle is empty, then a reference is bound to a null pointer inside TensorFlow codebase (various codepaths). This is undefined behavior.

The application does not adequately validate input before processing it, allowing unexpected values to reach sensitive code paths. Typical impact: varies by context: data corruption, logic bypass, or denial of service.

CVE-2022-29207 has a CVSS score of 5.5 (Medium). The vector is requires local access, low privileges required, and no user interaction. A CVSS score reflects the worst-case severity of the vulnerability, not your specific exposure. Whether this affects your application depends on whether the vulnerable code is present and reachable in your environment. A fixed version is available (2.6.4, 2.7.2, 2.8.1); upgrading removes the vulnerable code path.

Kodem intelligence

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We have patched the issue in GitHub commit a5b89cd68c02329d793356bda85d079e9e69b4e7 and GitHub commit dbdd98c37bc25249e8f288bd30d01e118a7b4498.

The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, as these are also affected and still in supported range.

1. What is CVE-2022-29207? CVE-2022-29207 is a medium-severity improper input validation vulnerability in tensorflow (pip), affecting versions < 2.6.4. It is fixed in 2.6.4, 2.7.2, 2.8.1. The application does not adequately validate input before processing it, allowing unexpected values to reach sensitive code paths.
2. How severe is CVE-2022-29207? CVE-2022-29207 has a CVSS score of 5.5 (Medium). This score reflects the worst-case severity of the vulnerability, not your specific exposure. Whether it represents real risk in your environment depends on whether the vulnerable code is present and reachable.
3. Which packages are affected by CVE-2022-29207?
   • tensorflow (pip) (versions < 2.6.4)
   • tensorflow-cpu (pip) (versions < 2.6.4)
   • tensorflow-gpu (pip) (versions < 2.6.4)
4. Is there a fix for CVE-2022-29207? Yes. CVE-2022-29207 is fixed in 2.6.4, 2.7.2, 2.8.1. Upgrade to this version or later.
5. Is CVE-2022-29207 exploitable, and should I be worried? Whether CVE-2022-29207 is exploitable in your environment depends on whether the vulnerable code is present and reachable. A CVSS score is a worst-case rating; it does not account for your specific deployment, configuration, or usage patterns. Kodem, an Intelligent Application Security platform, uses runtime intelligence to show which vulnerabilities actually execute in production, so you can focus on the ones that represent real risk. Get a demo
6. What actually determines whether CVE-2022-29207 is exploitable, and how bad it is? Exploitability and impact are not fixed properties of a CVE. They depend on runtime truth: whether the vulnerable code is present, reachable, and actually executes in your application. A high CVSS score on a dependency that never runs is not the same as real risk. Kodem, an Intelligent Application Security platform, uses runtime intelligence to reveal which vulnerabilities actually execute in production, so teams prioritize the ones that genuinely matter.
7. How do I fix CVE-2022-29207?
   • Upgrade tensorflow to 2.6.4 or later
   • Upgrade tensorflow to 2.7.2 or later
   • Upgrade tensorflow to 2.8.1 or later
   • Upgrade tensorflow-cpu to 2.6.4 or later
   • Upgrade tensorflow-cpu to 2.7.2 or later
   • Upgrade tensorflow-cpu to 2.8.1 or later
   • Upgrade tensorflow-gpu to 2.6.4 or later
   • Upgrade tensorflow-gpu to 2.7.2 or later
   • Upgrade tensorflow-gpu to 2.8.1 or later

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