GHSA-43Q8-3FV7-PR5X

Improper Validation of Integrity Check Value in TensorFlow

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Attribution

This vulnerability has been reported externally via a GitHub issue.

The implementation of tf.sparse.split does not fully validate the input arguments. Hence, a malicious user can trigger a denial of service via a segfault or a heap OOB read:

import tensorflow as tf
data = tf.random.uniform([1, 32, 32], dtype=tf.float32)
axis = [1, 2]
x = tf.sparse.from_dense(data)
result = tf.sparse.split(x,3, axis=axis)

The code assumes axis is a scalar. This is another instance of TFSA-2021-190 (CVE-2021-41206).

GHSA-43Q8-3FV7-PR5X has a CVSS score of 7.0 (High). 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 (2.5.3, 2.6.3, 2.7.1); upgrading removes the vulnerable code path.

Kodem intelligence

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We have patched the issue in GitHub commit 61bf91e768173b001d56923600b40d9a95a04ad5 (merging #53695).

The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

1. What is GHSA-43Q8-3FV7-PR5X? GHSA-43Q8-3FV7-PR5X is a high-severity security vulnerability in tensorflow (pip), affecting versions < 2.5.3. It is fixed in 2.5.3, 2.6.3, 2.7.1.
2. How severe is GHSA-43Q8-3FV7-PR5X? GHSA-43Q8-3FV7-PR5X has a CVSS score of 7.0 (High). 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 GHSA-43Q8-3FV7-PR5X?
   • tensorflow (pip) (versions < 2.5.3)
   • tensorflow-cpu (pip) (versions < 2.5.3)
   • tensorflow-gpu (pip) (versions < 2.5.3)
4. Is there a fix for GHSA-43Q8-3FV7-PR5X? Yes. GHSA-43Q8-3FV7-PR5X is fixed in 2.5.3, 2.6.3, 2.7.1. Upgrade to this version or later.
5. Is GHSA-43Q8-3FV7-PR5X exploitable, and should I be worried? Whether GHSA-43Q8-3FV7-PR5X 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 GHSA-43Q8-3FV7-PR5X 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 GHSA-43Q8-3FV7-PR5X?
   • Upgrade tensorflow to 2.5.3 or later
   • Upgrade tensorflow to 2.6.3 or later
   • Upgrade tensorflow to 2.7.1 or later
   • Upgrade tensorflow-cpu to 2.5.3 or later
   • Upgrade tensorflow-cpu to 2.6.3 or later
   • Upgrade tensorflow-cpu to 2.7.1 or later
   • Upgrade tensorflow-gpu to 2.5.3 or later
   • Upgrade tensorflow-gpu to 2.6.3 or later
   • Upgrade tensorflow-gpu to 2.7.1 or later

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