GHSA-WCV5-VRVR-3RX2

Integer Overflow or Wraparound in TensorFlow

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The Grappler component of TensorFlow is vulnerable to a denial of service via CHECK-failure (assertion failure) in constant folding:

  for (const auto& output_prop : output_props) {
    const PartialTensorShape output_shape(output_prop.shape());
    // ...
  }

The output_prop tensor has a shape that is controlled by user input and this can result in triggering one of the CHECKs in the PartialTensorShape constructor. This is an instance of TFSA-2021-198 (CVE-2021-41197).

An arithmetic operation produces a value that exceeds the integer type's maximum, causing it to wrap to an unexpected small value. Typical impact: incorrect size calculations leading to heap overflows or logic errors.

GHSA-WCV5-VRVR-3RX2 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 (2.5.3, 2.6.3, 2.7.1); upgrading removes the vulnerable code path.

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We have patched the issue in GitHub commit be7b286d40bc68cb0b56f702186cc4837d508058.

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-WCV5-VRVR-3RX2? GHSA-WCV5-VRVR-3RX2 is a medium-severity integer overflow or wraparound vulnerability in tensorflow (pip), affecting versions < 2.5.3. It is fixed in 2.5.3, 2.6.3, 2.7.1. An arithmetic operation produces a value that exceeds the integer type's maximum, causing it to wrap to an unexpected small value.
2. How severe is GHSA-WCV5-VRVR-3RX2? GHSA-WCV5-VRVR-3RX2 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 GHSA-WCV5-VRVR-3RX2?
   • 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-WCV5-VRVR-3RX2? Yes. GHSA-WCV5-VRVR-3RX2 is fixed in 2.5.3, 2.6.3, 2.7.1. Upgrade to this version or later.
5. Is GHSA-WCV5-VRVR-3RX2 exploitable, and should I be worried? Whether GHSA-WCV5-VRVR-3RX2 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-WCV5-VRVR-3RX2 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-WCV5-VRVR-3RX2?
   • 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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