CVE-2022-21731
MEDIUMType confusion leading to segfault in Tensorflow
EPSS Exploitation Probability
EPSS (Exploit Prediction Scoring System) is a daily probability model maintained by FIRST.org. It estimates the likelihood a CVE will be exploited in production environments within the next 30 days, derived from real-world threat intelligence signals.
Blast Radius
tensorflow🐍tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-gpu🐍tensorflow-gpu+1 moreReal-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.
Description
Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for ConcatV2 can be used to trigger a denial of service attack via a segfault caused by a type confusion. The axis argument is translated into concat_dim in the ConcatShapeHelper helper function. Then, a value for min_rank is computed based on concat_dim. This is then used to validate that the values tensor has at least the required rank. However, WithRankAtLeast receives the lower bound as a 64-bits value and then compares it against the maximum 32-bits integer value that could be represented. Due to the fact that min_rank is a 32-bits value and the value of axis, the rank argument is a negative value, so the error check is bypassed. 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.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | tensorflow | all versions | 2.5.3 |
| 🐍PyPI | tensorflow | ≥ 2.6.0&&< 2.6.3 | 2.6.3 |
| 🐍PyPI | tensorflow | ≥ 2.7.0&&< 2.7.1 | 2.7.1 |
| 🐍PyPI | tensorflow-cpu | all versions | 2.5.3 |
| 🐍PyPI | tensorflow-cpu | ≥ 2.6.0&&< 2.6.3 | 2.6.3 |
| 🐍PyPI | tensorflow-cpu | ≥ 2.7.0&&< 2.7.1 | 2.7.1 |
Research use only. For defensive security, authorized penetration testing, and academic research only. Never execute exploit code against systems without explicit written authorization.
Detection & mitigation playbook
Open-source dependencyDetect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for tensorflow. O3's reachability analysis confirms whether the vulnerable code path is actually invoked in your application, so you act on real exposure instead of every transitive match.
Fix
Update tensorflow to 2.5.3 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2022-21731 is resolved across your whole dependency graph.
Workarounds
If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.
How O3 protects you
O3 pinpoints whether CVE-2022-21731 is reachable in your code and exactly where to fix it, then blocks exploitation in production at runtime until the patched version is deployed.
Tailored to CVE-2022-21731. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.
Frequently Asked Questions
Is CVE-2022-21731 in your dependencies?
O3 detects CVE-2022-21731 across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.