GHSA-q8qj-fc9q-cphr is a medium-severity (CVSS 5.3) Improper Input Validation vulnerability in tensorflow. 1 public exploit reference exists, so weaponization risk is real. O3 Security confirms whether GHSA-q8qj-fc9q-cphr is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.
Undefined behavior in Tensorflow
Real-World Exposure
tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-gpu🐍tensorflow-gpuReal-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
Impact
If a user passes an invalid argument to dlpack.to_dlpack the expected validations will cause variables to bind to nullptr while setting a status variable to the error condition.
However, this status argument is not properly checked:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/c/eager/dlpack.cc#L265-L267
Hence, code following these methods will bind references to null pointers: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/c/eager/dlpack.cc#L279-L285
This is undefined behavior and reported as an error if compiling with -fsanitize=null.
Patches
We have patched the issue in 22e07fb204386768e5bcbea563641ea11f96ceb8 and will release a patch release for all affected versions.
We recommend users to upgrade to TensorFlow 2.2.1 or 2.3.1.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
Attribution
This vulnerability has been discovered during variant analysis of GHSA-rjjg-hgv6-h69v.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | tensorflow | ≥ 2.2.0&&< 2.2.1 | 2.2.1 |
| 🐍PyPI | tensorflow | ≥ 2.3.0&&< 2.3.1 | 2.3.1 |
| 🐍PyPI | tensorflow-cpu | ≥ 2.2.0&&< 2.2.1 | 2.2.1 |
| 🐍PyPI | tensorflow-cpu | ≥ 2.3.0&&< 2.3.1 | 2.3.1 |
| 🐍PyPI | tensorflow-gpu | ≥ 2.2.0&&< 2.2.1 | 2.2.1 |
| 🐍PyPI | tensorflow-gpu | ≥ 2.3.0&&< 2.3.1 | 2.3.1 |
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 2.2.1 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-q8qj-fc9q-cphr 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 GHSA-q8qj-fc9q-cphr 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 GHSA-q8qj-fc9q-cphr. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.
Frequently Asked Questions
Is GHSA-q8qj-fc9q-cphr in your dependencies?
O3 detects GHSA-q8qj-fc9q-cphr across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.