GHSA-9jjw-hf72-3mxw
CRITICALTensorFlow vulnerable to heap out of bounds read in filesystem glob matching
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-cpu🐍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
The general implementation for matching filesystem paths to globbing pattern is vulnerable to an access out of bounds of the array holding the directories:
if (!fs->Match(child_path, dirs[dir_index])) { ... }
Since dir_index is unconditionaly incremented outside of the lambda function where the vulnerable pattern occurs, this results in an access out of bounds issue under certain scenarios. For example, if /tmp/x is a directory that only contains a single file y, then the following scenario will cause a crash due to the out of bounds read:
>>> tf.io.gfile.glob('/tmp/x/')
Segmentation fault
There are multiple invariants and preconditions that are assumed by the parallel implementation of GetMatchingPaths but are not verified by the PRs introducing it (#40861 and #44310). Thus, we are completely rewriting the implementation to fully specify and validate these.
Patches
We have patched the issue in GitHub commit 8b5b9dc96666a3a5d27fad7179ff215e3b74b67c and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.
This issue only impacts master branch and the release candidates for TF version 2.4. The final release of the 2.4 release will be patched.
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 reported by members of the Aivul Team from Qihoo 360.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | tensorflow | ≥ 2.4.0rc0&&< 2.4.0 | 2.4.0 |
| 🐍PyPI | tensorflow-cpu | ≥ 2.4.0rc0&&< 2.4.0 | 2.4.0 |
| 🐍PyPI | tensorflow-gpu | ≥ 2.4.0rc0&&< 2.4.0 | 2.4.0 |
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 2.4.0 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-9jjw-hf72-3mxw 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-9jjw-hf72-3mxw 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-9jjw-hf72-3mxw. 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-9jjw-hf72-3mxw in your dependencies?
O3 detects GHSA-9jjw-hf72-3mxw across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.