GHSA-fpcp-9h7m-ffpx
MEDIUMNull pointer dereference 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
Impact
When building an XLA compilation cache, if default settings are used, TensorFlow triggers a null pointer dereference:
string allowed_gpus =
flr->config_proto()->gpu_options().visible_device_list();
In the default scenario, all devices are allowed, so flr->config_proto is nullptr.
Patches
We have patched the issue in GitHub commit e21af685e1828f7ca65038307df5cc06de4479e8. 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.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
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 GHSA-fpcp-9h7m-ffpx 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-fpcp-9h7m-ffpx 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-fpcp-9h7m-ffpx. 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-fpcp-9h7m-ffpx in your dependencies?
O3 detects GHSA-fpcp-9h7m-ffpx across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.