GHSA-qhxx-j73r-qpm2 is a medium-severity (CVSS 4.4) CWE-908 vulnerability in tensorflow. 1 public exploit reference exists, so weaponization risk is real. O3 Security confirms whether GHSA-qhxx-j73r-qpm2 is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.
Uninitialized memory access in TensorFlow
Real-World Exposure
tensorflow🐍tensorflow🐍tensorflow🐍tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu+7 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
Under certain cases, a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen:
struct QUInt8 {
QUInt8() {}
// ...
uint8_t value;
};
struct QInt16 {
QInt16() {}
// ...
int16_t value;
};
struct QUInt16 {
QUInt16() {}
// ...
uint16_t value;
};
struct QInt32 {
QInt32() {}
// ...
int32_t value;
};
Patches
We have patched the issue in GitHub commit ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2 and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.
Since this issue also impacts TF versions before 2.4, we will patch all releases between 1.15 and 2.3 inclusive.
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 | 1.15.5 |
| 🐍PyPI | tensorflow | ≥ 2.0.0&&< 2.0.4 | 2.0.4 |
| 🐍PyPI | tensorflow | ≥ 2.1.0&&< 2.1.3 | 2.1.3 |
| 🐍PyPI | tensorflow | ≥ 2.2.0&&< 2.2.2 | 2.2.2 |
| 🐍PyPI | tensorflow | ≥ 2.3.0&&< 2.3.2 | 2.3.2 |
| 🐍PyPI | tensorflow-cpu | all versions | 1.15.5 |
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 1.15.5 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-qhxx-j73r-qpm2 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-qhxx-j73r-qpm2 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-qhxx-j73r-qpm2. 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-qhxx-j73r-qpm2 in your dependencies?
O3 detects GHSA-qhxx-j73r-qpm2 across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.