Uninitialized memory access in TensorFlowGHSA-qhxx-j73r-qpm2
MEDIUMFix: tensorflow/tensorflow@ace0c15GHSA-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. A fix is available for tensorflow — see the affected versions and patch details below.
EPSS Exploitation Probability
Probability of exploitation in the next 30 days, from FIRST.org EPSS.
How urgent is this, really
GHSA-qhxx-j73r-qpm2 by exploitation likelihood (EPSS) against impact (CVSS). Outside the shaded patch-first corner.
Where this sits among everything scored
Of 385,738 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Counts from FIRST.org, log-scaled.
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 | 1.15.5pip install --upgrade 'tensorflow==1.15.5' |
| 🐍PyPI | tensorflow | ≥ 2.0.0&&< 2.0.4 | 2.0.4pip install --upgrade 'tensorflow==2.0.4' |
| 🐍PyPI | tensorflow | ≥ 2.1.0&&< 2.1.3 | 2.1.3pip install --upgrade 'tensorflow==2.1.3' |
| 🐍PyPI | tensorflow | ≥ 2.2.0&&< 2.2.2 | 2.2.2pip install --upgrade 'tensorflow==2.2.2' |
| 🐍PyPI | tensorflow | ≥ 2.3.0&&< 2.3.2 | 2.3.2pip install --upgrade 'tensorflow==2.3.2' |
| 🐍PyPI | tensorflow-cpu | all versions | 1.15.5pip install --upgrade 'tensorflow-cpu==1.15.5' |
Affected Products
tensorflowgoogleResearch 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, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.
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.
Frequently Asked Questions
Is GHSA-qhxx-j73r-qpm2 in your dependencies?
Find it across PyPI, including transitive dependencies.