GHSA-mxjj-953w-2c2v is a high-severity (CVSS 7.4) Out-of-bounds Read vulnerability in tensorflow. 1 public exploit reference exists, so weaponization risk is real. O3 Security confirms whether GHSA-mxjj-953w-2c2v is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.
Data corruption in tensorflow-lite
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
When determining the common dimension size of two tensors, TFLite uses a DCHECK which is no-op outside of debug compilation modes:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/internal/types.h#L437-L442
Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors.
Patches
We have patched the issue in 8ee24e7949a20 and will release patch releases for all versions between 1.15 and 2.3.
We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 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 reported by members of the Aivul Team from Qihoo 360.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | tensorflow | all versions | 1.15.4 |
| 🐍PyPI | tensorflow | ≥ 2.0.0&&< 2.0.3 | 2.0.3 |
| 🐍PyPI | tensorflow | ≥ 2.1.0&&< 2.1.2 | 2.1.2 |
| 🐍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 | all versions | 1.15.4 |
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 1.15.4 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-mxjj-953w-2c2v 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-mxjj-953w-2c2v 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-mxjj-953w-2c2v. 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-mxjj-953w-2c2v in your dependencies?
O3 detects GHSA-mxjj-953w-2c2v across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.