GHSA-hjmq-236j-8m87 is a medium-severity (CVSS 4) Buffer Overflow vulnerability in tensorflow. 1 public exploit reference exists, so weaponization risk is real. O3 Security confirms whether GHSA-hjmq-236j-8m87 is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.
Denial of service in tensorflow-lite
Real-World Exposure
tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-gpu🐍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
In TensorFlow Lite models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/segment_sum.cc#L39-L44
Patches
We have patched the issue in 204945b and will release patch releases for all affected versions.
We recommend users to upgrade to TensorFlow 2.2.1, or 2.3.1.
Workarounds
A potential workaround would be to add a custom Verifier to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps.
However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.
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 discovered from a variant analysis of GHSA-p2cq-cprg-frvm.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍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 | ≥ 2.2.0&&< 2.2.1 | 2.2.1 |
| 🐍PyPI | tensorflow-cpu | ≥ 2.3.0&&< 2.3.1 | 2.3.1 |
| 🐍PyPI | tensorflow-gpu | ≥ 2.2.0&&< 2.2.1 | 2.2.1 |
| 🐍PyPI | tensorflow-gpu | ≥ 2.3.0&&< 2.3.1 | 2.3.1 |
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 2.2.1 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-hjmq-236j-8m87 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-hjmq-236j-8m87 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-hjmq-236j-8m87. 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-hjmq-236j-8m87 in your dependencies?
O3 detects GHSA-hjmq-236j-8m87 across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.