GHSA-mhhc-q96p-mfm9
MEDIUMInfinite loop in TFLite
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-cpu🐍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
The strided slice implementation in TFLite has a logic bug which can allow an attacker to trigger an infinite loop. This arises from newly introduced support for ellipsis in axis definition:
for (int i = 0; i < effective_dims;) {
if ((1 << i) & op_context->params->ellipsis_mask) {
// ...
int ellipsis_end_idx =
std::min(i + 1 + num_add_axis + op_context->input_dims - begin_count,
effective_dims);
// ...
for (; i < ellipsis_end_idx; ++i) {
// ...
}
continue;
}
// ...
++i;
}
An attacker can craft a model such that ellipsis_end_idx is smaller than i (e.g., always negative). In this case, the inner loop does not increase i and the continue statement causes execution to skip over the preincrement at the end of the outer loop.
Patches
We have patched the issue in GitHub commit dfa22b348b70bb89d6d6ec0ff53973bacb4f4695.
The fix will be included in TensorFlow 2.6.0. This is the only affected version.
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 | ≥ 2.6.0rc0&&< 2.6.0rc2 | 2.6.0rc2 |
| 🐍PyPI | tensorflow-cpu | ≥ 2.6.0rc0&&< 2.6.0rc2 | 2.6.0rc2 |
| 🐍PyPI | tensorflow-gpu | ≥ 2.6.0rc0&&< 2.6.0rc2 | 2.6.0rc2 |
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 2.6.0rc2 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-mhhc-q96p-mfm9 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-mhhc-q96p-mfm9 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-mhhc-q96p-mfm9. 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-mhhc-q96p-mfm9 in your dependencies?
O3 detects GHSA-mhhc-q96p-mfm9 across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.