GHSA-gpfh-jvf9-7wg5 is a high-severity (CVSS 7.8) Use After Free vulnerability in tensorflow. 1 public exploit reference exists, so weaponization risk is real. O3 Security confirms whether GHSA-gpfh-jvf9-7wg5 is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.
Use after free / memory leak in `CollectiveReduceV2`
Real-World Exposure
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 async implementation of CollectiveReduceV2 suffers from a memory leak and a use after free:
import tensorflow as tf
tf.raw_ops.CollectiveReduceV2(
input=[],
group_size=[-10, -10, -10],
group_key=[-10, -10],
instance_key=[-10],
ordering_token=[],
merge_op='Mul',
final_op='Div')
This occurs due to the asynchronous computation and the fact that objects that have been std::move()d from are still accessed:
auto done_with_cleanup = [col_params, done = std::move(done)]() {
done();
col_params->Unref();
};
OP_REQUIRES_OK_ASYNC(c,
FillCollectiveParams(col_params, REDUCTION_COLLECTIVE,
/*group_size*/ c->input(1),
/*group_key*/ c->input(2),
/*instance_key*/ c->input(3)),
done);
Here, done is already moved from by the time OP_REQUIRES_OK_ASYNC macro needs to invoke it in case of errors. In this case, we get an undefined behavior, which can manifest via crashes, std::bad_alloc throws or just memory leaks.
Patches
We have patched the issue in GitHub commit ca38dab9d3ee66c5de06f11af9a4b1200da5ef75.
The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, as this version is the only one that is also affected.
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 | ≥ 2.6.0&&< 2.6.1 | 2.6.1 |
| 🐍PyPI | tensorflow-cpu | ≥ 2.6.0&&< 2.6.1 | 2.6.1 |
| 🐍PyPI | tensorflow-gpu | ≥ 2.6.0&&< 2.6.1 | 2.6.1 |
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 2.6.1 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-gpfh-jvf9-7wg5 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-gpfh-jvf9-7wg5 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-gpfh-jvf9-7wg5. 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-gpfh-jvf9-7wg5 in your dependencies?
O3 detects GHSA-gpfh-jvf9-7wg5 across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.