GHSA-6f89-8j54-29xf is a low-severity (CVSS 2.5) Buffer Overflow vulnerability in tensorflow. 1 public exploit reference exists, so weaponization risk is real. O3 Security confirms whether GHSA-6f89-8j54-29xf is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.
Heap buffer overflow in `FractionalAvgPoolGrad`
Real-World Exposure
tensorflow🐍tensorflow🐍tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu+4 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
The implementation of tf.raw_ops.FractionalAvgPoolGrad is vulnerable to a heap buffer overflow:
import tensorflow as tf
orig_input_tensor_shape = tf.constant([1, 3, 2, 3], shape=[4], dtype=tf.int64)
out_backprop = tf.constant([2], shape=[1, 1, 1, 1], dtype=tf.int64)
row_pooling_sequence = tf.constant([1], shape=[1], dtype=tf.int64)
col_pooling_sequence = tf.constant([1], shape=[1], dtype=tf.int64)
tf.raw_ops.FractionalAvgPoolGrad(
orig_input_tensor_shape=orig_input_tensor_shape, out_backprop=out_backprop,
row_pooling_sequence=row_pooling_sequence,
col_pooling_sequence=col_pooling_sequence, overlapping=False)
The implementation fails to validate that the pooling sequence arguments have enough elements as required by the out_backprop tensor shape.
Patches
We have patched the issue in GitHub commit 12c727cee857fa19be717f336943d95fca4ffe4f.
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
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 Ying Wang and Yakun Zhang of Baidu X-Team.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | tensorflow | all versions | 2.1.4 |
| 🐍PyPI | tensorflow | ≥ 2.2.0&&< 2.2.3 | 2.2.3 |
| 🐍PyPI | tensorflow | ≥ 2.3.0&&< 2.3.3 | 2.3.3 |
| 🐍PyPI | tensorflow | ≥ 2.4.0&&< 2.4.2 | 2.4.2 |
| 🐍PyPI | tensorflow-cpu | all versions | 2.1.4 |
| 🐍PyPI | tensorflow-cpu | ≥ 2.2.0&&< 2.2.3 | 2.2.3 |
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 2.1.4 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-6f89-8j54-29xf 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-6f89-8j54-29xf 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-6f89-8j54-29xf. 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-6f89-8j54-29xf in your dependencies?
O3 detects GHSA-6f89-8j54-29xf across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.