GHSA-8fvv-46hw-vpg3
MEDIUMGHSA-8fvv-46hw-vpg3 is a medium-severity (CVSS 4.8) CWE-131 vulnerability in tensorflow. 1 public exploit reference exists, so weaponization risk is real. O3 Security confirms whether GHSA-8fvv-46hw-vpg3 is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.
Overflow in `tf.keras.losses.poisson`
Blast Radius
tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-gpu🐍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
tf.keras.losses.poisson receives a y_pred and y_true that are passed through functor::mul in BinaryOp. If the resulting dimensions overflow an int32, TensorFlow will crash due to a size mismatch during broadcast assignment.
import numpy as np
import tensorflow as tf
true_value = tf.reshape(shape=[1, 2500000000], tensor = tf.zeros(dtype=tf.bool, shape=[50000, 50000]))
pred_value = np.array([[[-2]], [[8]]], dtype = np.float64)
tf.keras.losses.poisson(y_true=true_value,y_pred=pred_value)
Patches
We have patched the issue in GitHub commit c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c.
The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1 and 2.9.3, as these are also affected and still in supported range. However, we will not cherrypick this commit into TensorFlow 2.8.x, as it depends on Eigen behavior that changed between 2.8 and 2.9.
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 Pattarakrit Rattankul.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | tensorflow | all versions | 2.9.3 |
| 🐍PyPI | tensorflow | ≥ 2.10.0&&< 2.10.1 | 2.10.1 |
| 🐍PyPI | tensorflow-cpu | all versions | 2.9.3 |
| 🐍PyPI | tensorflow-gpu | all versions | 2.9.3 |
| 🐍PyPI | tensorflow-cpu | ≥ 2.10.0&&< 2.10.1 | 2.10.1 |
| 🐍PyPI | tensorflow-gpu | ≥ 2.10.0&&< 2.10.1 | 2.10.1 |
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 2.9.3 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-8fvv-46hw-vpg3 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-8fvv-46hw-vpg3 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-8fvv-46hw-vpg3. 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-8fvv-46hw-vpg3 in your dependencies?
O3 detects GHSA-8fvv-46hw-vpg3 across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.