GHSA-f49c-87jh-g47q
HIGHTensorFlow has double free in Fractional(Max/Avg)Pool
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
nn_ops.fractional_avg_pool_v2 and nn_ops.fractional_max_pool_v2 require the first and fourth elements of their parameter pooling_ratio to be equal to 1.0, as pooling on batch and channel dimensions is not supported.
import tensorflow as tf
import os
import numpy as np
from tensorflow.python.ops import nn_ops
try:
arg_0_tensor = tf.random.uniform([3, 30, 50, 3], dtype=tf.float64)
arg_0 = tf.identity(arg_0_tensor)
arg_1_0 = 2
arg_1_1 = 3
arg_1_2 = 1
arg_1_3 = 1
arg_1 = [arg_1_0,arg_1_1,arg_1_2,arg_1_3,]
arg_2 = True
arg_3 = True
seed = 341261001
out = nn_ops.fractional_avg_pool_v2(arg_0,arg_1,arg_2,arg_3,seed=seed,)
except Exception as e:
print("Error:"+str(e))
Patches
We have patched the issue in GitHub commit ee50d1e00f81f62a4517453f721c634bbb478307.
The fix will be included in TensorFlow 2.12. We will also cherrypick this commit on TensorFlow 2.11.1.
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 was reported by dmc1778, of [email protected].
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | tensorflow | all versions | 2.11.1 |
| 🐍PyPI | tensorflow-cpu | all versions | 2.11.1 |
| 🐍PyPI | tensorflow-gpu | all versions | 2.11.1 |
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 2.11.1 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-f49c-87jh-g47q 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-f49c-87jh-g47q 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-f49c-87jh-g47q. 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-f49c-87jh-g47q in your dependencies?
O3 detects GHSA-f49c-87jh-g47q across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.