GHSA-f49c-87jh-g47q is a high-severity (CVSS 8) CWE-415 vulnerability in tensorflow. A fix is available for tensorflow — see the affected versions and patch details below.
TensorFlow has double free in Fractional(Max/Avg)Pool
Exploitation Status
No confirmed exploitation observed yet
- A successful exploit gives an attacker total control of the affected component, not partial access.
- CISA’s own triage has not observed active exploitation or public proof-of-concept code for this CVE as of its last assessment.
Exploitation and automatability from CISA’s SSVC triage for GHSA-f49c-87jh-g47q.
EPSS Exploitation Probability
Probability of exploitation in the next 30 days, from FIRST.org EPSS.
How urgent is this, really
GHSA-f49c-87jh-g47q by exploitation likelihood (EPSS) against impact (CVSS). Outside the shaded patch-first corner.
Where this sits among everything scored
Of 379,842 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Counts from FIRST.org, log-scaled.
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
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 | 2.11.1pip install --upgrade 'tensorflow==2.11.1' |
| 🐍PyPI | tensorflow-cpu | all versions | 2.11.1pip install --upgrade 'tensorflow-cpu==2.11.1' |
| 🐍PyPI | tensorflow-gpu | all versions | 2.11.1pip install --upgrade 'tensorflow-gpu==2.11.1' |
Affected Products
tensorflowgoogleDetection & mitigation playbook
Open-source dependencyDetect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for tensorflow, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.
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.
Frequently Asked Questions
Is GHSA-f49c-87jh-g47q in your dependencies?
Find it across PyPI, including transitive dependencies.