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MEDIUM severity

Deadlock in mutually recursive `tf.function` objectsGHSA-h67m-xg8f-fxcf

MEDIUMFix: tensorflow/tensorflow@afac815

GHSA-h67m-xg8f-fxcf is a medium-severity (CVSS 5.5) CWE-667 vulnerability in tensorflow. A fix is available for tensorflow — see the affected versions and patch details below.

Also known asBIT-tensorflow-2021-41213CVE-2021-41213PYSEC-2021-405PYSEC-2021-622PYSEC-2021-820
Published
Updated
Affected
9 pkgs
Patched
9 / 9
Exploits
None indexed
Exploitation data as of Nov 10, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

EPSS Exploitation Probability

via FIRST.org ↗
0.2%probability of exploitation in next 30 days
Lower Risk0.00%
Lower risk than most CVEs16th percentile — riskier than 16% of all scored CVEsHighest risk
0.00%0.25%0.50%0.74%0.0%0.2%May 26Sep 26Nov 26

Probability of exploitation in the next 30 days, from FIRST.org EPSS.

How urgent is this, really

Where this sits among everything scored

Real-World Exposure

9 pkgs affected
🐍tensorflow🐍tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-gpu🐍tensorflow-gpu+1 more

Real-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 code behind tf.function API can be made to deadlock when two tf.function decorated Python functions are mutually recursive:

import tensorflow as tf

@tf.function()               
def fun1(num):
    if num == 1:
        return
    print(num)
    fun2(num-1)

@tf.function()
def fun2(num):
    if num == 0:
        return
    print(num)
    fun1(num-1)

fun1(9)

This occurs due to using a non-reentrant Lock Python object.

Loading any model which contains mutually recursive functions is vulnerable. An attacker can cause denial of service by causing users to load such models and calling a recursive tf.function, although this is not a frequent scenario.

Patches

We have patched the issue in GitHub commit afac8158d43691661ad083f6dd9e56f327c1dcb7.

The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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 members of the Aivul Team from Qihoo 360.

Affected Packages

9 total 9 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPItensorflow≥ 2.6.0&&< 2.6.12.6.1pip install --upgrade 'tensorflow==2.6.1'
🐍PyPItensorflow≥ 2.5.0&&< 2.5.22.5.2pip install --upgrade 'tensorflow==2.5.2'
🐍PyPItensorflowall versions2.4.4pip install --upgrade 'tensorflow==2.4.4'
🐍PyPItensorflow-cpu≥ 2.6.0&&< 2.6.12.6.1pip install --upgrade 'tensorflow-cpu==2.6.1'
🐍PyPItensorflow-cpu≥ 2.5.0&&< 2.5.22.5.2pip install --upgrade 'tensorflow-cpu==2.5.2'
🐍PyPItensorflow-cpuall versions2.4.4pip install --upgrade 'tensorflow-cpu==2.4.4'

Affected Products

1 product · 5 configurations
Application
tensorflowgoogle
≥ 2.6.0 && < 2.6.1
1 version
2.7.0

Detection & mitigation playbook

Open-source dependency
  1. Detect

    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.

  2. Fix

    Update tensorflow to 2.6.1 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-h67m-xg8f-fxcf is resolved across your whole dependency graph.

  3. Workarounds

    Cap what an attacker can consume: apply request size, rate and timeout limits in front of the affected component, and run it with memory and CPU limits so exhaustion degrades one worker rather than the whole service.

Frequently Asked Questions

### Impact The [code behind `tf.function` API](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/python/eager/def_function.py#L542) can be made to deadlock when two `tf.function` decorated Python functions are mutually recursive: ```python import tensorflow as tf @tf.function() def fun1(num): if num == 1: return print(num) fun2(num-1) @tf.function() def fun2(num): if num == 0: return print(num) fun1(num-1) fun1(9) ``` This occurs due to using a non-reentrant `Lock` Python object. Loadi
O3 Security · Impact-Aware SCA

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Deadlock in mutually recursive `tf.function` objects