CVE-2021-41196 is a medium-severity (CVSS 5.5) CWE-191 vulnerability in tensorflow. 2 public exploit references exist, so weaponization risk is real. A fix is available for tensorflow — see the affected versions and patch details below.
Crash in `max_pool3d` when size argument is 0 or negative
EPSS Exploitation Probability
Probability of exploitation in the next 30 days, from FIRST.org EPSS.
How urgent is this, really
CVE-2021-41196 by exploitation likelihood (EPSS) against impact (CVSS). Outside the shaded patch-first corner.
Where this sits among everything scored
Of 385,738 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🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-gpu🐍tensorflow-gpu+1 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 Keras pooling layers can trigger a segfault if the size of the pool is 0 or if a dimension is negative:
import tensorflow as tf
pool_size = [2, 2, 0]
layer = tf.keras.layers.MaxPooling3D(strides=1, pool_size=pool_size)
input_tensor = tf.random.uniform([3, 4, 10, 11, 12], dtype=tf.float32)
res = layer(input_tensor)
This is due to the TensorFlow's implementation of pooling operations where the values in the sliding window are not checked to be strictly positive.
Patches
We have patched the issue in GitHub commit 12b1ff82b3f26ff8de17e58703231d5a02ef1b8b (merging #51975).
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 externally via a GitHub issue.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | tensorflow | ≥ 2.6.0&&< 2.6.1 | 2.6.1pip install --upgrade 'tensorflow==2.6.1' |
| 🐍PyPI | tensorflow | ≥ 2.5.0&&< 2.5.2 | 2.5.2pip install --upgrade 'tensorflow==2.5.2' |
| 🐍PyPI | tensorflow | all versions | 2.4.4pip install --upgrade 'tensorflow==2.4.4' |
| 🐍PyPI | tensorflow-cpu | ≥ 2.6.0&&< 2.6.1 | 2.6.1pip install --upgrade 'tensorflow-cpu==2.6.1' |
| 🐍PyPI | tensorflow-cpu | ≥ 2.5.0&&< 2.5.2 | 2.5.2pip install --upgrade 'tensorflow-cpu==2.5.2' |
| 🐍PyPI | tensorflow-cpu | all versions | 2.4.4pip install --upgrade 'tensorflow-cpu==2.4.4' |
Affected Products
tensorflowgoogleResearch 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, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.
Fix
Update tensorflow to 2.6.1 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2021-41196 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 CVE-2021-41196 in your dependencies?
Find it across PyPI, including transitive dependencies.