CVE-2021-37665 is a high-severity (CVSS 7.8) Improper Input Validation vulnerability in tensorflow. A fix is available for tensorflow — see the affected versions and patch details below.
Incomplete validation in MKL requantization
EPSS Exploitation Probability
Probability of exploitation in the next 30 days, from FIRST.org EPSS.
How urgent is this, really
CVE-2021-37665 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
Due to incomplete validation in MKL implementation of requantization, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays:
import tensorflow as tf
tf.raw_ops.RequantizationRangePerChannel(
input=[],
input_min=[0,0,0,0,0],
input_max=[1,1,1,1,1],
clip_value_max=1)
The implementation does not validate the dimensions of the input tensor.
A similar issue occurs in MklRequantizePerChannelOp:
import tensorflow as tf
from tensorflow.python.ops import gen_math_ops
gen_math_ops.requantize_per_channel(
input=[],
input_min=[-100,-100,-100,-100,-100],
input_max=[-100,-100,-100],
requested_output_min=[-100,-100,-100,-100,-100],
requested_output_max=[],
out_type=tf.int)
The implementation does not perform full validation for all the input arguments.
Patches
We have patched the issue in GitHub commit 9e62869465573cb2d9b5053f1fa02a81fce21d69 and in the Github commit 203214568f5bc237603dbab6e1fd389f1572f5c9.
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | tensorflow | all versions | 2.3.4pip install --upgrade 'tensorflow==2.3.4' |
| 🐍PyPI | tensorflow | ≥ 2.4.0&&< 2.4.3 | 2.4.3pip install --upgrade 'tensorflow==2.4.3' |
| 🐍PyPI | tensorflow | ≥ 2.5.0&&< 2.5.1 | 2.5.1pip install --upgrade 'tensorflow==2.5.1' |
| 🐍PyPI | tensorflow-cpu | all versions | 2.3.4pip install --upgrade 'tensorflow-cpu==2.3.4' |
| 🐍PyPI | tensorflow-cpu | ≥ 2.4.0&&< 2.4.3 | 2.4.3pip install --upgrade 'tensorflow-cpu==2.4.3' |
| 🐍PyPI | tensorflow-cpu | ≥ 2.5.0&&< 2.5.1 | 2.5.1pip install --upgrade 'tensorflow-cpu==2.5.1' |
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 2.3.4 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2021-37665 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-2021-37665 in your dependencies?
Find it across PyPI, including transitive dependencies.