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HIGH severity

CVE-2021-37682 — tensorflow

HIGHFix: tensorflow/tensorflow@4a91f20

CVE-2021-37682 is a high-severity (CVSS 7.1) CWE-908 vulnerability in tensorflow. A fix is available for tensorflow — see the affected versions and patch details below.

Use of unitialized value in TFLite

Also known asBIT-tensorflow-2021-37682GHSA-4c4g-crqm-xrxwPYSEC-2021-304PYSEC-2021-595PYSEC-2021-793
Published
Updated
Affected
9 pkgs
Patched
9 / 9
Exploits
None indexed
Exploitation data as of Oct 11, 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 CVEs7th percentile — riskier than 7% of all scored CVEsHighest risk
0.00%0.23%0.45%0.68%0.0%0.2%Apr 26Aug 26Oct 26

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

How urgent is this, really

CVE-2021-37682 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

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

All TFLite operations that use quantization can be made to use unitialized values. For example:

    const auto* affine_quantization =
        reinterpret_cast<TfLiteAffineQuantization*>(
            filter->quantization.params);

The issue stems from the fact that quantization.params is only valid if quantization.type is different that kTfLiteNoQuantization. However, these checks are missing in large parts of the code.

Patches

We have patched the issue in GitHub commits 537bc7c723439b9194a358f64d871dd326c18887, 4a91f2069f7145aab6ba2d8cfe41be8a110c18a5 and 8933b8a21280696ab119b63263babdb54c298538.

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

9 total 9 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPItensorflowall versions2.3.4pip install --upgrade 'tensorflow==2.3.4'
🐍PyPItensorflow≥ 2.4.0&&< 2.4.32.4.3pip install --upgrade 'tensorflow==2.4.3'
🐍PyPItensorflow≥ 2.5.0&&< 2.5.12.5.1pip install --upgrade 'tensorflow==2.5.1'
🐍PyPItensorflow-cpuall versions2.3.4pip install --upgrade 'tensorflow-cpu==2.3.4'
🐍PyPItensorflow-cpu≥ 2.4.0&&< 2.4.32.4.3pip install --upgrade 'tensorflow-cpu==2.4.3'
🐍PyPItensorflow-cpu≥ 2.5.0&&< 2.5.12.5.1pip install --upgrade 'tensorflow-cpu==2.5.1'

Affected Products

1 product · 6 configurations
Application
tensorflowgoogle
≥ 2.4.0 && < 2.4.3
2 versions
2.5.02.6.0

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 2.3.4 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2021-37682 is resolved across your whole dependency graph.

  3. 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

### Impact All TFLite operations that use quantization can be made to use unitialized values. [For example](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/depthwise_conv.cc#L198-L200): ```cc const auto* affine_quantization = reinterpret_cast<TfLiteAffineQuantization*>( filter->quantization.params); ``` The issue stems from the fact that `quantization.params` is only valid if `quantization.type` is different that `kTfLiteNoQuantization`. However, these checks are missing in large parts of the code. ### Pa
O3 Security · Impact-Aware SCA

Is CVE-2021-37682 in your dependencies?

Find it across PyPI, including transitive dependencies.

CVE-2021-37682: tensorflow — Fixed in 2.3.4