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MEDIUM severity

CVE-2020-15213 — tensorflow

MEDIUMFix: tensorflow/tensorflow@204945b

CVE-2020-15213 is a medium-severity (CVSS 4) Buffer Overflow vulnerability in tensorflow. 1 public exploit reference exists, so weaponization risk is real. A fix is available for tensorflow — see the affected versions and patch details below.

Denial of service in tensorflow-lite

Also known asBIT-tensorflow-2020-15213GHSA-hjmq-236j-8m87PYSEC-2020-136PYSEC-2020-293PYSEC-2020-328
Published
Updated
Affected
6 pkgs
Patched
6 / 6
Exploits
1 known
Exploitation data as of Oct 10, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

EPSS Exploitation Probability

via FIRST.org ↗
0.8%probability of exploitation in next 30 days
Lower Risk0.00%
Lower risk than most CVEs54th percentile — riskier than 54% of all scored CVEsHighest risk
0.00%0.42%0.84%1.26%0.2%0.8%Apr 26Aug 26Oct 26

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

How urgent is this, really

CVE-2020-15213 by exploitation likelihood (EPSS) against impact (CVSS). Outside the shaded patch-first corner.

Where this sits among everything scored

Of 385,987 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Counts from FIRST.org, log-scaled.

Real-World Exposure

6 pkgs affected
🐍tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-gpu🐍tensorflow-gpu

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

In TensorFlow Lite models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/segment_sum.cc#L39-L44

Patches

We have patched the issue in 204945b and will release patch releases for all affected versions.

We recommend users to upgrade to TensorFlow 2.2.1, or 2.3.1.

Workarounds

A potential workaround would be to add a custom Verifier to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps.

However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.

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 discovered from a variant analysis of GHSA-p2cq-cprg-frvm.

Affected Packages

6 total 6 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPItensorflow≥ 2.2.0&&< 2.2.12.2.1pip install --upgrade 'tensorflow==2.2.1'
🐍PyPItensorflow≥ 2.3.0&&< 2.3.12.3.1pip install --upgrade 'tensorflow==2.3.1'
🐍PyPItensorflow-cpu≥ 2.2.0&&< 2.2.12.2.1pip install --upgrade 'tensorflow-cpu==2.2.1'
🐍PyPItensorflow-cpu≥ 2.3.0&&< 2.3.12.3.1pip install --upgrade 'tensorflow-cpu==2.3.1'
🐍PyPItensorflow-gpu≥ 2.2.0&&< 2.2.12.2.1pip install --upgrade 'tensorflow-gpu==2.2.1'
🐍PyPItensorflow-gpu≥ 2.3.0&&< 2.3.12.3.1pip install --upgrade 'tensorflow-gpu==2.3.1'

Affected Products

1 product · 2 configurations
Application
tensorflowgoogle
≥ 2.3.0 && < 2.3.1
range
Exploits & PoCs
1

Research 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 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 2.2.1 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2020-15213 is resolved across your whole dependency graph.

  3. Workarounds

    Constrain what reaches the vulnerable code: limit the size and shape of untrusted input, isolate the affected component in a sandboxed or least-privileged process, and enable the platform's memory-safety mitigations (ASLR, stack protector, hardened allocator) so an out-of-bounds access is more likely to fail closed than to be exploitable.

Frequently Asked Questions

### Impact In TensorFlow Lite models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/segment_sum.cc#L39-L44 ### Patches We have patched the issue in 204945b and will release patch releases for all affected versions. We recommend u
O3 Security · Impact-Aware SCA

Is CVE-2020-15213 in your dependencies?

Find it across PyPI, including transitive dependencies.

CVE-2020-15213: tensorflow DoS — Fixed in 2.2.1