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HIGH severity

CVE-2021-41219 — tensorflow

HIGHFix: tensorflow/tensorflow@e6cf28c

CVE-2021-41219 is a high-severity (CVSS 7.8) CWE-824 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.

Undefined behavior via `nullptr` reference binding in sparse matrix multiplication

Also known asBIT-tensorflow-2021-41219GHSA-4f99-p9c2-3j8xPYSEC-2021-411PYSEC-2021-628PYSEC-2021-826
Published
Updated
Affected
9 pkgs
Patched
9 / 9
Exploits
1 known
Exploitation data as of Oct 9, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

EPSS Exploitation Probability

via FIRST.org ↗
0.2%probability of exploitation in next 30 days
Lower Risk+0.01%
Lower risk than most CVEs10th percentile — riskier than 10% of all scored CVEsHighest risk
0.00%0.24%0.47%0.71%0.2%0.2%Jul 26Oct 26

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

How urgent is this, really

CVE-2021-41219 by exploitation likelihood (EPSS) against impact (CVSS). Outside the shaded patch-first corner.

Where this sits among everything scored

Of 384,993 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

The code for sparse matrix multiplication is vulnerable to undefined behavior via binding a reference to nullptr:

import tensorflow as tf
  
tf.raw_ops.SparseMatMul(
  a=[[1.0,1.0,1.0]],
  b=[[],[],[]],
  transpose_a=False,
  transpose_b=False,
  a_is_sparse=False, 
  b_is_sparse=True)

This occurs whenever the dimensions of a or b are 0 or less. In the case on one of these is 0, an empty output tensor should be allocated (to conserve the invariant that output tensors are always allocated when the operation is successful) but nothing should be written to it (that is, we should return early from the kernel implementation). Otherwise, attempts to write to this empty tensor would result in heap OOB access.

Patches

We have patched the issue in GitHub commit e6cf28c72ba2eb949ca950d834dd6d66bb01cfae.

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 · 3 configurations
Application
tensorflowgoogle
≥ 2.5.0 && < 2.5.2
1 version
2.6.0
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 2.6.1 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2021-41219 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 The [code for sparse matrix multiplication](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/sparse_matmul_op.cc#L954-L1086) is vulnerable to undefined behavior via binding a reference to `nullptr`: ```python import tensorflow as tf tf.raw_ops.SparseMatMul( a=[[1.0,1.0,1.0]], b=[[],[],[]], transpose_a=False, transpose_b=False, a_is_sparse=False, b_is_sparse=True) ``` This occurs whenever the dimensions of `a` or `b` are 0 or less. In the case on one of these is 0, an empty output tensor should be allocat
O3 Security · Impact-Aware SCA

Is CVE-2021-41219 in your dependencies?

Find it across PyPI, including transitive dependencies.

CVE-2021-41219: tensorflow — Fixed in 2.6.1