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LOW severity

Segfault in SparseCountSparseOutputGHSA-hr84-fqvp-48mm

LOWFix: tensorflow/tensorflow@c57c0b9

GHSA-hr84-fqvp-48mm is a low-severity (CVSS 2.5) CWE-131 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.

Also known asBIT-tensorflow-2021-29521CVE-2021-29521PYSEC-2021-158PYSEC-2021-449PYSEC-2021-647
Published
Updated
Affected
6 pkgs
Patched
6 / 6
Exploits
1 known
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 CVEs8th percentile — riskier than 8% of all scored CVEsHighest risk
0.00%0.23%0.46%0.69%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

GHSA-hr84-fqvp-48mm 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

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

Specifying a negative dense shape in tf.raw_ops.SparseCountSparseOutput results in a segmentation fault being thrown out from the standard library as std::vector invariants are broken.

import tensorflow as tf

indices = tf.constant([], shape=[0, 0], dtype=tf.int64)
values = tf.constant([], shape=[0, 0], dtype=tf.int64)
dense_shape = tf.constant([-100, -100, -100], shape=[3], dtype=tf.int64)
weights = tf.constant([], shape=[0, 0], dtype=tf.int64)

tf.raw_ops.SparseCountSparseOutput(indices=indices, values=values, dense_shape=dense_shape, weights=weights, minlength=79, maxlength=96, binary_output=False)

This is because the implementation assumes the first element of the dense shape is always positive and uses it to initialize a BatchedMap<T> (i.e., std::vector<absl::flat_hash_map<int64,T>>) data structure.

  bool is_1d = shape.NumElements() == 1;
  int num_batches = is_1d ? 1 : shape.flat<int64>()(0);
  ...
  auto per_batch_counts = BatchedMap<W>(num_batches);

If the shape tensor has more than one element, num_batches is the first value in shape.

Ensuring that the dense_shape argument is a valid tensor shape (that is, all elements are non-negative) solves this issue.

Patches

We have patched the issue in GitHub commit c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5.

The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3.

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 Yakun Zhang and Ying Wang of Baidu X-Team.

Affected Packages

6 total 6 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPItensorflow≥ 2.3.0&&< 2.3.32.3.3pip install --upgrade 'tensorflow==2.3.3'
🐍PyPItensorflow≥ 2.4.0&&< 2.4.22.4.2pip install --upgrade 'tensorflow==2.4.2'
🐍PyPItensorflow-cpu≥ 2.3.0&&< 2.3.32.3.3pip install --upgrade 'tensorflow-cpu==2.3.3'
🐍PyPItensorflow-cpu≥ 2.4.0&&< 2.4.22.4.2pip install --upgrade 'tensorflow-cpu==2.4.2'
🐍PyPItensorflow-gpu≥ 2.3.0&&< 2.3.32.3.3pip install --upgrade 'tensorflow-gpu==2.3.3'
🐍PyPItensorflow-gpu≥ 2.4.0&&< 2.4.22.4.2pip install --upgrade 'tensorflow-gpu==2.4.2'

Affected Products

1 product · 2 configurations
Application
tensorflowgoogle
≥ 2.4.0 && < 2.4.2
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.

TensorFlow is an end-to-end open source platform for machine learning. S…

github.comNVDMay 2021

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 2.3.3 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-hr84-fqvp-48mm 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 Specifying a negative dense shape in `tf.raw_ops.SparseCountSparseOutput` results in a segmentation fault being thrown out from the standard library as `std::vector` invariants are broken. ```python import tensorflow as tf indices = tf.constant([], shape=[0, 0], dtype=tf.int64) values = tf.constant([], shape=[0, 0], dtype=tf.int64) dense_shape = tf.constant([-100, -100, -100], shape=[3], dtype=tf.int64) weights = tf.constant([], shape=[0, 0], dtype=tf.int64) tf.raw_ops.SparseCountSparseOutput(indices=indices, values=values, dense_shape=dense_shape, weights=weights, minlength=79,
O3 Security · Impact-Aware SCA

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Segfault in SparseCountSparseOutput (Low 2.5)