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HIGH severity

CVE-2021-29608 — tensorflow

HIGHFix: tensorflow/tensorflow@b761c9b

CVE-2021-29608 is a high-severity (CVSS 7.8) 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.

Heap OOB and null pointer dereference in `RaggedTensorToTensor`

Also known asBIT-tensorflow-2021-29608GHSA-rgvq-pcvf-hx75PYSEC-2021-245PYSEC-2021-536PYSEC-2021-734
Published
Updated
Affected
12 pkgs
Patched
12 / 12
Exploits
1 known
Exploitation data as of Oct 10, 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 CVEs13th percentile — riskier than 13% of all scored CVEsHighest risk
0.00%0.24%0.49%0.73%0.1%0.2%Apr 26Aug 26Oct 26

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

How urgent is this, really

CVE-2021-29608 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

12 pkgs affected
🐍tensorflow🐍tensorflow🐍tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu+4 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

Due to lack of validation in tf.raw_ops.RaggedTensorToTensor, an attacker can exploit an undefined behavior if input arguments are empty:

import tensorflow as tf

shape = tf.constant([-1, -1], shape=[2], dtype=tf.int64)
values = tf.constant([], shape=[0], dtype=tf.int64)
default_value = tf.constant(404, dtype=tf.int64)
row = tf.constant([269, 404, 0, 0, 0, 0, 0], shape=[7], dtype=tf.int64)
rows = [row]
types = ['ROW_SPLITS']

tf.raw_ops.RaggedTensorToTensor(
  shape=shape, values=values, default_value=default_value, 
  row_partition_tensors=rows, row_partition_types=types)

The implementation only checks that one of the tensors is not empty, but does not check for the other ones.

There are multiple DCHECK validations to prevent heap OOB, but these are no-op in release builds, hence they don't prevent anything.

Patches

We have patched the issue in GitHub commit b761c9b652af2107cfbc33efd19be0ce41daa33e followed by GitHub commit f94ef358bb3e91d517446454edff6535bcfe8e4a and GitHub commit c4d7afb6a5986b04505aca4466ae1951686c80f6.

The fix will be included in TensorFlow 2.5.0. We will also cherrypick these commits on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, 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 Yakun Zhang and Ying Wang of Baidu X-Team.

Affected Packages

12 total 12 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPItensorflowall versions2.1.4pip install --upgrade 'tensorflow==2.1.4'
🐍PyPItensorflow≥ 2.2.0&&< 2.2.32.2.3pip install --upgrade 'tensorflow==2.2.3'
🐍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-cpuall versions2.1.4pip install --upgrade 'tensorflow-cpu==2.1.4'
🐍PyPItensorflow-cpu≥ 2.2.0&&< 2.2.32.2.3pip install --upgrade 'tensorflow-cpu==2.2.3'

Affected Products

1 product · 4 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.

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 2.1.4 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2021-29608 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 Due to lack of validation in `tf.raw_ops.RaggedTensorToTensor`, an attacker can exploit an undefined behavior if input arguments are empty: ```python import tensorflow as tf shape = tf.constant([-1, -1], shape=[2], dtype=tf.int64) values = tf.constant([], shape=[0], dtype=tf.int64) default_value = tf.constant(404, dtype=tf.int64) row = tf.constant([269, 404, 0, 0, 0, 0, 0], shape=[7], dtype=tf.int64) rows = [row] types = ['ROW_SPLITS'] tf.raw_ops.RaggedTensorToTensor( shape=shape, values=values, default_value=default_value, row_partition_tensors=rows, row_partition_types=typ
O3 Security · Impact-Aware SCA

Is CVE-2021-29608 in your dependencies?

Find it across PyPI, including transitive dependencies.

CVE-2021-29608: tensorflow — Fixed in 2.1.4