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CVE-2021-37679 — tensorflow

HIGHFix: tensorflow/tensorflow@4e25654

CVE-2021-37679 is a high-severity (CVSS 7.8) Out-of-bounds Read vulnerability in tensorflow. A fix is available for tensorflow — see the affected versions and patch details below.

Heap OOB in nested `tf.map_fn` with `RaggedTensor`s

Also known asBIT-tensorflow-2021-37679GHSA-g8wg-cjwc-xhhpPYSEC-2021-301PYSEC-2021-592PYSEC-2021-790
Published
Updated
Affected
9 pkgs
Patched
9 / 9
Exploits
None indexed
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 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-2021-37679 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

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

It is possible to nest a tf.map_fn within another tf.map_fn call. However, if the input tensor is a RaggedTensor and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap:

import tensorflow as tf
x = tf.ragged.constant([[1,2,3], [4,5], [6]])
t = tf.map_fn(lambda r: tf.map_fn(lambda y: r, r), x)
z = tf.ragged.constant([[[1,2,3],[1,2,3],[1,2,3]],[[4,5],[4,5]],[[6]]])

The t and z outputs should be identical, however this is not the case. The last row of t contains data from the heap which can be used to leak other memory information.

The bug lies in the conversion from a Variant tensor to a RaggedTensor. The implementation does not check that all inner shapes match and this results in the additional dimensions in the above example.

The same implementation can result in data loss, if input tensor is tweaked:

import tensorflow as tf
x = tf.ragged.constant([[1,2], [3,4,5], [6]])
t = tf.map_fn(lambda r: tf.map_fn(lambda y: r, r), x)

Here, the output tensor will only have 2 elements for each inner dimension.

Patches

We have patched the issue in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12.

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 Haris Sahovic.

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-2021-37679 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 It is possible to nest a `tf.map_fn` within another `tf.map_fn` call. However, if the input tensor is a `RaggedTensor` and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap: ```python import tensorflow as tf x = tf.ragged.constant([[1,2,3], [4,5], [6]]) t = tf.map_fn(lambda r: tf.map_fn(lambda y: r, r), x) z = tf.ragged.constant([[[1,2,3],[1,2,3],[1,2,3]],[[4,5],[4,5]],[[6]]]) ``` The `t` and `z` outputs should be identical, however this is not the case. The last row of `
O3 Security · Impact-Aware SCA

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CVE-2021-37679: tensorflow — Fixed in 2.3.4