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🛡️ CVE-2022-35960 — tensorflow

🟠 CVSS 8.0 — High ✅ No Known Exploit CWE-617 OSV
8.0
CVSS Score
0 Low4 Medium7 High9 Critical10

Description

TensorFlow vulnerable to CHECK failure in TensorListReserve via missing validation

Impact

In [core/kernels/list_kernels.cc's TensorListReserve](https://github.com/tensorflow/tensorflow/blob/c8ba76d48567aed347508e0552a257641931024d/tensorflow/core/kernels/list_kernels.cc#L322-L325), num_elements is assumed to be a tensor of size 1. When a num_elements of more than 1 element is provided, then tf.raw_ops.TensorListReserve fails the CHECK_EQ in CheckIsAlignedAndSingleElement.

```python

import tensorflow as tf

tf.raw_ops.TensorListReserve(element_shape=(1,1), num_elements=tf.constant([1,1], dtype=tf.int32), element_dtype=tf.int8)

```

Patches

We have patched the issue in GitHub commit [b5f6fbfba76576202b72119897561e3bd4f179c7](https://github.com/tensorflow/tensorflow/commit/b5f6fbfba76576202b72119897561e3bd4f179c7).

The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range.

For more information

Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by Kang Hong Jin from Singapore Management University.

How this vulnerability can be exploited

This issue can be reached over the network, attack complexity is high, an attacker needs no privileges on the target. No user interaction is required. The scope is unchanged, so the impact stays within the vulnerable component. Rated impact: confidentiality none, integrity none, availability high.

CVSS metrics in full

The score comes from this vector: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H

  • Attack vector: Network — reachable from anywhere that can route to the service.
  • Attack complexity: High — the attacker first has to win a race, learn a secret or otherwise prepare the target.
  • Privileges required: None — an unauthenticated stranger can try it.
  • User interaction: None — nobody has to be tricked into anything.
  • Scope: Unchanged — the damage stays inside the vulnerable component.
  • Confidentiality impact: None.
  • Integrity impact: None.
  • Availability impact: High — total loss, or loss the attacker controls.

Weakness class

CVE-2022-35960 is classified as CWE-617: Reachable Assertion. The product contains an assert() or similar statement that can be triggered by an attacker, which leads to an application exit or other behavior that is more severe than necessary.

Affected software

CVE-2022-35960 is recorded against 3 packages.

  • tensorflow
  • tensorflow-cpu (from 2.9.0 up to 2.9.1)
  • tensorflow-gpu (from 2.9.0 up to 2.9.1)

Timeline and source

Published on 6 March 2024 and last revised on 13 July 2026. No public exploit is currently recorded for this entry. A vendor advisory or fix has been published. Record sourced from OSV.

References

github.com (Web)
github.com (Web)
github.com (Web)
nvd.nist.gov (Web)

Other advisories for this package

tensorflow has other advisories on record. If you are patching this one, these are worth checking on the same host:

Same weakness in other software

These advisories are the same class of weakness (CWE-617: Reachable Assertion) in other software:

Details

Severity HIGH
CVSS Score 8.0
CVSS Vector CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H
CWE CWE-617
Public Exploit ✅ No
Source OSV
Published 2024-03-06
Updated 2026-08-20
Modified 2026-07-13

Affected Packages

Software From version Fixed in
tensorflow — —
tensorflow-cpu 2.9.0 2.9.1
tensorflow-gpu 2.9.0 2.9.1

Similar Threats

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ⓘ Data Notice: The information presented above has been compiled from publicly available internet sources. Boteraser aggregates this data solely for informational purposes and does not independently classify, evaluate, or endorse any findings about the vulnerabilities listed. The accuracy and completeness of this information is the sole responsibility of the original publishers. Boteraser and its operators accept no liability for any decisions made based on this data.

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