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🛡️ CVE-2022-36000 — tensorflow

🟠 CVSS 8.0 — High ✅ No Known Exploit CWE-476 OSV
8.0
CVSS Score
0 Low4 Medium7 High9 Critical10

Description

TensorFlow vulnerable to null dereference on MLIR on empty function attributes

Impact

Eig can be fed an incorrect Tout input, resulting in a CHECK fail that can trigger a denial of service attack.

```python

import tensorflow as tf

import numpy as np

arg_0=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)

arg_1=tf.complex128

arg_2=True

arg_3=''

tf.raw_ops.Eig(input=arg_0, Tout=arg_1, compute_v=arg_2, name=arg_3)

```

Patches

We have patched the issue in GitHub commit [aed36912609fc07229b4d0a7b44f3f48efc00fd0](https://github.com/tensorflow/tensorflow/commit/aed36912609fc07229b4d0a7b44f3f48efc00fd0).

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 刘力源, Information System & Security and Countermeasures Experiments Center, Beijing Institute of Technology.

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-2022-36000 is classified as CWE-476: NULL Pointer Dereference. A pointer that can be null is used without a check, crashing the process.

Affected software

CVE-2022-36000 is recorded against 3 packages.

  • tensorflow (from 2.9.0 up to 2.9.1)
  • 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 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)
nvd.nist.gov (Advisory)
github.com (Web)
github.com (Package)
github.com (Web)
pypi.org (Package)
github.com (Advisory)

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-476: NULL Pointer Dereference) 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-476
Public Exploit ✅ No
Source OSV
Published 2026-07-13
Updated 2026-08-20
Modified 2026-07-13

Affected Packages

Software From version Fixed in
tensorflow 2.9.0 2.9.1
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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