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🛡️ CVE-2022-41886 — tensorflow

🟠 CVSS 8.0 — High ⚠️ Exploit Public CWE-131 OSV
8.0
CVSS Score
0 Low4 Medium7 High9 Critical10

Description

Overflow in ImageProjectiveTransformV2

Impact

When [tf.raw_ops.ImageProjectiveTransformV2](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/image/image_ops.cc) is given a large output shape, it overflows.

```python

import tensorflow as tf

interpolation = "BILINEAR"

fill_mode = "REFLECT"

images = tf.constant(0.184634328, shape=[2,5,8,3], dtype=tf.float32)

transforms = tf.constant(0.378575385, shape=[2,8], dtype=tf.float32)

output_shape = tf.constant([1879048192,1879048192], shape=[2], dtype=tf.int32)

tf.raw_ops.ImageProjectiveTransformV2(images=images, transforms=transforms, output_shape=output_shape, interpolation=interpolation, fill_mode=fill_mode)

```

Patches

We have patched the issue in GitHub commit [8faa6ea692985dbe6ce10e1a3168e0bd60a723ba](https://github.com/tensorflow/tensorflow/commit/8faa6ea692985dbe6ce10e1a3168e0bd60a723ba).

The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, 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 Neophytos Christou from the Secure Systems Lab (SSL) at Brown University.

How this vulnerability can be exploited

This issue can be reached over the network, attack complexity is high, an attacker needs low-level privileges on the target. A user must be tricked into taking some action. 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:L/UI:R/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: Low — an ordinary user account is enough.
  • User interaction: Required — someone has to click, open or visit something.
  • 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-2022-41886 is classified as CWE-131: Incorrect Buffer Size Calculation. The size computed for an allocation does not match what is later written into it, producing an overflow.

Affected software

CVE-2022-41886 is recorded against 3 packages.

  • tensorflow (from 2.10.0 up to 2.10.1)
  • tensorflow-cpu (from 2.10.0 up to 2.10.1)
  • tensorflow-gpu (from 2.10.0 up to 2.10.1)

Timeline and source

Published on 13 July 2026. A public exploit is known to exist, which raises the urgency of patching considerably. 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-131: Incorrect Buffer Size Calculation) in other software:

Details

Severity HIGH
CVSS Score 8.0
CVSS Vector CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:N/A:H
CWE CWE-131
Public Exploit ⚠️ Yes
Source OSV
Published 2026-07-13
Updated 2026-08-20
Modified 2026-07-13

Affected Packages

Software From version Fixed in
tensorflow 2.10.0 2.10.1
tensorflow-cpu 2.10.0 2.10.1
tensorflow-gpu 2.10.0 2.10.1

Similar Threats

Exploit Protection

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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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