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🛡️ CVE-2023-25668 — tensorflow

🔴 CVSS 9.5 — Critical ⚠️ Exploit Public CWE-122 OSV
9.5
CVSS Score
0 Low4 Medium7 High9 Critical10

Description

TensorFlow has a heap out-of-buffer read vulnerability in the QuantizeAndDequantize operation

Impact

Attackers using Tensorflow can exploit the vulnerability. They can access heap memory which is not in the control of user, leading to a crash or RCE.

When axis is larger than the dim of input, c->Dim(input,axis) goes out of bound.

Same problem occurs in the QuantizeAndDequantizeV2/V3/V4/V4Grad operations too.

```python

import tensorflow as tf

@tf.function

def test():

tf.raw_ops.QuantizeAndDequantizeV2(input=[2.5],

input_min=[1.0],

input_max=[10.0],

signed_input=True,

num_bits=1,

range_given=True,

round_mode='HALF_TO_EVEN',

narrow_range=True,

axis=0x7fffffff)

test()

```

Patches

We have patched the issue in GitHub commit [7b174a0f2e40ff3f3aa957aecddfd5aaae35eccb](https://github.com/tensorflow/tensorflow/commit/7b174a0f2e40ff3f3aa957aecddfd5aaae35eccb).

The fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1

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.

How this vulnerability can be exploited

This issue can be reached over the network, attack complexity is low, 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 high, integrity high, availability high.

CVSS metrics in full

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

  • Attack vector: Network — reachable from anywhere that can route to the service.
  • Attack complexity: Low — the attack works reliably, with no preparation.
  • 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: High — total loss, or loss the attacker controls.
  • Integrity impact: High — total loss, or loss the attacker controls.
  • Availability impact: High — total loss, or loss the attacker controls.

Weakness class

CVE-2023-25668 is classified as CWE-122: Heap-based Buffer Overflow. A write past the end of a heap allocation corrupts allocator metadata or neighbouring objects.

Affected software

CVE-2023-25668 is recorded against 3 packages.

  • tensorflow (fixed in 2.11.1)
  • tensorflow-cpu (fixed in 2.11.1)
  • tensorflow-gpu (fixed in 2.11.1)

Timeline and source

Published on 29 June 2026 and last revised on 1 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)
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-122: Heap-based Buffer Overflow) in other software:

Details

Severity CRITICAL
CVSS Score 9.5
CVSS Vector CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
CWE CWE-122
Public Exploit ⚠️ Yes
Source OSV
Published 2026-06-29
Updated 2026-08-20
Modified 2026-07-01

Affected Packages

Software From version Fixed in
tensorflow — 2.11.1
tensorflow-cpu — 2.11.1
tensorflow-gpu — 2.11.1

Similar Threats

Exploit Protection

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CVE-2023-25668 carries CVSS 9.5 Critical rating and a public exploit already exists. BotEraser checks your installation against this and other known CVE records, and blocks IPs associated with exploit activity.

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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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