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🛡️ CVE-2022-23561 — tensorflow

🟠 CVSS 8.0 — High ✅ No Known Exploit CWE-787 OSV
8.0
CVSS Score
0 Low4 Medium7 High9 Critical10

Description

Out of bounds write in TFLite

Impact

An attacker can craft a TFLite model that would cause a write outside of bounds of an array in TFLite. In fact, the attacker can override the linked list used by the memory allocator. This can be leveraged for an arbitrary write primitive under certain conditions.

Patches

We have patched the issue in GitHub commit [6c0b2b70eeee588591680f5b7d5d38175fd7cdf6](https://github.com/tensorflow/tensorflow/commit/6c0b2b70eeee588591680f5b7d5d38175fd7cdf6).

The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Wang Xuan of Qihoo 360 AIVul Team.

How this vulnerability can be exploited

This issue can be reached over the network, attack complexity is low, an attacker needs low-level privileges on the target. No user interaction is required. Rated impact: confidentiality high, integrity high, availability high.

CVSS metrics in full

The score comes from this vector: CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

  • Attack vector: Network — reachable from anywhere that can route to the service.
  • Attack complexity: Low — the attack works reliably, with no preparation.
  • Attack requirements: None — no deployment-specific condition has to hold.
  • Privileges required: Low — an ordinary user account is enough.
  • User interaction: None — nobody has to be tricked into anything.
  • 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-2022-23561 is classified as CWE-787: Out-of-bounds Write. Data is written past the end or before the start of a buffer, corrupting whatever is stored there.

Affected software

CVE-2022-23561 is recorded against 3 packages.

  • tensorflow (from 2.7.0 up to 2.7.1)
  • tensorflow-cpu (from 2.7.0 up to 2.7.1)
  • tensorflow-gpu (from 2.6.0 up to 2.6.3)

Timeline and source

Published on 9 July 2026 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)
nvd.nist.gov (Advisory)
github.com (Web)
github.com (Web)
github.com (Web)
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-787: Out-of-bounds Write) in other software:

Details

Severity HIGH
CVSS Score 8.0
CVSS Vector CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N
CWE CWE-787
Public Exploit ✅ No
Source OSV
Published 2026-07-09
Updated 2026-08-20
Modified 2026-07-13

Affected Packages

Software From version Fixed in
tensorflow 2.7.0 2.7.1
tensorflow-cpu 2.7.0 2.7.1
tensorflow-gpu 2.6.0 2.6.3

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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