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🛡️ CVE-2022-23563 — tensorflow

🟠 CVSS 8.0 — High ✅ No Known Exploit CWE-367 OSV
8.0
CVSS Score
0 Low4 Medium7 High9 Critical10

Description

Insecure temporary file in Tensorflow

Impact

In multiple places, TensorFlow uses tempfile.mktemp to create temporary files. While this is acceptable in testing, in utilities and libraries it is dangerous as a different process can create the file between the check for the filename in mktemp and the actual creation of the file by a subsequent operation (a TOC/TOU type of weakness).

In several instances, TensorFlow was supposed to actually create a temporary directory instead of a file. This logic bug is hidden away by the mktemp function usage.

Patches

We have patched the issue in several commits, replacing mktemp with the safer mkstemp/mkdtemp functions, according to the usage pattern.

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 on huntr.dev for one scenario and discovered via variant analysis on other instances.

How this vulnerability can be exploited

This issue can be reached with local access to the system, 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 none.

CVSS metrics in full

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

  • Attack vector: Local — a local account, shell or session on the host is needed.
  • 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: None.

Weakness class

CVE-2022-23563 is classified as CWE-367: Time-of-check Time-of-use Race Condition. The state checked and the state acted upon can differ, because it may change between the two steps.

Affected software

CVE-2022-23563 is recorded against 3 packages.

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

Timeline and source

Published on 9 July 2026 and last revised on 13 July 2026. No public exploit is currently recorded for this entry. Record sourced from OSV.

References

github.com (Web)
nvd.nist.gov (Advisory)
github.com (Web)
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-367: Time-of-check Time-of-use Race Condition) in other software:

Details

Severity HIGH
CVSS Score 8.0
CVSS Vector CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:N/SC:N/SI:N/SA:N
CWE CWE-367
Public Exploit ✅ No
Source OSV
Published 2026-07-09
Updated 2026-08-20
Modified 2026-07-13
Fix URL N/A

Affected Packages

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

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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