MediumVulnerability

CVE-2023-25667: TensorFlow integer overflow in frame, height, width and channel calculation

Source: NVD(opens in a new tab)
Identifier: CVE-2023-25667
Published: March 25, 2023
Record updated: October 9, 2026

Affected: tensorflow < 2.11.1
tensorflow-cpu < 2.11.1
tensorflow-gpu < 2.11.1
Fixed in: 2.11.1
tensorflow 2.11.1 released March 20, 2023, 5 days before disclosure
tensorflow-cpu 2.11.1 released March 20, 2023, 5 days before disclosure
Known exploitation: Not listed in the CISA Known Exploited Vulnerabilities catalog at the last check.
EPSS: 0.3%

Summary

CVE-2023-25667 affects TensorFlow, an open source machine learning platform, in versions prior to 2.12.0 and 2.11.1. An integer overflow occurs when 2^31 <= num_frames * height * width * channels < 2^32, for example in a Full HD screencast of at least 346 frames. The weakness is classified as CWE-190, Integer Overflow or Wraparound.

Mitigation

A fix is included in TensorFlow version 2.12.0 and version 2.11.1.

Details

Scores and exploitation

CVSS score: 6.5(medium)
Patch available: Yes
EPSS: 0.3%Estimated probability that this CVE is exploited in the next 30 days, from the FIRST Exploit Prediction Scoring System. Read on November 4, 2026.

Weaknesses and techniques

Weakness (CWE): CWE-190

Affected

Packages: tensorflow@< 2.11.1 (fixed: 2.11.1)
tensorflow-cpu@< 2.11.1 (fixed: 2.11.1)
tensorflow-gpu@< 2.11.1 (fixed: 2.11.1)
Vendors: Google

Classification

Attack type: Denial of service
Attack sophistication: Moderate
AI component targeted: Framework
Impact: Integrity,
Availability
Labels: security

How this record was produced

Summaries, mitigations and labels on this page are generated automatically from the source. The linked source is authoritative. The source gives this advisory a description and no title, so the title above is a headline generated from the source text.

Original source: https://nvd.nist.gov/vuln/detail/CVE-2023-25667

First tracked: February 15, 2026 at 08:41 PM ET, from NVD/CVE Database

Classifier: claude-haiku-5-5, prompt v4

Classifier confidence: 90% (self-assessed)

Affected packages in the Exposure Registry

Matched by package name and ecosystem. Each entry shows whether the package delegates to a language model and how many tracked packages depend on it.

tensorflowPyPINo LLM dependency detected · 26 tracked dependents
tensorflow-gpuPyPINo LLM dependency detected · 2 tracked dependents

Summary

Mitigation

Affected packages in the Exposure Registry

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