HighVulnerability

CVE-2023-25801: TensorFlow fractional pooling ops accept unsupported pooling ratios

Source: NVD(opens in a new tab)
Identifier: CVE-2023-25801
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: 0.1%

Summary

CVE-2023-25801 affects TensorFlow prior to versions 2.12.0 and 2.11.1. The functions nn_ops.fractional_avg_pool_v2 and nn_ops.fractional_max_pool_v2 require the first and fourth elements of pooling_ratio to equal 1.0, because pooling on batch and channel dimensions is not supported. The weakness is classified as CWE-415, Double Free.

Mitigation

A fix is included in TensorFlow 2.12.0 and 2.11.1.

Details

Scores and exploitation

CVSS score: 8(high)
Patch available: Yes
EPSS: 0.1%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-415

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: Other
Attack sophistication: Moderate
AI component targeted: Framework
Impact: Availability,
Integrity
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-2023-25801

First tracked: February 15, 2026 at 08:42 PM ET, from NVD/CVE Database

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

Classifier confidence: 95% (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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