HighVulnerability

CVE-2025-12058: Keras load_model arbitrary local file read and SSRF via StringLookup vocabulary

Source: NVD(opens in a new tab)
Identifier: CVE-2025-12058
Published: October 29, 2025
Record updated: October 9, 2026

Affected: keras < 3.12.0
Fixed in: 3.12.0
released October 27, 2025, 2 days before disclosure
Known exploitation: Not listed in the CISA Known Exploited Vulnerabilities catalog at the last check.
EPSS: 0.2%

Summary

CVE-2025-12058 affects the Keras.Model.load_model method, including when called with safe_mode=True. Loading a specially crafted .keras archive with a malicious vocabulary path in the StringLookup layer's configuration lets an attacker read arbitrary local files, retrievable via get_vocabulary(). Because Keras uses tf.io.gfile, which supports remote filesystems and HTTP/HTTPS, the same path can also trigger server-side requests to arbitrary network endpoints. The safe_mode=True flag did not restrict this external path loading.

Mitigation

The source does not state a fix yet. Check the original advisory for updates.

Details

Scores and exploitation

Patch available: Yes
EPSS: 0.2%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-502
Attack pattern (CAPEC): CAPEC-586

Affected

Packages: keras@< 3.12.0 (fixed: 3.12.0)
Vendors: Google

Classification

Attack type: Other
Attack sophistication: Moderate
AI component targeted: Framework
Impact: Confidentiality
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-2025-12058

First tracked: February 15, 2026 at 08:42 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.

kerasPyPINo LLM dependency detected · 6 tracked dependents

Summary

Mitigation

Affected packages in the Exposure Registry

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