HighVulnerability

CVE-2026-1462: Keras TFSMLayer loads attacker-controlled SavedModels despite safe_mode

Source: NVD(opens in a new tab)
Identifier: CVE-2026-1462
Published: April 13, 2026
Record updated: October 9, 2026

Affected: keras < 3.13.2
Fixed in: 3.13.2
Known exploitation: Not listed in the CISA Known Exploited Vulnerabilities catalog at the last check.
EPSS: 0.4%

Summary

CVE-2026-1462 affects the `TFSMLayer` class in the `keras` package, version 3.13.0. Attacker-controlled TensorFlow SavedModels load during deserialization of `.keras` models even when `safe_mode=True`, bypassing that protection. The result is arbitrary code execution during model inference under the victim's privileges, caused by unconditional loading of external SavedModels, serialization of attacker-controlled file paths, and missing validation in `from_config()`.

Mitigation

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

Details

Scores and exploitation

Patch available: Yes
EPSS: 0.4%Estimated probability that this CVE is exploited in the next 30 days, from the FIRST Exploit Prediction Scoring System. Read on October 15, 2026.
Disclosure date: April 13, 2026

Weaknesses and techniques

Weakness (CWE): CWE-502
Attack pattern (CAPEC): CAPEC-586
AI threat technique (MITRE ATLAS): AML.T0010

Affected

Packages: keras@< 3.13.2 (fixed: 3.13.2)
Vendors: Google

Classification

Attack type: Supply chain,
Model evasion
Attack sophistication: Moderate
AI component targeted: Framework
Impact: Confidentiality,
Integrity,
Safety
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-2026-1462

First tracked: April 13, 2026 at 02:07 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.

kerasPyPINo LLM dependency detected · 6 tracked dependents

Summary

Mitigation

Affected packages in the Exposure Registry

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