CVE-2025-9906 & CVE-2025-9905 — Keras Library Vulnerabilities Arbitrary Code Execution in AI/ML Framework Vulnerability Analysis Report

Author: CyberDudeBivash · Powered by: CyberDudeBivash

Executive Summary

Two serious vulnerabilities were disclosed in the Keras library, widely used in deep learning workflows.

CVE-2025-9906: CVSS 8.6 (High)
CVE-2025-9905: CVSS 7.3 (High)

Both issues allow arbitrary code execution (ACE), which could be weaponized in supply-chain attacks, malicious model distribution, or unsafe deserialization of model files. Since Keras underpins many AI/ML production pipelines, the impact radius is vast — from research environments to enterprise ML deployments.

Technical Details

CVE-2025-9906 (CVSS 8.6)

Type: Deserialization / unsafe model parsing flaw.
Impact: Maliciously crafted model files (.h5 / TensorFlow SavedModel) can trigger execution of arbitrary code when loaded.
Attack Scenario: An attacker uploads or distributes a tainted model (e.g., via GitHub, Hugging Face, PyPI) → victim loads it into Keras → embedded payload executes.
Severity Justification: High (8.6) because exploitation requires crafted input but leads to full compromise of ML host.

CVE-2025-9905 (CVSS 7.3)

Type: Input validation flaw in preprocessing utilities.
Impact: Under certain conditions, hostile inputs (images, JSON configs, or serialized weight files) cause Keras functions to execute unintended code paths.
Attack Scenario: Malicious dataset/model metadata used in pipelines (e.g., CI/CD for ML ops) → triggers RCE during training or inference setup.
Severity Justification: Medium-High (7.3) — requires malicious input file or supply-chain poisoning.

Exploitation Risks

Supply Chain Poisoning: Malicious models on public repositories can infect enterprise environments.
CI/CD Attack Surface: Automated retraining workflows that pull community models are especially at risk.
Cloud ML Platforms: Shared GPU/TPU environments may be abused as stepping stones for lateral movement.
Data Exfiltration: Attackers can run arbitrary Python code to harvest credentials, data, or inject persistence.

Detection & Indicators

Unexpected system calls during keras.models.load_model() execution.
Presence of pickled objects / suspicious lambdas in model files.
ML pipelines spawning child processes not normally used by training jobs.
Integrity mismatches in downloaded models (hash checks failing).

Immediate Mitigations

Upgrade to the patched version of Keras (check PyPI / GitHub releases).
Verify model integrity — only load models from trusted sources; validate SHA256 hashes.
Sandbox risky operations — run model ingestion in restricted containers.
Disable auto-execution features — avoid eval() or pickle-based deserialization in untrusted contexts.
Code reviews — audit ML pipeline code for unsafe load practices.

Longer-Term Recommendations

Secure MLOps: Enforce model signing and verification (e.g., Sigstore, cosign).
Policy Enforcement: Treat ML artifacts like binaries — with vulnerability scanning and provenance checks.
Zero-Trust ML: Assume third-party datasets/models are malicious until validated.
Continuous Threat Hunting: Monitor ML workloads for anomalies in system resource usage.

CyberDudeBivash Action Checklist

Patch Keras to latest release across all environments.
Audit ML repos for untrusted .h5 / SavedModel files.
Enforce SHA256 signature verification for every model load.
Run risky ML jobs in containerized sandboxes with restricted privileges.
Monitor for suspicious process execution from ML training pipelines.
Educate data scientists & engineers about malicious model supply-chain threats.

Conclusion

CVE-2025-9906 and CVE-2025-9905 highlight how AI/ML frameworks are becoming prime cyber targets. Exploiting Keras vulnerabilities offers attackers direct execution inside GPU/TPU-equipped environments, often with privileged access. Organizations must patch quickly, enforce model provenance controls, and integrate security into MLOps pipelines.

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