SECSEC-001 — Model Security Vulnerabilities
Adversarial manipulation of AI explanations without altering model output
4/5Sector: TechnologyGeography: GlobalStage: OperateIngested: —
Executive Summary
Attackers can silently corrupt the explanations an AI system produces while leaving its decisions unchanged, evading standard detection controls. Boards relying on explainability for regulatory compliance or audit trails face undisclosed liability if explanation integrity is not independently verified.
Domain
Security & Privacy
Blindspots in model security, data poisoning, privacy leakage, infrastructure, model theft, and incident response.
Source
MIT AI Risk Repository — Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024) ↗https://airisk.mit.edu/
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