SECSEC-001 — Model Security Vulnerabilities
Adversarial Prompt Attacks Elicit Unintended LLM Behaviour
4/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
Deliberately engineered inputs can manipulate large language models into producing harmful or unauthorised outputs, bypassing intended safeguards. Boards face regulatory exposure and operational liability where such vulnerabilities exist in client-facing or decision-support systems.
Domain
Security & Privacy
Blindspots in model security, data poisoning, privacy leakage, infrastructure, model theft, and incident response.
Source
MIT AI Risk Repository — Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024) ↗https://airisk.mit.edu/
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