SECSEC-001 — Model Security Vulnerabilities
LLM Safety Filters Bypassed via Simple Prompt Manipulation Techniques
4/5Sector: TechnologyGeography: GlobalStage: OperateIngested: —
Executive Summary
Large language models can be induced to produce harmful outputs through straightforward prompt modifications including role-play, obfuscation, and code integration, requiring no specialist knowledge. Organisations deploying LLMs face material reputational and regulatory exposure if input and output controls are not validated against these well-documented attack vectors.
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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