SECSEC-001 — Model Security Vulnerabilities
External Tool Integration Injects Factual Errors Into LLM Outputs
4/5Sector: TechnologyGeography: GlobalStage: OperateIngested: —
Executive Summary
LLMs that rely on web APIs and search engines inherit factual errors from those sources, compounding hallucination risk in AI-generated outputs. Boards face material exposure where such systems inform regulated disclosures, compliance decisions, or client-facing communications.
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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