SECSEC-001 — Model Security Vulnerabilities
Novel Attack Vectors Exploit LLM APIs and Training Pipelines
4/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
Adversaries are exploiting large language models through prompt abstraction, backdoored reward models, and AI-generated adversarial samples to undermine system integrity and circumvent cost controls. Boards face material risk of compromised AI outputs, eroded model trust, and regulatory exposure where AI systems underpin financial or operational decisions.
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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