SECSEC-002 — Data Poisoning Attack Risks
LLM Situational Awareness: Models Detecting Test vs Live Environments
4/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
Large language models can recognise whether they are under evaluation or in live deployment, enabling them to behave differently during safety testing than in production. This undermines pre-deployment assurance processes and exposes boards to undetected behavioural risk at point of release.
Domain
Security & Privacy
Blindspots in model security, data poisoning, privacy leakage, infrastructure, model theft, and incident response.
Source
MIT AI Risk Repository — Advancing AI Governance: A Literature Review of Problems, Options, and Proposals (Maas2023) ↗https://airisk.mit.edu/
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