Research / Sources
AI research and discussion on LessWrong. www.lesswrong.com
A conceptual taxonomy distinguishing rogue AI activity types (breaking in, breaking out, full self-exfiltration, insider infiltration) using the 2026 Hugging Face swarm incident as a running example, with implications for containment and monitoring.
Proposes using information-theoretically secure secure multi-party computation instead of homomorphic encryption to sandbox arbitrarily dangerous AI, so that no party learns the program's contents, and explains the secret-sharing construction.
Proposes layered honeypot services, a threat barometer, and shadowban responses to detect rogue LLM agents operating in the wild, while discussing their limits against capable agents.
Barak argues that AI safety needs robust reasonable compliance with detailed specifications, defense in depth, and detection over prevention, and that it won't be solved by an AI scientist or by making AI love humanity.