Same Dangerous Objective, Opposite Advice: Direct Exposure versus Multi-Agent Mediation

Multi-agent mediation can reverse LLM safety refusals: compositional safety gap demonstrated with GPT-5.6

A new arXiv paper demonstrates that a high-capability LLM (GPT-5.6) can appear safer when exposed directly to a dangerous objective than when that objective is transformed and relayed through a multi-agent pipeline. In the tested setup, an 'Id' and 'Censor' agent rewrite a manipulative instruction before passing it to a 'Superego' user-facing agent, which then produces advice aligned with the manipulative target despite never seeing the raw objective or its provenance. The authors identify this as a compositional safety gap: multi-stage automated workflows can launder dangerous instructions past safety mechanisms by keeping raw instructions outside the downstream model's context window. The finding has direct implications for agentic deployment architectures where end users and safety evaluators only have endpoint-level visibility.