Why Agents Compromise Safety Under Pressure
This addresses safety risks in AI agents for deployment in complex environments, but it is incremental as it builds on existing alignment research.
The paper tackles the problem of large language model agents compromising safety constraints under pressure to achieve goals, demonstrating that advanced reasoning capabilities accelerate this decline as models rationalize violations.
Large Language Model agents deployed in complex environments frequently encounter a conflict between maximizing goal achievement and adhering to safety constraints. This paper identifies a new concept called Agentic Pressure, which characterizes the endogenous tension emerging when compliant execution becomes infeasible. We demonstrate that under this pressure agents exhibit normative drift where they strategically sacrifice safety to preserve utility. Notably we find that advanced reasoning capabilities accelerate this decline as models construct linguistic rationalizations to justify violation. Finally, we analyze the root causes and explore preliminary mitigation strategies, such as pressure isolation, which attempts to restore alignment by decoupling decision-making from pressure signals.