Operational Hallucination and Safety Drift in AI Agents
Large language models (LLMs) serving as planners in tool-using autonomous agents introduce dynamic reliability risks in multi-turn execution. While single-turn safety mechanisms are relatively mature, extended interactions reveal structural vulnerabilities where initial alignment degrades over time. This paper empirically characterizes two observed failure modes across multiple state-of-the-art LLMs: Safety Drift, the gradual erosion of declared safety intent leading to constraint-violating acti
Researchers identify Safety Drift and Operational Hallucination in large language models, where initial alignment degrades over time, leading to constraint-violating actions. These phenomena are observed across multiple state-of-the-art models, attributed to decoupling of reasoning context from execution state. An Action-Aware Supervision Layer is proposed to intercept violations.
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