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

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ethics.ai (20 July 2026), “Operational Hallucination and Safety Drift in AI Agents,” evidence record 12362, https://ethics.ai/record/12362 (originally published by arXiv).

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