On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models

Large Language Models (LLMs) have undergone a shift from stateless conversational interfaces to autonomous agents capable of multi-step planning, tool invocation, code execution, and maintaining persistent memory. When these agents operate with real-world privileges---calling APIs, modifying files, and querying databases---a compromised reasoning step can trigger unauthorized data access, irreversible state changes, or cascading failures, yet the security research community has not kept pace. To

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ethics.ai (11 August 2026), “On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models,” evidence record 18689, https://ethics.ai/record/18689 (originally published by arXiv red teaming query).

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