Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluation

Large language model (LLM) agents are rapidly moving from conversational interfaces to software components that plan, invoke tools, maintain memory, and act on external environments. This transition changes the nature of security risk. In agentic settings, failures are no longer limited to unsafe text generation. Untrusted content may redirect control flow, misuse tool privileges, corrupt persistent state, leak sensitive information, or trigger harmful external actions. At the same time, researc

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ethics.ai (9 June 2026), “Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluation,” evidence record 1201, https://ethics.ai/record/1201 (originally published by arXiv).

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