An intent-aware security defense framework for AI agents
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An arXiv paper proposes the Intent-Aware Security framework, which treats an agent's trusted deployment intent as a first-class security object and defines intent preservation and intent drift. The authors point out that actions that contain no attack pattern and fall within the authorized scope may still violate role or organizational constraints, and existing defenses do not judge whether an action is faithful to the deployment purpose. The study constructs a benchmark of 887 validated samples covering 35 agents and proposes four defenses.
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- Intent-Aware Protection for AI Agents
arXiv · 2026-11-12 · Deep reading
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- arXiv: Jailbreaks, Prompt Injection, Poisoning & DefensesIntent-aware defenses for AI agents
The paper proposes the Intent-Aware Security framework, which treats an agent's trusted deployment intent as a first-class security object, defines intent preservation and intent drift, and extracts an Intent Profile from organizational, role, and developer intent. The authors point out that actions that contain no attack pattern and are within the authorized scope may still violate role or organizational constraints, while existing defenses mainly detect prompt injection, harmful outputs, or suspicious behavior and do not judge whether an action is faithful to the deployment purpose. The study constructs a benchmark of 887 validation samples covering 35 agents and proposes four defenses: pre-execution detection, post-execution detection, input alignment, and system prompt hardening.
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