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Contextual Agent Security: A Policy for Every Purpose
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Judging an action's safety requires knowledge of the context in which the action takes place. To human agents who act in various contexts, this may seem obvious: performing an action such as email deletion may or may not be appropriate depending on the email's content, the goal (e.g., to erase sensitive emails or to clean up trash), and the type of email address (e.g., work or personal). Unlike people, computational systems have often had only limited agency in limited contexts. Thus, manually crafted policies and user confirmation (e.g., smartphone app permissions or network access control lists), while imperfect, have sufficed to restrict harmful actions. However, with the upcoming deployment of generalist agents that support a multitude of tasks (e.g., an automated personal assistant), we argue that we must rethink security designs to adapt to the scale of contexts and capabilities of these systems. As a first step, this paper explores contextual security in the domain of agents and proposes contextual agent security (Conseca), a framework to generate just-in-time, contextual, and human-verifiable security policies.
Forward citations
Cited by 2 Pith papers
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DualView: Preventing Indirect Prompt Injection in Personal AI Agents
DualView extends Dual-LLM symbol isolation into the shared user environment via dual Agent/Human views, blocking both immediate and stored IPI at 0% ASR while preserving near-baseline utility.
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LLM Agents Should Employ Security Principles
A position paper proposing AgentSandbox, a framework that applies Saltzer-Schroeder security principles to LLM agents and reports large attack-success-rate reductions on AgentDojo.
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