Pith. sign in

REVIEW 2 cited by

Contextual Agent Security: A Policy for Every Purpose

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2501.17070 v3 pith:RN7GRK3P submitted 2025-01-28 cs.CR cs.CLcs.LG

classification cs.CRcs.CLcs.LG
keywords securitycontextualactionagentscontextsemailagentlimited
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DualView: Preventing Indirect Prompt Injection in Personal AI Agents

    cs.CR 2026-07 conditional novelty 7.0 of 10

    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.

  2. LLM Agents Should Employ Security Principles

    cs.CR 2025-05 conditional novelty 5.0 of 10

    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.

Pith tools