arXiv AI
MiniScope: Authorizing Agents with Least-Privilege Permissions
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Jul 3
Janus: a Playground for User-Involved Agentic Permission Management
arXiv:2607. 01510v1 Announce Type: new Abstract: AI agents that autonomously execute tool calls on a user's behalf raise pressing questions about permission management: what role could users play, and what role should they play?
arXiv AI
Jul 16
How Agents Ask for Permission: User Permissions for AI Agents, from Interfaces to Enforcement
arXiv:2607. 13718v1 Announce Type: cross Abstract: As AI agents gain prevalance, users are increasingly exposed to the risks such systems entail.
arXiv AI
Sep 15
Empirical Evaluation of Task-Based Permission Scoping Architecture for AI Agents
The paper evaluates a task-based permission scoping architecture for AI agents, comparing a fine‑tuned RoBERTa‑large encoder to few‑shot Claude Haiku 4.5 on a 600‑prompt dataset. It shows the new system achieves comparable macro‑F1 (0.881 vs. 0.886) and higher precision (0.897 vs. 0.842), while reducing severity‑weighted residual risk from 1.12 to 0.63. The study also introduces an attack‑surface elimination metric, demonstrating that task‑granular control can close 84.4% of the severity‑weighted surface, far surpassing role‑based ceilings alone.
arXiv AI
Sep 1
AgenTRIM: Tool Risk Mitigation for Agentic AI
arXiv:2601.12449v2 Announce Type: replace-cross Abstract: AI agents are autonomous systems that combine LLMs with external tools to solve complex tasks. While such tools extend capability, improper t...
arXiv AI
Sep 25
Progressive Skill Discovery as Access Control for Tool-Using LLM Agents: Structural Governance through Role-Scoped Capability Delivery
The paper introduces skilder, a framework that organizes LLM agent capabilities into role‑scoped bundles of skills, tools, and instructions, with explicit limits. Agents start with a minimal role catalog, discover the roles needed for a task, and receive the associated tools only through a single MCP server, ensuring deterministic enforcement of scope. Experiments on 13 tasks with six models show that skilder’s authorization layer prevents unauthorized tool calls and parameter violations while maintaining flexibility through dynamic cross‑role capability acquisition.
arXiv AI
Jun 19
When Lower Privileges Suffice: Investigating Over-Privileged Tool Selection in LLM Agents
arXiv:2606. 20023v1 Announce Type: cross Abstract: As LLM agents increasingly select tools autonomously, their choices among tools with different privileges become safety-relevant.