arXiv AI
Beyond OAuth: Task-Scoped Authorization for AI Agents via Natural Language Slices
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arXiv AI
Sep 23
ActGov: Governing LLM Agent Actions via Policy-Constrained Validation
ActGov is a runtime enforcement framework that validates each action proposed by a large language model (LLM) agent before it interacts with external tools, ensuring that actions stay within task‑scoped authorization boundaries and comply with dynamically constructed policies. It builds policies from tool specifications, benign tasks, and failure traces, verifying updates via SMT‑based counterexample checking. In evaluations on AgentDojo and AgentDyn benchmarks, ActGov consistently reduces indirect prompt‑injection attack success while maintaining task utility, outperforming existing defenses.
Hugging Face Trending Papers
Sep 21
ActGov: Governing LLM Agent Actions via Policy-Constrained Validation
Large language model (LLM) agents increasingly execute long-horizon workflows through external tools, allowing untrusted outputs to influence subsequent actions and exceed user authorization. Existing...
arXiv AI
Sep 2
Delegation Without Trust: An Empirical Gap Analysis of Identity, Authorization, and Runtime Governance in Multi-Agent LLM Systems
The paper examines the security challenges of delegating authority to autonomous LLM agents that act on users’ behalf. It introduces a threat model with four adversaries and eight security requirements, demonstrates that current frameworks (LangGraph, CrewAI, AutoGen, MCP) fail to meet these standards, and presents an authorization broker that blocks all identified threats with minimal overhead. The broker is shown to resist numerous attacks and limits compromised sub‑agents to their delegated tasks, and its principles are implemented in VotalAI’s LLM Shield.
arXiv AI
Oct 7
MiniScope: Authorizing Agents with Least-Privilege Permissions
arXiv:2512.11147v2 Announce Type: replace-cross Abstract: AI agents are increasingly granted autonomous access to sensitive user data and third-party services, making effective permission management...
arXiv AI
2d ago
SUDP: Secret-Use Delegation Protocol for Agentic Systems
The paper introduces the Secret-Use Delegation Protocol (SUDP), a method for agentic systems to perform user-authorized operations without granting reusable authority to untrusted requesters. It formalizes the Agent Secret Use (ASU) problem, outlines seven required security properties, and demonstrates that SUDP satisfies six of them under standard cryptographic assumptions, with the remaining property addressed via hardware-rooted runtime composition. A reference implementation shows minimal overhead for mediated calls.
arXiv AI
4d ago
Act on Your Behalf, Beyond Your Authorization: Benchmarking LLM Agent Authorization Alignment
The paper introduces AuthorizationBench, a benchmark designed to evaluate whether large language model (LLM) agents act within the scope of user authorization when acting on their behalf. The benchmark comprises 286 executable tasks across ten application domains, covering situational triggers, presence or absence of direct harmful consequences, and varying reasoning effort. In experiments with four models and multiple agent harnesses, agents violated authorization in 19.2% of trajectories, with rates ranging from 10.5% to 26.6% across configurations, and increased reasoning effort did not substantially reduce these violations.