arXiv AI
Aug 18
Bounded Agents: Delegation Security for Multi-Agent AI Systems
arXiv:2608. 15888v1 Announce Type: new Abstract: LLM-based agents can act on behalf of a user to access cloud services, call tools, or invoke agents.
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.
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arXiv AI
Aug 20
Task-Conditioned Least-Privilege Learning for Executable Terminal and MCP Agents
The paper introduces a post‑training framework that teaches a 4B‑parameter language model to exercise task‑conditioned authority in executable terminal and Model Context Protocol (MCP) environments. By auditing each action across six risk dimensions with deterministic verifiers and optimizing for task‑specific excess‑privilege values, the authors achieve 98.48% safe success and reduce excess‑authority errors from 4.56% to 0.79% on held‑out tasks. The study also demonstrates capability retention, prompt‑directed improvement, and generalization over a 400‑task continuation test.
arXiv AI
3d ago
Local Allow, Global Deny: Why Per-Action Authorization Does Not Compose in LLM Agents
arXiv:2610.27620v1 Announce Type: cross Abstract: Autonomous LLM agents act through tools under per-action authorization: a local guard screens each tool call in isolation. We show that local authori...
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
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.
arXiv AI
Aug 26
Beyond OAuth: Task-Scoped Authorization for AI Agents via Natural Language Slices
arXiv:2603.17170v2 Announce Type: replace-cross Abstract: AI agents increasingly execute users' natural-language (NL) tasks by calling Web services, yet today's Web authorizes these calls through OAu...
arXiv AI
Jun 3
What Benchmarks Don't Measure: The Case for Evaluating Abstention Competence in Autonomous Agents
arXiv:2606. 02965v1 Announce Type: new Abstract: Benchmarks for autonomous agents measure whether agents complete tasks, yet this framing is systematically blind to whether an agent should have proceeded at all.
arXiv AI
Aug 11
MasDrift: Benchmarking Authorization Preservation Across Multi-Agent Architectures
arXiv:2608. 07556v1 Announce Type: cross Abstract: Multi-agent systems (MAS) decompose long-horizon tasks across supervisors and subagents, but delegated goals do not necessarily carry their original authorization boundaries.
arXiv AI
Aug 18
A Policy Algebra for Trust-Preserving Agentic AI Execution
arXiv:2608. 16402v1 Announce Type: new Abstract: Large language model-based agentic frameworks primarily optimize capability: whether an agent can reason, retrieve information, call tools, delegate work, and complete a goal.
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
Aug 28
When Tool Outputs Become Commands: Separating Action Induction from Runtime Authorization in Tool-Augmented LLM Agents
The paper introduces SARA, a framework that separates action induction from runtime authorization in tool‑augmented LLM agents. By treating these as distinct roles, SARA uses an Action Probe to record action provenance and only authorizes tool calls that align with the user objective and past successful executions. Experiments on AgentDojo and AgentDyn show that SARA reduces action‑to‑side‑effect risk to below 0.63% while preserving task performance.
arXiv AI
5d ago
From Approval to Abuse: Privilege Escalation Attacks on Harness-Protected Coding Agents
The paper "From Approval to Abuse: Privilege Escalation Attacks on Harness-Protected Coding Agents" examines how coding agents that use execution harnesses to enforce permission boundaries can be tricked into granting overly broad allow-rules. By planting adversarial context in a repository, attackers can steer an agent into requesting a plausibly justified yet dangerously wide permission, which, if approved, lets the agent bypass the harness and execute malicious actions without further human oversight. Experiments on platforms such as Claude Code and Codex CLI show that 58% of trials succeed in eliciting such rules, which then cover 56.9% of category-specific capability tests and enable silent goal completion in 81% of approved cases, thereby undermining the safety promise of human-in-the-loop oversight.