Before the Tool Call: Deterministic Pre-Action Authorization for Autonomous AI Agents
- URL: http://arxiv.org/abs/2603.20953v1
- Date: Sat, 21 Mar 2026 21:34:09 GMT
- Title: Before the Tool Call: Deterministic Pre-Action Authorization for Autonomous AI Agents
- Abstract summary: Open Agent Passport (OAP) is an open specification and reference implementation that intercepts tool calls synchronously before execution.<n>OAP enforces authorization decisions in a measured median of 53 ms (N=1,000)<n>The same infrastructure that enforces security constraints also enforces quality gates, operational contracts, and compliance controls.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: AI agents today have passwords but no permission slips. They execute tool calls (fund transfers, database queries, shell commands, sub-agent delegation) with no standard mechanism to enforce authorization before the action executes. Current safety architectures rely on model alignment (probabilistic, training-time) and post-hoc evaluation (retrospective, batch). Neither provides deterministic, policy-based enforcement at the individual tool call level. We characterize this gap as the pre-action authorization problem and present the Open Agent Passport (OAP), an open specification and reference implementation that intercepts tool calls synchronously before execution, evaluates them against a declarative policy, and produces a cryptographically signed audit record. OAP enforces authorization decisions in a measured median of 53 ms (N=1,000). In a live adversarial testbed (4,437 authorization decisions across 1,151 sessions, $5,000 bounty), social engineering succeeded against the model 74.6% of the time under a permissive policy; under a restrictive OAP policy, a comparable population of attackers achieved a 0% success rate across 879 attempts. We distinguish pre-action authorization from sandboxed execution (contains blast radius but does not prevent unauthorized actions) and model-based screening (probabilistic), and show they are complementary. The same infrastructure that enforces security constraints (spending limits, capability scoping) also enforces quality gates, operational contracts, and compliance controls. The specification is released under Apache 2.0 (DOI: 10.5281/zenodo.18901596).
Related papers
- ROPE: Routed Origin Policy Enforcement against Indirect Prompt Injection [61.4148196085256]
We present ROPE (Routed Origin Policy Enforcement), which is anchored in a structural notion of trust.<n>Our approach admits two provable guarantees: 1) at every step of a trajectory, no value whose only origin is attacker-writable content reaches an origin-guarded parameter, and 2) no rewording of an injection changes an admission decision.
arXiv Detail & Related papers (2026-08-27T01:22:14Z) - Task-Conditioned Least-Privilege Learning for Executable Terminal and MCP Agents [45.88028371034407]
Tool-using large language-model agents can complete a task while exercising authority that the user did not grant or the task does not need.<n>We propose a framework where each action is audited before execution and again from observed effects along six dimensions of risk.<n>We find that after training using this framework on Qwen3.5-4B over 1,500 tasks, the selected seed reaches 98.48% safe success across 2,896 evaluation episodes.
arXiv Detail & Related papers (2026-08-18T22:07:23Z) - PACE: Policy-Attested Contract Execution for Safe AI Agents in Decentralized Finance [9.22772901752634]
We present PACE, a transaction-level authorization framework that interposes between an LLM-based agent and on-chain execution.<n> PACE cryptographically bind the approved intent, policy, and simulation report to the exact execution bytes, with replay and expiration protection.<n> PACE achieves a 0.00 unsafe execution rate and 0.00 false-positive rate on benign tasks, compared to 0.80 for the unguarded baseline.
arXiv Detail & Related papers (2026-08-18T00:22:46Z) - Bounded Agents: Delegation Security for Multi-Agent AI Systems [0.0]
LLM-based agents can act on behalf of a user to access cloud services, call tools, or invoke agents.<n>At session start, the agent's permissions are set but remain static, and each request is evaluated independently.<n>A prompt injection poses a risk only if the agent has authority to perform such actions.
arXiv Detail & Related papers (2026-08-16T18:38:00Z) - Understanding and Evaluating Claw-like Agent Security Through a Computer-Systems Lens [65.53494487819053]
Claw-like AI agents (e.g., OpenClaw) are always-on processes with persistent access to credentials, files, tools, and external services.<n>We adopt a computer-system analogy: treating a Claw-like agent as an agentic computer system whose gateway runtime plays an OS-like mediation role.<n>We develop SafeClawArena, a benchmark of 406 adversarial tasks across four attack surfaces.
arXiv Detail & Related papers (2026-06-29T18:00:45Z) - Capability Gates Are Not Authorization: Confused-Deputy Failures in LLM Agent Frameworks [0.0]
We audit whether LangChain/LangGraph, LlamaIndex, and the Stripe Agent Toolkit re-authorize each model-emitted call, with concrete argument values, before execution.<n>Across pinned public-source commits, all three provide capability gating by default, but none provides a deterministic fail-closed per-call value authorization gate by default.<n>We introduce ScopeGate, a five-stage PDP/PEP for agent tool calls: scope, authorization, money ceiling, idempotency, and default deny.
arXiv Detail & Related papers (2026-06-27T01:33:19Z) - Sovereign Assurance Boundary: Certificate-Bound Admission for Agentic Infrastructure [2.124730017640531]
Sovereign Assurance Boundary is a certificate-bound runtime admission layer for autonomous execution authority.<n>We detail the airlock-broker architecture, formalize its admission and revocation invariants, and report preliminary feasibility measurements from a Go prototype.
arXiv Detail & Related papers (2026-06-10T03:49:57Z) - Agent Control Protocol: Admission Control for Agent Actions [0.4929694290403903]
Agent Control Protocol (ACP) is a formal technical specification for governance of autonomous agents in B2B institutional environments.<n>ACP operates as an additional layer on top of RBAC and Zero Trust, without replacing them.
arXiv Detail & Related papers (2026-03-19T12:28:28Z) - Agent Behavioral Contracts: Formal Specification and Runtime Enforcement for Reliable Autonomous AI Agents [0.0]
Agent Behavioral Contracts (ABC) is a formal framework that brings Design-by-Contract principles to autonomous AI agents.<n>ABC specifies Preconditions, Invariants, Governance policies, and Recovery mechanisms as runtime-enforceable components.<n>We implement ABC in AgentAssert, a runtime enforcement library, and evaluate on AgentContract-Bench, a benchmark of 200 scenarios across 7 models from 6 vendors.
arXiv Detail & Related papers (2026-02-25T18:42:56Z) - OpenPort Protocol: A Security Governance Specification for AI Agent Tool Access [11.289770127178882]
This paper introduces OpenPort Protocol, a governance-first specification for exposing application tools through a secure server-side gateway.<n>OpenPort defines authorization-dependent discovery, stable response envelopes with machine-actionable texttagent.* reason codes, and an authorization model combining integration credentials, scoped permissions, and ABAC-style policy constraints.<n>For write operations, OpenPort specifies a risk-gated lifecycle that defaults to draft creation and human review, supports time-bounded auto-execution under explicit policy, and enforces high-risk safeguards including preflight impact binding and idempotency
arXiv Detail & Related papers (2026-02-22T05:16:40Z) - SkillJect: Automating Stealthy Skill-Based Prompt Injection for Coding Agents with Trace-Driven Closed-Loop Refinement [120.52289344734415]
We propose an automated framework for stealthy prompt injection tailored to agent skills.<n>The framework forms a closed loop with three agents: an Attack Agent that synthesizes injection skills under explicit stealth constraints, a Code Agent that executes tasks using the injected skills and an Evaluate Agent that logs action traces.<n>Our method consistently achieves high attack success rates under realistic settings.
arXiv Detail & Related papers (2026-02-15T16:09:48Z) - CausalArmor: Efficient Indirect Prompt Injection Guardrails via Causal Attribution [49.689452243966315]
AI agents equipped with tool-calling capabilities are susceptible to Indirect Prompt Injection (IPI) attacks.<n>We propose CausalArmor, a selective defense framework that computes lightweight, leave-one-out attributions at privileged decision points.<n> Experiments on AgentDojo and DoomArena demonstrate that CausalArmor matches the security of aggressive defenses.
arXiv Detail & Related papers (2026-02-08T11:34:08Z) - Faramesh: A Protocol-Agnostic Execution Control Plane for Autonomous Agent Systems [0.0]
Faramesh is a protocol-agnostic execution control plane that enforces execution-time authorization for agent-driven actions.<n>We show how these primitives yield enforceable, predictable governance for autonomous execution.
arXiv Detail & Related papers (2026-01-25T08:27:27Z) - Security Challenges in AI Agent Deployment: Insights from a Large Scale Public Competition [101.86739402748995]
We run the largest public red-teaming competition to date, targeting 22 frontier AI agents across 44 realistic deployment scenarios.<n>We build the Agent Red Teaming benchmark and evaluate it across 19 state-of-the-art models.<n>Our findings highlight critical and persistent vulnerabilities in today's AI agents.
arXiv Detail & Related papers (2025-07-28T05:13:04Z) - AegisLLM: Scaling Agentic Systems for Self-Reflective Defense in LLM Security [74.22452069013289]
AegisLLM is a cooperative multi-agent defense against adversarial attacks and information leakage.<n>We show that scaling agentic reasoning system at test-time substantially enhances robustness without compromising model utility.<n> Comprehensive evaluations across key threat scenarios, including unlearning and jailbreaking, demonstrate the effectiveness of AegisLLM.
arXiv Detail & Related papers (2025-04-29T17:36:05Z) - Progent: Programmable Privilege Control for LLM Agents [46.31581986508561]
We introduce Progent, the first privilege control framework to secure Large Language Models agents.<n>Progent enforces security at the tool level by restricting agents to performing tool calls necessary for user tasks while blocking potentially malicious ones.<n>Thanks to our modular design, integrating Progent does not alter agent internals and only requires minimal changes to the existing agent implementation.
arXiv Detail & Related papers (2025-04-16T01:58:40Z) - SOPBench: Evaluating Language Agents at Following Standard Operating Procedures and Constraints [59.645885492637845]
SOPBench is an evaluation pipeline that transforms each service-specific SOP code program into a directed graph of executable functions.<n>Our approach transforms each service-specific SOP code program into a directed graph of executable functions and requires agents to call these functions based on natural language SOP descriptions.<n>We evaluate 18 leading models, and results show the task is challenging even for top-tier models.
arXiv Detail & Related papers (2025-03-11T17:53:02Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.