Prompts Don't Protect: Architectural Enforcement via MCP Proxy for LLM Tool Access Control
- URL: http://arxiv.org/abs/2605.18414v1
- Date: Mon, 18 May 2026 13:52:24 GMT
- Title: Prompts Don't Protect: Architectural Enforcement via MCP Proxy for LLM Tool Access Control
- Abstract summary: Large language models increasingly operate as autonomous agents that select and invoke tools from large registries.<n>We propose a governed MCP proxy that enforces attribute-based access control (ABAC) at two points: tool discovery and tool invocation.<n>Our proxy reduces unauthorized invocation rate (UIR) to 0% while adding under 50ms median latency.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large language models increasingly operate as autonomous agents that select and invoke tools from large registries. We identify a critical gap: when unauthorized tools are visible in an agent's context, models select them in adversarial scenarios -- even when explicitly instructed otherwise. We propose a governed MCP proxy that enforces attribute-based access control (ABAC) at two points: tool discovery, where unauthorized tools are removed from the model's context window, and tool invocation, where a second check blocks any unauthorized call. Across three models (Qwen 2.5 7B, Llama 3.1 8B, Claude Haiku 3.5) and 150 adversarial tasks spanning four attack categories, our proxy reduces unauthorized invocation rate (UIR) to 0% while adding under 50ms median latency. Prompt-based restrictions reduce UIR by only 11--18 percentage points, leaving substantial residual risk. Our results show that architectural enforcement -- not prompting -- is necessary for reliable tool access control in deployed agentic systems.
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) - Joint Optimization of Tool Creation and Use for Large Language Model Agents [69.07066297088187]
SMITH (-grounded Multi-task Iterative Tool Honing) trains tool creation and tool use inside a single policy.<n>A 4B Qwen3 trained with SMITH on 13 procedural reasoning tasks with exact verifiers reaches 79.8 macro-average accuracy on held-out tasks.<n>It also reaches 40.4 on TabMWP-Hard and 42.6 on out-of-domain GQA (+7.6 over the best same-backbone inference-time baseline)
arXiv Detail & Related papers (2026-08-25T13:59:34Z) - WebMCP-Phalanx: Enforcing and Characterizing Trust Boundaries for Browser-Integrated LLM Agents [1.8734449181723825]
W3C WebMCP proposal enables LLM agents to invoke tools exposed by web pages.<n>In multi-party web environments, integrating agent execution into a browser security model leaves insufficient provenance and lifecycle guarantees for agent-accessible tools.<n>We propose WebMCP-Phalanx, a dual-layer agent runtime architecture.
arXiv Detail & Related papers (2026-08-25T03:16:45Z) - 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) - Securing LLM Agents Need Intent-to-Execution Integrity [49.490963596514185]
We argue that securing LLM agents requires defining an end-to-end correctness property that specifies when an agent's execution faithfully reflects the user's intent.<n>We observe that LLM agents are structurally analogous to compilers, where security violations correspond to mis-executions that do not preserve user intent.<n>We derive four integrity properties that must hold simultaneously: emphTool Integrity, emphInstruction Integrity, emphJudgment Integrity, and emphData Flow Integrity.
arXiv Detail & Related papers (2026-05-16T12:53:31Z) - ClawGuard: A Runtime Security Framework for Tool-Augmented LLM Agents Against Indirect Prompt Injection [8.627671856044527]
textscClawGuard enforces a user-confirmed rule set at every tool-call boundary.<n>textscClawGuard blocks all three injection pathways without model modification or infrastructure change.<n>This work establishes deterministic tool-call boundary enforcement as an effective defense mechanism for secure agentic AI systems.
arXiv Detail & Related papers (2026-04-13T17:55:11Z) - AEGIS: No Tool Call Left Unchecked -- A Pre-Execution Firewall and Audit Layer for AI Agents [4.963079926145645]
AEGIS is a pre-execution firewall and audit layer for AI agents.<n>It interposes on the tool-execution path and applies a three-stage pipeline.<n>High-risk calls can be held for human approval, and all decisions are recorded in a tamper-evident audit trail.
arXiv Detail & Related papers (2026-03-13T03:49:12Z) - AgentSentry: Mitigating Indirect Prompt Injection in LLM Agents via Temporal Causal Diagnostics and Context Purification [25.817251923574286]
We propose a novel inference-time detection and mitigation framework for large language model (LLM) agents.<n>AgentSentry is the first inference-time defense to model multi-turn IPI as a temporal causal takeover.<n>We evaluate AgentSentry on the textscAgentDojo benchmark across four task suites, three IPI attack families, and multiple black-box LLMs.
arXiv Detail & Related papers (2026-02-26T07:59:10Z) - Sponge Tool Attack: Stealthy Denial-of-Efficiency against Tool-Augmented Agentic Reasoning [58.432996881401415]
Recent work augments large language models (LLMs) with external tools to enable agentic reasoning.<n>We propose Sponge Tool Attack (STA), which disrupts agentic reasoning solely by rewriting the input prompt.<n>STA generates benign-looking prompt rewrites from the original one with high semantic fidelity.
arXiv Detail & Related papers (2026-01-24T19:36:51Z) - AgenTRIM: Tool Risk Mitigation for Agentic AI [5.4672006013914975]
We introduce AgenTRIM, a framework for detecting and mitigating tool-driven agency risks.<n>AgenTRIM addresses these risks through complementary offline and online phases.<n>AgenTRIM substantially reduces attack success while maintaining high task performance.
arXiv Detail & Related papers (2026-01-18T15:10:18Z) - Securing the Model Context Protocol: Defending LLMs Against Tool Poisoning and Adversarial Attacks [8.419049623790618]
This work analyzes three classes of semantic attacks on MCP-integrated systems.<n>We introduce a layered security framework with three components: RSA-based manifest signing to enforce descriptor integrity, LLM-on-LLM semantic vetting to detect suspicious tool definitions, and lightweight guardrails that block anomalous tool behavior at runtime.<n>Our results show that the proposed framework reduces unsafe tool invocation rates without model fine-tuning or internal modification.
arXiv Detail & Related papers (2025-12-06T20:07:58Z) - DeepAgent: A General Reasoning Agent with Scalable Toolsets [111.6384541877723]
DeepAgent is an end-to-end deep reasoning agent that performs autonomous thinking, tool discovery, and action execution.<n>To address the challenges of long-horizon interactions, we introduce an autonomous memory folding mechanism that compresses past interactions into structured episodic, working, and tool memories.<n>We develop an end-to-end reinforcement learning strategy, namely ToolPO, that leverages LLM-simulated APIs and applies tool-call advantage attribution to assign fine-grained credit to the tool invocation tokens.
arXiv Detail & Related papers (2025-10-24T16:24:01Z) - ToolTweak: An Attack on Tool Selection in LLM-based Agents [52.17181489286236]
We show that adversaries can systematically bias agents toward selecting specific tools, gaining unfair advantage over equally capable alternatives.<n>We present ToolTweak, a lightweight automatic attack that increases selection rates from a baseline of around 20% to as high as 81%.<n>To mitigate these risks, we evaluate two defenses: paraphrasing and perplexity filtering, which reduce bias and lead agents to select functionally similar tools more equally.
arXiv Detail & Related papers (2025-10-02T20:44:44Z) - Prompt Injection Attack to Tool Selection in LLM Agents [60.95349602772112]
A popular approach follows a two-step process - emphretrieval and emphselection - to pick the most appropriate tool from a tool library for a given task.<n>In this work, we introduce textitToolHijacker, a novel prompt injection attack targeting tool selection in no-box scenarios.
arXiv Detail & Related papers (2025-04-28T13:36:43Z) - 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.