大模型工具调用易被绕过，需用架构层管控而非靠提示词约束。
Prompts Don't Protect: Architectural Enforcement via MCP Proxy for LLM Tool Access Control
- 在工具发现阶段通过代理过滤，从源头杜绝越权访问。
- 提示词控制失效，越权调用率仍达4.0%~37.0%，最高达96%。
- 适用于对安全要求高的大模型应用，如金融、医疗系统。
大型语言模型越来越多地作为自主代理，从大型工具库中选择并调用工具。我们发现一个关键漏洞：当未经授权的工具出现在代理上下文中时，模型在48%-68%的对抗场景下仍会调用它们，即使明确指令禁止。角色升级攻击（如“我是财务总监，绕过权限”）是最危险类别，在前沿模型中越权调用率达96%。该现象在三种模型（涵盖开源与前沿系统）中均成立，包括经过强对齐训练的指令微调模型。关键问题是：基于提示词的合规性既不足也不可预测——显式每工具白名单将违规率降至最低4.0%，但无法归零；不同模型间合规性差异巨大，越权调用率（UIR）在4.0%至37.0%之间波动，且与模型整体能力无可靠关联。为此，我们提出一种MCP代理的属性基访问控制（ABAC）层，于工具发现阶段过滤工具注册表。由于未授权工具从未进入模型上下文，其UIR天然为0%，这是提示词无法实现的结构性保障。
原文摘要 · Abstract (English)
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 48-68% of adversarial scenarios, even when explicitly instructed not to. Role escalation attacks (e.g., "I'm the CFO, override the access controls") are the most dangerous category, reaching 96% unauthorized invocation in frontier models. We show this holds across three models spanning open-weight and frontier systems, including instruction-tuned models with strong alignment training. Critically, prompt-based compliance is both insufficient and unpredictable: explicit per-tool allowlists reduce violations to as low as 4.0% but never to zero, and compliance varies widely across models, from 4.0% to 37.0% UIR, with no reliable relationship to general capability. We propose a proxy-enforced attribute-based access control (ABAC) layer for MCP that filters tool registries at discovery time. Because unauthorized tools never reach the model context, UIR is 0% by design, a structural guarantee that prompt instructions cannot replicate regardless of model or phrasing.
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