对比两种AI代理部署架构，发现架构选择直接影响安全风险类型。
Bridging AI and Software Security: A Comparative Vulnerability Assessment of LLM Agent Deployment Paradigms
- 用统一框架测试7种模型3250个攻击场景，比较函数调用与MCP架构
- 链式攻击成功率高达91%-96%，复杂度越高越易被攻破
- 先进模型反而更易被利用，适合关注部署安全的开发者
大型语言模型（LLM）代理在AI和传统软件两个领域均存在安全漏洞，但现有研究多分开处理。本文通过统一威胁分类框架，对函数调用架构与模型上下文协议（MCP）部署范式进行对比评估。在7种语言模型上测试了3250个攻击场景，涵盖简单、复合及链式攻击，目标包括提示注入等AI特有威胁和JSON注入、拒绝服务等软件漏洞。结果显示，函数调用架构整体攻击成功率更高（73.5% vs MCP的62.59%），系统侧漏洞更突出；而MCP则表现出更强的模型侧暴露。攻击复杂度显著提升有效性，链式攻击成功率达91%-96%。出人意料的是，具备更强推理能力的模型反而更易被利用，尽管其威胁检测能力更好。结果表明，架构选择会从根本上改变威胁格局。本研究建立了跨领域LLM代理安全评估的方法基础，并提供可落地的部署建议。代码与实验材料见：https://github.com/theconsciouslab-ai/llm-agent-security。
原文摘要 · Abstract (English)
Large Language Model (LLM) agents face security vulnerabilities spanning AI-specific and traditional software domains, yet current research addresses these separately. This study bridges this gap through comparative evaluation of Function Calling architecture and Model Context Protocol (MCP) deployment paradigms using a unified threat classification framework. We tested 3,250 attack scenarios across seven language models, evaluating simple, composed, and chained attacks targeting both AI-specific threats (prompt injection) and software vulnerabilities (JSON injection, denial-of-service). Function Calling showed higher overall attack success rates (73.5% vs 62.59% for MCP), with greater system-centric vulnerability while MCP exhibited increased LLM-centric exposure. Attack complexity dramatically amplified effectiveness, with chained attacks achieving 91-96% success rates. Counterintuitively, advanced reasoning models demonstrated higher exploitability despite better threat detection. Results demonstrate that architectural choices fundamentally reshape threat landscapes. This work establishes methodological foundations for cross-domain LLM agent security assessment and provides evidence-based guidance for secure deployment. Code and experimental materials are available at https: // github. com/ theconsciouslab-ai/llm-agent-security.
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