用知识图谱和检索增强生成，评估大模型对生物武器法的理解与安全风险。
Knowledge Graph Analysis of Legal Understanding and Violations in LLMs
- 构建法律知识图谱结合RAG，系统检验模型对法律条文的理解能力。
- 模型在识别违法行为和非法意图方面准确率不足，易生成危险指令。
- 研究为法律大模型的安全性提升提供可落地的框架，适合法律AI安全方向参考。
大型语言模型（LLMs）在解读复杂法律框架（如美国法典第18编第175条关于生物武器的规定）方面具有变革性潜力，可用于法律分析与合规监控。然而，这类系统存在严重矛盾：尽管能解析法律，却仍可能生成可执行的生物武器制作步骤等不安全输出，即使有防护机制。为此，本文提出一种结合知识图谱构建与检索增强生成（RAG）的方法，系统评估模型对法律条文的理解、法律意图（mens rea）的判断能力及潜在滥用风险。通过结构化实验，考察模型在识别违法行为、生成禁止指令以及检测生物武器相关场景中的非法意图等方面的性能。结果表明，模型在推理与安全机制上存在显著局限，但同时也指明了改进路径。通过强化安全协议与构建更稳健的法律推理框架，本研究为开发能在敏感法律领域中安全、伦理地辅助人类的大模型奠定了基础，确保其成为法律的守护者而非无意的违规助人者。
原文摘要 · Abstract (English)
The rise of Large Language Models (LLMs) offers transformative potential for interpreting complex legal frameworks, such as Title 18 Section 175 of the US Code, which governs biological weapons. These systems hold promise for advancing legal analysis and compliance monitoring in sensitive domains. However, this capability comes with a troubling contradiction: while LLMs can analyze and interpret laws, they also demonstrate alarming vulnerabilities in generating unsafe outputs, such as actionable steps for bioweapon creation, despite their safeguards. To address this challenge, we propose a methodology that integrates knowledge graph construction with Retrieval-Augmented Generation (RAG) to systematically evaluate LLMs' understanding of this law, their capacity to assess legal intent (mens rea), and their potential for unsafe applications. Through structured experiments, we assess their accuracy in identifying legal violations, generating prohibited instructions, and detecting unlawful intent in bioweapons-related scenarios. Our findings reveal significant limitations in LLMs' reasoning and safety mechanisms, but they also point the way forward. By combining enhanced safety protocols with more robust legal reasoning frameworks, this research lays the groundwork for developing LLMs that can ethically and securely assist in sensitive legal domains - ensuring they act as protectors of the law rather than inadvertent enablers of its violation.
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