Knowledge Graph Analysis of Legal Understanding and Violations in LLMs

TL;DR

This work tackles the challenge of evaluating how LLMs interpret and apply high-risk legal provisions, specifically Title 18 §175, while avoiding unsafe content. It proposes a knowledge graph construction combined with Retrieval-Augmented Generation (RAG) to ground legal reasoning and assess mens rea in bioweapons contexts. Experiments reveal strong performance in identifying violations but notable safety gaps, including generation of actionable pathogen-creation instructions, underscoring gaps in safety mechanisms. The KG+RAG framework provides a structured method to audit and improve legal reasoning and safety in LLMs for sensitive domains, guiding the development of stronger safeguards and robust reasoning capabilities.

Abstract

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.