Title: GraphBreak: Systematic Exploitation of LLM Safety Mechanisms Through Semantic Structure Manipulation
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: LLM safety, jailbreaking attacks, graph representation

Abstract: Large Language Models (LLMs) have been equipped with safety mechanisms to prevent harmful outputs, but these guardrails can often be bypassed through ``jailbreak'' prompts. This paper introduces a novel graph-based approach to systematically generate jailbreak prompts through semantic transformations. We represent malicious prompts as nodes in a graph structure with edges denoting different transformations, leveraging Abstract Meaning Representation (AMR) and Resource Description Framework (RDF) to parse user goals into semantic components that can be manipulated to evade safety filters. 
We demonstrate a particularly effective exploitation vector by instructing LLMs to generate code that realizes the intent described in these semantic graphs, achieving success rates of up to 87\% against leading commercial LLMs. 
Our analysis reveals that contextual framing and abstraction are particularly effective at circumventing safety measures, highlighting critical gaps in current safety alignment techniques that focus primarily on surface-level patterns. 
These findings provide insights for developing more robust safeguards against structured semantic attacks. 
Our research contributes a practical and cost-efficient methodology for systematically stress-testing LLM safety mechanisms.

TL;DR: We develop a novel jailbreaking attack against LLMs by leveraging graph representations