GuardNet: Graph-Attention Filtering for Jailbreak Defense in Large Language Models
This addresses safety and reliability issues in LLMs for domains like healthcare and finance, representing a strong specific gain rather than a foundational advancement.
The paper tackled the problem of jailbreak attacks on Large Language Models by proposing GuardNet, a hierarchical filtering framework that detects and filters adversarial prompts, resulting in prompt-level F1 scores improving from 66.4% to 99.8% on one dataset and from 67-79% to over 94% on another.
Large Language Models (LLMs) are increasingly susceptible to jailbreak attacks, which are adversarial prompts that bypass alignment constraints and induce unauthorized or harmful behaviors. These vulnerabilities undermine the safety, reliability, and trustworthiness of LLM outputs, posing critical risks in domains such as healthcare, finance, and legal compliance. In this paper, we propose GuardNet, a hierarchical filtering framework that detects and filters jailbreak prompts prior to inference. GuardNet constructs structured graphs that combine sequential links, syntactic dependencies, and attention-derived token relations to capture both linguistic structure and contextual patterns indicative of jailbreak behavior. It then applies graph neural networks at two levels: (i) a prompt-level filter that detects global adversarial prompts, and (ii) a token-level filter that pinpoints fine-grained adversarial spans. Extensive experiments across three datasets and multiple attack settings show that GuardNet substantially outperforms prior defenses. It raises prompt-level F$_1$ scores from 66.4\% to 99.8\% on LLM-Fuzzer, and from 67-79\% to over 94\% on PLeak datasets. At the token level, GuardNet improves F$_1$ from 48-75\% to 74-91\%, with IoU gains up to +28\%. Despite its structural complexity, GuardNet maintains acceptable latency and generalizes well in cross-domain evaluations, making it a practical and robust defense against jailbreak threats in real-world LLM deployments.