@inproceedings{schwartz-etal-2025-graph,
    title = "Graph of Attacks with Pruning: Optimizing Stealthy Jailbreak Prompt Generation for Enhanced {LLM} Content Moderation",
    author = "Schwartz, Daniel  and
      Bespalov, Dmitriy  and
      Wang, Zhe  and
      Kulkarni, Ninad  and
      Qi, Yanjun",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-industry.46/",
    pages = "659--671",
    ISBN = "979-8-89176-333-3",
    abstract = "As large language models (LLMs) become increasingly prevalent, ensuring their robustness against adversarial misuse is crucial. This paper introduces the Graph of Attacks with Pruning (GAP) framework, an advanced approach for generating stealthy jailbreak prompts to evaluate and enhance LLM safeguards. GAP addresses limitations in existing tree-based LLM jailbreak methods by implementing an interconnected graph structure that enables knowledge sharing across attack paths. Our experimental evaluation demonstrates GAP{'}s superiority over existing techniques, achieving a 20.8{\%} increase in attack success rates while reducing query costs by 62.7{\%}. GAP consistently outperforms state-of-the-art methods for attacking both open and closed LLMs, with attack success rates of {\ensuremath{>}}96{\%}. Additionally, we present specialized variants like GAP-Auto for automated seed generation and GAP-VLM for multimodal attacks. GAP-generated prompts prove highly effective in improving content moderation systems, increasing true positive detection rates by 108.5{\%} and accuracy by 183.6{\%} when used for fine-tuning."
}