TRAP: Tree-Guided Reinforcement-Augmented Probing for Automated LLM Jailbreaking

Summary

TRAP is a black-box framework for automated jailbreak strategy discovery against aligned LLMs. It organizes known jailbreak methods and successful attack templates into a semantic strategy tree, and a tree-structured gradient bandit reinforcement-learning policy uses evaluator feedback to pick and update strategies across attack objectives. On AdvBench across seven aligned LLMs, TRAP outperforms its counterparts on six models under the reported settings, with attack success rates of 45.6% to 97.9%.

Topics

• Prompt injection and jailbreaks

Related items

• InfoDeveloping Gemini 4 Argon responsibly and safelySimilar attack · DeepMind Safety Research
• InfoRethinking Quality in Large Language Model Routing: Security-Conditioned Model-and-Budget SelectionSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)
• InfoTop GenAI security resources — October 2026Similar attack · Adversa AI Blog
• InfoSame Attack, Different Judge: A First Controlled Adversarial Comparison of Jev, LLM Judges, and Purpose-Built Safety ClassifiersSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)
• InfoThe Tokens Strike Back: Safety-Aware Denoising with Directional Embedding-based RevisionSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)