TROJail: Trajectory-Level Optimization for Multi-Turn Large Language Model Jailbreaks with Process Rewards
📰 ArXiv cs.AI
Learn to optimize large language model jailbreaks using trajectory-level optimization with process rewards to improve model safety
Action Steps
- Formulate the task of training automated multi-turn attackers as a multi-turn optimization problem
- Implement trajectory-level optimization to learn long-term attack strategies
- Use process rewards to guide the optimization process
- Evaluate the effectiveness of the TROJail approach in identifying model safety vulnerabilities
- Apply the TROJail technique to improve the security of large language models
Who Needs to Know This
NLP engineers and researchers can benefit from this technique to enhance model security and identify potential vulnerabilities
Key Insight
💡 Trajectory-level optimization with process rewards can improve the effectiveness of multi-turn jailbreak attacks on large language models
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💡 TROJail: Optimize large language model jailbreaks using trajectory-level optimization with process rewards #LLM #ModelSafety
Key Takeaways
Learn to optimize large language model jailbreaks using trajectory-level optimization with process rewards to improve model safety
Full Article
Title: TROJail: Trajectory-Level Optimization for Multi-Turn Large Language Model Jailbreaks with Process Rewards
Abstract:
arXiv:2512.07761v3 Announce Type: replace Abstract: Large language models have seen widespread adoption, yet they remain vulnerable to multi-turn jailbreak attacks, threatening their safe deployment. This has led to the task of training automated multi-turn attackers to probe model safety vulnerabilities. However, existing approaches typically rely on turn-level optimization, which is insufficient for learning long-term attack strategies. To bridge this gap, we formulate this task as a multi-tur
Abstract:
arXiv:2512.07761v3 Announce Type: replace Abstract: Large language models have seen widespread adoption, yet they remain vulnerable to multi-turn jailbreak attacks, threatening their safe deployment. This has led to the task of training automated multi-turn attackers to probe model safety vulnerabilities. However, existing approaches typically rely on turn-level optimization, which is insufficient for learning long-term attack strategies. To bridge this gap, we formulate this task as a multi-tur
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