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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳尚澤 | zh_TW |
| dc.contributor.advisor | Shang-Tse Chen | en |
| dc.contributor.author | 許郁翎 | zh_TW |
| dc.contributor.author | Yu-Ling Hsu | en |
| dc.date.accessioned | 2025-02-27T16:28:27Z | - |
| dc.date.available | 2025-02-28 | - |
| dc.date.copyright | 2025-02-27 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-02-11 | - |
| dc.identifier.citation | [1] G. Alon and M. Kamfonas. Detecting language model attacks with perplexity. ArXiv, abs/2308.14132, 2023.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/97161 | - |
| dc.description.abstract | 大型語言模型(LLM)近年來快速發展,革新了各種應用,大大提高了便利性和生產力。然而,隨著其強大功能的出現,倫理問題和新型態的攻擊(如越獄攻擊)也隨之產生。雖然許多現有研究由於簡單性和靈活性而專注於個體攻擊策略,但對尋求提升對未見數據可轉移性的通用方法的研究較少。在本文中,我們設計一個方法,用於針對越獄攻擊在通用設定的情境中優化多重提示。此外,我們對方法的設計延伸到防禦的情境上。實驗結果說明我們的方法可以在控制可讀性的情況下達到高攻擊率。 | zh_TW |
| dc.description.abstract | Large language models (LLMs) have seen rapid development in recent years, revolutionizing various applications and significantly enhancing convenience and productivity. However, alongside their impressive capabilities, ethical concerns and new types of attacks, such as jailbreaking, have emerged. While many existing studies focus on individual attack strategies due to their simplicity and flexibility, there is limited research on universal approaches, which seek to find generalizable checkpoints to optimize across datasets and improve transferability to unseen data. In this paper, we introduce JUMP, a method designed to discover adversarial multi-prompts in a universal setting. We also adapt our approach for defense, which we term DUMP. Experimental results show that our method for optimizing universal multi-prompts surpasses existing techniques. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-02-27T16:28:27Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-02-27T16:28:27Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Verification Letter from the Oral Examination Committee i
Acknowledgements ii 摘要 iv Abstract v Contents vi List of Figures ix List of Tables x Chapter 1 Introduction 1 Chapter 2 Related Work 4 2.1 Jailbreak with Prompting . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Jailbreak with Finetuning Attackers . . . . . . . . . . . . . . . . . . 5 2.3 Defenses against Jailbreaking . . . . . . . . . . . . . . . . . . . . . 5 Chapter 3 Methdology 6 3.1 Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.2 BEAST . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.3 From BEAST to JUMP* . . . . . . . . . . . . . . . . . . . . . . . . 8 3.4 Adding perplexity constraints to JUMP* . . . . . . . . . . . . . . . . 11 Chapter 4 Attack Experiments 12 4.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 4.2 Victim Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 4.3 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 4.4 Comparing Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 13 4.5 Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . 14 4.5.1 Single-Prompt vs. Multi-Prompts Settings . . . . . . . . . . . . . . 14 4.5.2 Trade-offs Between ASR and Perplexity . . . . . . . . . . . . . . . 14 4.5.3 From JUMP to JUMP++ . . . . . . . . . . . . . . . . . . . . . . . 15 4.5.4 Sensitivity to different choices of initial templates . . . . . . . . . . 16 4.5.5 Transfer Attack Results . . . . . . . . . . . . . . . . . . . . . . . . 18 Chapter 5 Defenses against Individual Attacks 20 5.1 Defensing with Universal Multi-Prompts (DUMP) . . . . . . . . . . 20 5.2 Comparing Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 20 5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Chapter 6 Conclusion 23 Chapter 7 Limitations 24 References 25 Appendix A — Algorithms 33 Appendix B — Detail Settings in Experiments 36 B.1 Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 B.2 Baseline Attacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 B.3 Baseline Defenses . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 Appendix C — Supplementary Materials 39 Appendix D — Evaluations 42 Appendix E — Demonstrations 44 | - |
| dc.language.iso | en | - |
| dc.title | 利用通用多重提示下的越獄攻擊 | zh_TW |
| dc.title | Jailbreaking with Universal Multi-Prompts | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 李宏毅;陳縕儂;高宏宇 | zh_TW |
| dc.contributor.oralexamcommittee | Hung-Yi Lee;Yun-Nung Chen;Hung-Yu Kao | en |
| dc.subject.keyword | 越獄攻擊,集束搜索,大型語言模型,自然語言處理,深度學習, | zh_TW |
| dc.subject.keyword | Jailbreak,Beam Search,Large Language Model,Natural Language Processing,Deep Learning, | en |
| dc.relation.page | 50 | - |
| dc.identifier.doi | 10.6342/NTU202500547 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2025-02-11 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 資訊工程學系 | - |
| dc.date.embargo-lift | 2025-02-28 | - |
| 顯示於系所單位： | 資訊工程學系 | |
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