Transferable & Stealthy Ensemble Attacks: A Black-Box Jailbreaking Framework for Large Language Models
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915600760045568 |
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| author | Yang, Yiqi Fu, Hongye |
| author_facet | Yang, Yiqi Fu, Hongye |
| contents | We present a novel black-box jailbreaking framework that integrates multiple LLM-as-Attacker strategies to deliver highly transferable and effective attacks. The framework is grounded in three key insights from prior jailbreaking research and practice: ensemble approaches outperform single methods in exposing aligned LLM vulnerabilities, malicious instructions vary in jailbreaking difficulty requiring tailored optimization, and disrupting semantic coherence of malicious prompts can manipulate their embeddings to boost success rates. Validated in the Competition for LLM and Agent Safety 2024, our solution achieved top rankings in the Jailbreaking Attack Track. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_23558 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Transferable & Stealthy Ensemble Attacks: A Black-Box Jailbreaking Framework for Large Language Models Yang, Yiqi Fu, Hongye Cryptography and Security Artificial Intelligence We present a novel black-box jailbreaking framework that integrates multiple LLM-as-Attacker strategies to deliver highly transferable and effective attacks. The framework is grounded in three key insights from prior jailbreaking research and practice: ensemble approaches outperform single methods in exposing aligned LLM vulnerabilities, malicious instructions vary in jailbreaking difficulty requiring tailored optimization, and disrupting semantic coherence of malicious prompts can manipulate their embeddings to boost success rates. Validated in the Competition for LLM and Agent Safety 2024, our solution achieved top rankings in the Jailbreaking Attack Track. |
| title | Transferable & Stealthy Ensemble Attacks: A Black-Box Jailbreaking Framework for Large Language Models |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2410.23558 |