SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Acceso en línea: | |
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| _version_ | 1866910485788491776 |
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| author | Robey, Alexander Wong, Eric Hassani, Hamed Pappas, George J. |
| author_facet | Robey, Alexander Wong, Eric Hassani, Hamed Pappas, George J. |
| contents | Despite efforts to align large language models (LLMs) with human intentions, widely-used LLMs such as GPT, Llama, and Claude are susceptible to jailbreaking attacks, wherein an adversary fools a targeted LLM into generating objectionable content. To address this vulnerability, we propose SmoothLLM, the first algorithm designed to mitigate jailbreaking attacks. Based on our finding that adversarially-generated prompts are brittle to character-level changes, our defense randomly perturbs multiple copies of a given input prompt, and then aggregates the corresponding predictions to detect adversarial inputs. Across a range of popular LLMs, SmoothLLM sets the state-of-the-art for robustness against the GCG, PAIR, RandomSearch, and AmpleGCG jailbreaks. SmoothLLM is also resistant against adaptive GCG attacks, exhibits a small, though non-negligible trade-off between robustness and nominal performance, and is compatible with any LLM. Our code is publicly available at \url{https://github.com/arobey1/smooth-llm}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_03684 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks Robey, Alexander Wong, Eric Hassani, Hamed Pappas, George J. Machine Learning Artificial Intelligence Despite efforts to align large language models (LLMs) with human intentions, widely-used LLMs such as GPT, Llama, and Claude are susceptible to jailbreaking attacks, wherein an adversary fools a targeted LLM into generating objectionable content. To address this vulnerability, we propose SmoothLLM, the first algorithm designed to mitigate jailbreaking attacks. Based on our finding that adversarially-generated prompts are brittle to character-level changes, our defense randomly perturbs multiple copies of a given input prompt, and then aggregates the corresponding predictions to detect adversarial inputs. Across a range of popular LLMs, SmoothLLM sets the state-of-the-art for robustness against the GCG, PAIR, RandomSearch, and AmpleGCG jailbreaks. SmoothLLM is also resistant against adaptive GCG attacks, exhibits a small, though non-negligible trade-off between robustness and nominal performance, and is compatible with any LLM. Our code is publicly available at \url{https://github.com/arobey1/smooth-llm}. |
| title | SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2310.03684 |