Obscure but Effective: Classical Chinese Jailbreak Prompt Optimization via Bio-Inspired Search
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866911540189331456 |
|---|---|
| author | Huang, Xun Qin, Simeng Jia, Xiaoshuang Duan, Ranjie Yan, Huanqian Zeng, Zhitao Yang, Fei Liu, Yang Jia, Xiaojun |
| author_facet | Huang, Xun Qin, Simeng Jia, Xiaoshuang Duan, Ranjie Yan, Huanqian Zeng, Zhitao Yang, Fei Liu, Yang Jia, Xiaojun |
| contents | As Large Language Models (LLMs) are increasingly used, their security risks have drawn increasing attention. Existing research reveals that LLMs are highly susceptible to jailbreak attacks, with effectiveness varying across language contexts. This paper investigates the role of classical Chinese in jailbreak attacks. Owing to its conciseness and obscurity, classical Chinese can partially bypass existing safety constraints, exposing notable vulnerabilities in LLMs. Based on this observation, this paper proposes a framework, CC-BOS, for the automatic generation of classical Chinese adversarial prompts based on multi-dimensional fruit fly optimization, facilitating efficient and automated jailbreak attacks in black-box settings. Prompts are encoded into eight policy dimensions-covering role, behavior, mechanism, metaphor, expression, knowledge, trigger pattern and context; and iteratively refined via smell search, visual search, and cauchy mutation. This design enables efficient exploration of the search space, thereby enhancing the effectiveness of black-box jailbreak attacks. To enhance readability and evaluation accuracy, we further design a classical Chinese to English translation module. Extensive experiments demonstrate that effectiveness of the proposed CC-BOS, consistently outperforming state-of-the-art jailbreak attack methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_22983 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Obscure but Effective: Classical Chinese Jailbreak Prompt Optimization via Bio-Inspired Search Huang, Xun Qin, Simeng Jia, Xiaoshuang Duan, Ranjie Yan, Huanqian Zeng, Zhitao Yang, Fei Liu, Yang Jia, Xiaojun Artificial Intelligence Cryptography and Security As Large Language Models (LLMs) are increasingly used, their security risks have drawn increasing attention. Existing research reveals that LLMs are highly susceptible to jailbreak attacks, with effectiveness varying across language contexts. This paper investigates the role of classical Chinese in jailbreak attacks. Owing to its conciseness and obscurity, classical Chinese can partially bypass existing safety constraints, exposing notable vulnerabilities in LLMs. Based on this observation, this paper proposes a framework, CC-BOS, for the automatic generation of classical Chinese adversarial prompts based on multi-dimensional fruit fly optimization, facilitating efficient and automated jailbreak attacks in black-box settings. Prompts are encoded into eight policy dimensions-covering role, behavior, mechanism, metaphor, expression, knowledge, trigger pattern and context; and iteratively refined via smell search, visual search, and cauchy mutation. This design enables efficient exploration of the search space, thereby enhancing the effectiveness of black-box jailbreak attacks. To enhance readability and evaluation accuracy, we further design a classical Chinese to English translation module. Extensive experiments demonstrate that effectiveness of the proposed CC-BOS, consistently outperforming state-of-the-art jailbreak attack methods. |
| title | Obscure but Effective: Classical Chinese Jailbreak Prompt Optimization via Bio-Inspired Search |
| topic | Artificial Intelligence Cryptography and Security |
| url | https://arxiv.org/abs/2602.22983 |