Obscure but Effective: Classical Chinese Jailbreak Prompt Optimization via Bio-Inspired Search
arXiv:2602.22983v1 Announce Type: new Abstract: 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
arXiv:2602.22983v1 Announce Type: new Abstract: 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.
Executive Summary
The article 'Obscure but Effective: Classical Chinese Jailbreak Prompt Optimization via Bio-Inspired Search' proposes a novel framework, CC-BOS, for generating classical Chinese adversarial prompts to bypass safety constraints in Large Language Models (LLMs). The framework leverages multi-dimensional fruit fly optimization and incorporates a classical Chinese to English translation module for evaluation. The authors demonstrate the effectiveness of CC-BOS through extensive experiments, outperforming state-of-the-art jailbreak attack methods. While the research sheds light on the vulnerabilities of LLMs, it also raises concerns about the potential misuse of these models. The findings have significant implications for the development and deployment of LLMs in critical applications.
Key Points
- ▸ Classical Chinese is identified as a vulnerable language context for jailbreak attacks due to its conciseness and obscurity.
- ▸ The CC-BOS framework proposes a novel approach to generating classical Chinese adversarial prompts using bio-inspired search.
- ▸ The framework incorporates a classical Chinese to English translation module for evaluation and readability.
Merits
Strength
The proposed framework, CC-BOS, demonstrates improved effectiveness in jailbreak attacks compared to state-of-the-art methods.
Technical Innovation
The use of multi-dimensional fruit fly optimization and bio-inspired search in the CC-BOS framework represents a novel approach to generating adversarial prompts.
Demerits
Limitation
The framework's reliance on classical Chinese may limit its applicability to other language contexts.
Security Concerns
The vulnerability of LLMs to jailbreak attacks raises concerns about the potential misuse of these models in critical applications.
Expert Commentary
The article demonstrates a significant advancement in the field of LLM security, highlighting the importance of addressing the vulnerabilities of these models. The proposed framework, CC-BOS, represents a novel approach to generating adversarial prompts, but its reliance on classical Chinese may limit its applicability to other language contexts. The findings of the article have significant implications for the development and deployment of LLMs in critical applications, emphasizing the need for robust security features and regulatory frameworks.
Recommendations
- ✓ Future research should focus on developing more generalizable frameworks for generating adversarial prompts across various language contexts.
- ✓ The development of regulatory frameworks should prioritize the security and accountability of LLMs in critical applications.
