The Tower of Babel Revisited: Multilingual Jailbreak Prompts on Closed-Source Large Language Models

Large language models (LLMs) have seen widespread applications across various domains, yet remain vulnerable to adversarial prompt injections. While most existing research on jailbreak attacks and hallucination phenomena has focused primarily on open-source models, we investigate the frontier of closed-source LLMs under multilingual attack scenarios. We present a first-of-its-kind integrated adversarial framework that leverages diverse attack techniques to systematically evaluate frontier proprietary solutions, including GPT-4o, DeepSeek-R1, Gemini-1.5-Pro, and Qwen-Max. Our evaluation spans six categories of security contents in both English and Chinese, generating 38,400 responses across 32 types of jailbreak attacks. Attack success rate (ASR) is utilized as the quantitative metric to assess performance from three dimensions: prompt design, model architecture, and language environment. Our findings suggest that Qwen-Max is the most vulnerable, while GPT-4o shows the strongest defense. Notably, prompts in Chinese consistently yield higher ASRs than their English counterparts, and our novel Two-Sides attack technique proves to be the most effective across all models. This work highlights a dire need for language-aware alignment and robust cross-lingual defenses in LLMs, and we hope it will inspire researchers, developers, and policymakers toward more robust and inclusive AI systems.

Large Language Models Based Fuzzing Techniques: A Survey

In the modern era where software plays a pivotal role, software security and vulnerability analysis have become essential for software development. Fuzzing test, as an efficient software testing method, are widely used in various domains. Moreover, the rapid development of Large Language Models (LLMs) has facilitated their application in the field of software testing, demonstrating remarkable performance. Considering that existing fuzzing test techniques are not entirely automated and software vulnerabilities continue to evolve, there is a growing trend towards employing fuzzing test generated based on large language models. This survey provides a systematic overview of the approaches that fuse LLMs and fuzzing tests for software testing. In this paper, a statistical analysis and discussion of the literature in three areas, namely LLMs, fuzzing test, and fuzzing test generated based on LLMs, are conducted by summarising the state-of-the-art methods up until 2024. Our survey also investigates the potential for widespread deployment and application of fuzzing test techniques generated by LLMs in the future.