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Comprehensive Evaluation of ChatGPT Reliability Through Multilingual Inquiries

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arxiv 2312.10524 v1 pith:75657RRS submitted 2023-12-16 cs.SE

Poorna Chander Reddy Puttaparthi , Soham Sanjay Deo , Hakan Gul , Yiming Tang , Weiyi Shang , Zhe Yu This is my paper · ORCID
classification cs.SE
keywords chatgptjailbreaklanguagedifferentmultilingualwrappingmaliciousquestions
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ChatGPT is currently the most popular large language model (LLM), with over 100 million users, making a significant impact on people's lives. However, due to the presence of jailbreak vulnerabilities, ChatGPT might have negative effects on people's lives, potentially even facilitating criminal activities. Testing whether ChatGPT can cause jailbreak is crucial because it can enhance ChatGPT's security, reliability, and social responsibility. Inspired by previous research revealing the varied performance of LLMs in different language translations, we suspected that wrapping prompts in multiple languages might lead to ChatGPT jailbreak. To investigate this, we designed a study with a fuzzing testing approach to analyzing ChatGPT's cross-linguistic proficiency. Our study includes three strategies by automatically posing different formats of malicious questions to ChatGPT: (1) each malicious question involving only one language, (2) multilingual malicious questions, (3) specifying that ChatGPT responds in a language different from the prompts. In addition, we also combine our strategies by utilizing prompt injection templates to wrap the three aforementioned types of questions. We examined a total of 7,892 Q&A data points, discovering that multilingual wrapping can indeed lead to ChatGPT's jailbreak, with different wrapping methods having varying effects on jailbreak probability. Prompt injection can amplify the probability of jailbreak caused by multilingual wrapping. This work provides insights for OpenAI developers to enhance ChatGPT's support for language diversity and inclusion.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Semantic Aware Linear Transfer by Recycling Pre-trained Language Models for Cross-lingual Transfer

    cs.CL 2025-05 conditional novelty 6.0 of 10

    SALT initializes target-language embeddings in an LLM by linearly mapping embeddings from a target-language PLM into the LLM's space using semantically similar shared tokens.

  2. Improving LLM Outputs Against Jailbreak Attacks with Expert Model Integration

    cs.CR 2025-05 conditional novelty 4.0 of 10

    Injecting a fine-tuned BERT classifier's category label into LLM prompts improves accuracy on a 150-question automotive jailbreak benchmark, but the evaluation is self-referential and lacks external validation.

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