mlsanityData to 14 Oct 2026

Comprehensive Evaluation of ChatGPT Reliability Through Multilingual Inquiries

arXiv 2312.10524 ↗First posted 16 Dec 2023cs.SEcited by 11

On the map: LLM safety and security → Jailbreaking

Abstract (arXiv)

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.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Placed in Jailbreaking, one of 12 directions in LLM safety and security. Builds on 16 of its 37 references that are in the map. First posted on arXiv 16 Dec 2023.

Builds on

16 of 37 references.

Closest ancestors · 3

Same direction, newest first

  1. 2023Multilingual Jailbreak Challenges in Large Language Models10 Oct
  2. 2023GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts19 Sep
  3. 2023Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study23 May

Other references · 13

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  2. 2022Red Teaming Language Models with Language Modelscited by 1,026
  3. 2023A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivitycited by 894
  4. 2023Prompt Injection attack against LLM-integrated Applicationscited by 605
  5. 2023ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learningcited by 206
  6. 2023Is ChatGPT A Good Translator? Yes With GPT-4 As The Enginecited by 176
  7. 2022Language Contamination Helps Explain the Cross-lingual Capabilities of English Pretrained Modelscited by 92
  8. 2018DLFuzz: Differential Fuzzing Testing of Deep Learning Systemscited by 65
  9. 2018Evaluating Fuzz Testingcited by 32
  10. 2021RobOT: Robustness-Oriented Testing for Deep Learning Systemscited by 28
  11. 2023Safeguarding Crowdsourcing Surveys from ChatGPT with Prompt Injectioncited by 11
  12. 2021On the Relationship Between the Developer's Perceptible Race and Ethnicity and the Evaluation of Contributions in OSScited by 4
1 more
  1. 2021Women's Participation in Open Source Software: A Survey of the Literaturecited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 7; 2025: 2; 2026: 2.

11 papers.

  1. 2024Red-Teaming for Generative AI: Silver Bullet or Security Theater?cited by 83
  2. 2024Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontierscited by 51
  3. 2024RTP-LX: Can LLMs Evaluate Toxicity in Multilingual Scenarios?cited by 36
  4. 2024Sandwich attack: Multi-language Mixture Adaptive Attack on LLMscited by 20
  5. 2024Cognitive Overload Attack:Prompt Injection for Long Contextcited by 13
  6. 2024A Chinese Dataset for Evaluating the Safeguards in Large Language Modelscited by 12
  7. 2024Evaluating and Mitigating Linguistic Discrimination in Large Language Modelscited by 8
  8. 2025Semantic Aware Linear Transfer by Recycling Pre-trained Language Models for Cross-lingual Transfercited by 2
  9. 2025Improving LLM Outputs Against Jailbreak Attacks with Expert Model Integrationcited by 0
  10. 2026The "Knowledge-Behavior Gap" in Cultural Taboo Safety of Large Language Modelscited by 0
  11. 2026Language Chain in Alignment: Cross-lingual Ranking Preference Optimizationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024A Cross-Language Investigation into Jailbreak Attacks in Large Language Models
  2. 2024All in How You Ask for It: Simple Black-Box Method for Jailbreak Attacks
  3. 2024Characterizing and Evaluating the Reliability of LLMs against Jailbreak Attacks
  4. 2024Compromesso! Italian Many-Shot Jailbreaks Undermine the Safety of Large Language Models
  5. 2025Evaluation empirique de la s'ecurisation et de l'alignement de ChatGPT et Gemini: analyse comparative des vuln'erabilit'es par exp'erimentations de jailbreaks
  6. 2024A Comprehensive Study of Jailbreak Attack versus Defense for Large Language Models
  7. 2023"Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models
  8. 2026Multilingual jailbreaking of LLMs using low-resource languages