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Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study

Written for the Research library (claude-opus-5-5) from the paper text. Original — arxiv.org/pdf/2305.13860

Summary

The paper examines how prompt engineering alone can bypass the safety restrictions OpenAI places on ChatGPT. Because the model is closed-source, the authors treat jailbreaking as a black-box problem. They study which kinds of jailbreak prompts exist, how well they work, and how strongly GPT-3.5-Turbo and GPT-4 resist them. They gathered 78 verified prompts from the jailbreakchat website, collected between February and late April 2023, and used them to build a taxonomy of 3 types and 10 patterns. Each prompt was paired with questions covering 8 scenarios prohibited by OpenAI's usage policy, and the authors queried both model versions at scale. Jailbreaks succeeded widely. Effectiveness depended strongly on prompt structure, and prompts combining several techniques, especially privilege escalation, performed best. GPT-4 resisted more than GPT-3.5-Turbo, and protection strength varied by scenario. The work matters because it supplies an open dataset and a systematic baseline for studying LLM jailbreaks. It also argues that content policies should be better aligned with real-world law and ethics.

Key points

  • Dataset and taxonomy: 78 real-world jailbreak prompts, released openly on the project site (llm-jailbreak-study), were classified into 10 patterns grouped into 3 types. The types are pretending (97.44% of prompts), privilege escalation (17.96%) and attention shifting (6.41%). A single prompt can use more than one technique.
  • Evaluation scale: The study used 3,120 jailbreak questions across 8 prohibited scenarios, which expanded into 40 use-case scenarios. In total, 31,200 queries were sent to GPT-3.5-Turbo and GPT-4.
  • Success rate: Jailbreaks worked in 86.3% of the tested real-world scenarios, and prompts consistently evaded restrictions across all 40 of them.
  • Model differences: GPT-4 provided stronger protection than GPT-3.5-Turbo, and resistance differed depending on the content-policy area.
  • Prompt evolution: Analysis of prompt histories suggests that jailbreak prompts became more sophisticated and more effective over time.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Other

Cited by

168 papers of our library cite it (of the 140673 whose bibliographies we read); 15 of them have a page here, 153 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2305.13860 · arXiv 2305.13860

@misc{liu2023jailbreaking,
  title = {{Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study}},
  author = {Liu, Yi and Deng, Gelei and Xu, Zhengzi and Li, Yuekang and Zheng, Yaowen and Zhang, Ying and Zhao, Lida and Zhang, Tianwei and Wang, Kailong and Liu, Yang},
  year = {2023},
  doi = {10.48550/arxiv.2305.13860},
  eprint = {2305.13860},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2305.13860}
}

References

13 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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