Tricking LLMs into Disobedience: Formalizing, Analyzing, and Detecting Jailbreaks
Abhinav Sukumar Rao, Atharva Roshan Naik, Sachin Vashistha, Somak Aditya, Monojit Choudhury
Abstract
Recent explorations with commercial Large Language Models (LLMs) have shown that non-expert users can jailbreak LLMs by simply manipulating their prompts; resulting in degenerate output behavior, privacy and security breaches, offensive outputs, and violations of content regulator policies. Limited studies have been conducted to formalize and analyze these attacks and their mitigations. We bridge this gap by proposing a formalism and a taxonomy of known (and possible) jailbreaks. We survey existing jailbreak methods and their effectiveness on open-source and commercial LLMs (such as GPT-based models, OPT, BLOOM, and FLAN-T5-XXL). We further discuss the challenges of jailbreak detection in terms of their effectiveness against known attacks. For further analysis, we release a dataset of model outputs across 3700 jailbreak prompts over 4 tasks.
BibTeX
@inproceedings{rao-etal-2024-tricking,
title = "Tricking {LLM}s into Disobedience: Formalizing, Analyzing, and Detecting Jailbreaks",
author = "Rao, Abhinav Sukumar and
Naik, Atharva Roshan and
Vashistha, Sachin and
Aditya, Somak and
Choudhury, Monojit",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1462/",
pages = "16802--16830"
}