JailbreakEval: An Integrated Toolkit for Evaluating Jailbreak Attempts Against Large Language Models
Delong Ran, Jinyuan Liu, Yichen Gong, Jingyi Zheng, Xinlei He,, Tianshuo Cong, Anyu Wang

TL;DR
JailbreakEval is a comprehensive toolkit designed to standardize and simplify the evaluation of jailbreak attacks on large language models, addressing the lack of consensus and diverse methodologies in current research.
Contribution
The paper provides a systematic taxonomy of jailbreak evaluators and introduces JailbreakEval, an integrated toolkit that streamlines and standardizes jailbreak assessment processes.
Findings
Analyzed nearly 90 jailbreak studies to identify evaluation methodologies.
Developed JailbreakEval with multiple evaluators for flexible testing.
Facilitates consistent comparison of jailbreak methods and defenses.
Abstract
Jailbreak attacks induce Large Language Models (LLMs) to generate harmful responses, posing severe misuse threats. Though research on jailbreak attacks and defenses is emerging, there is no consensus on evaluating jailbreaks, i.e., the methods to assess the harmfulness of an LLM's response are varied. Each approach has its own set of strengths and weaknesses, impacting their alignment with human values, as well as the time and financial cost. This diversity challenges researchers in choosing suitable evaluation methods and comparing different attacks and defenses. In this paper, we conduct a comprehensive analysis of jailbreak evaluation methodologies, drawing from nearly 90 jailbreak research published between May 2023 and April 2024. Our study introduces a systematic taxonomy of jailbreak evaluators, offering indepth insights into their strengths and weaknesses, along with the current…
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Taxonomy
TopicsDeception detection and forensic psychology
MethodsSparse Evolutionary Training · Residual Connection · Softmax · Layer Normalization · Byte Pair Encoding · Label Smoothing · Adam · Attention Is All You Need · Linear Layer · Multi-Head Attention
