While Large Language Models (LLMs) display versatile functionality, they
continue to generate harmful, biased, and toxic content, as demonstrated by the
prevalence of human-designed jailbreaks. In this work, we present Tree of
Attacks with Pruning (TAP), an automated method for generating jailbreaks that
only requires black-box access to the target LLM. TAP utilizes an LLM to
iteratively refine candidate (attack) prompts using tree-of-thoughts reasoning
until one of the generated prompts jailbreaks the target. Crucially, before
sending prompts to the target, TAP assesses them and prunes the ones unlikely
to result in jailbreaks. Using tree-of-thought reasoning allows TAP to navigate
a large search space of prompts and pruning reduces the total number of queries
sent to the target. In empirical evaluations, we observe that TAP generates
prompts that jailbreak state-of-the-art LLMs (including GPT4 and GPT4-Turbo)
for more than 80% of the prompts using only a small number of queries. This
significantly improves upon the previous state-of-the-art black-box method for
generating jailbreaks.

使用大型语言模型和树状攻击剪枝自动生成越狱（黑盒攻击）的方法，有效地生成恶意、偏见和有毒内容。

攻击之树：自动破解黑盒 LLM

Tree of Attacks: Jailbreaking Black-Box LLMs Automatically

Media organizations bear great reponsibility because of their considerable
influence on shaping beliefs and positions of our society. Any form of media
can contain overly biased content, e.g., by reporting on political events in a
selective or incomplete manner. A relevant question hence is whether and how
such form of imbalanced news coverage can be exposed. The research presented in
this paper addresses not only the automatic detection of bias but goes one step
further in that it explores how political bias and unfairness are manifested
linguistically. In this regard we utilize a new corpus of 6964 news articles
with labels derived from adfontesmedia.com and develop a neural model for bias
assessment. By analyzing this model on article excerpts, we find insightful
bias patterns at different levels of text granularity, from single words to the
whole article discourse.

本研究利用一个新的、基于 6964 篇新闻文章的辅助指标标签的语料库，开发了一种神经模型对新闻文章中的政治偏见和不公平进行分析，并从单词到整篇文章的不同层次的文本粒度发现了深刻的偏见模式。