In this paper, we study the harmlessness alignment problem of multimodal
large language models~(MLLMs). We conduct a systematic empirical analysis of
the harmlessness performance of representative MLLMs and reveal that the image
input poses the alignment vulnerability of MLLMs. Inspired by this, we propose
a novel jailbreak method named HADES, which hides and amplifies the harmfulness
of the malicious intent within the text input, using meticulously crafted
images. Experimental results show that HADES can effectively jailbreak existing
MLLMs, which achieves an average Attack Success Rate~(ASR) of 90.26% for
LLaVA-1.5 and 71.60% for Gemini Pro Vision. Our code and data will be publicly
released.

研究了多模态大型语言模型（MLLMs）的无害对齐问题，通过对代表性 MLLMs 的无害性能进行系统的实证分析，揭示了图像输入对 MLLMs 的对准易受攻击的弱点。在此基础上，提出了一种名为 HADES 的新型越狱方法，利用精心制作的图像隐藏和放大文本输入中的恶意意图的有害性。实验结果表明，HADES 能够有效地越狱现有的 MLLMs，其中对于 LLaVA-1.5 平均攻击成功率（ASR）为 90.26%，对于 Gemini Pro Vision 为 71.60%。我们的代码和数据将会公开发布。

图像是对齐的弱点：利用视觉漏洞对跨模态大型语言模型进行越狱

Images are Achilles' Heel of Alignment: Exploiting Visual  Vulnerabilities for Jailbreaking Multimodal Large Language Models

We introduce Hades, an unsupervised algorithm to detect singularities in
data. This algorithm employs a kernel goodness-of-fit test, and as a
consequence it is much faster and far more scaleable than the existing
topology-based alternatives. Using tools from differential geometry and optimal
transport theory, we prove that Hades correctly detects singularities with high
probability when the data sample lives on a transverse intersection of
equidimensional manifolds. In computational experiments, Hades recovers
singularities in synthetically generated data, branching points in road network
data, intersection rings in molecular conformation space, and anomalies in
image data.

Hades 是一种无监督算法，可通过核拟合度检验快速而可扩展地检测数据中的奇点。通过应用微分几何和最优传输理论，我们证明当数据样本生存在等维度流形的横交集上时，Hades 能够以很高的概率正确地检测奇点。在计算实验中，Hades 在合成生成的数据、道路网络数据中的分叉点、分子构象空间中的交叉环以及图像数据中的异常情况中成功地恢复出奇点。

HADES：基于本地度量比较的快速奇异性检测

HADES: Fast Singularity Detection with Local Measure Comparison

This paper introduces HADES, a novel tool for automatic comparative documents
with similar structures. HADES is designed to streamline the work of
professionals dealing with large volumes of documents, such as policy
documents, legal acts, and scientific papers. The tool employs a multi-step
pipeline that begins with processing PDF documents using topic modeling,
summarization, and analysis of the most important words for each topic. The
process concludes with an interactive web app with visualizations that
facilitate the comparison of the documents. HADES has the potential to
significantly improve the productivity of professionals dealing with high
volumes of documents, reducing the time and effort required to complete tasks
related to comparative document analysis. Our package is publically available
on GitHub.

HADES 是一种自动文档比较工具，通过主题建模、文摘、关键词分析等多个步骤，使政策文件、法律法规和科学论文等大规模文献比较工作更加高效，同时具有交互式的 Web 应用程序和可视化功能，可公开访问 GitHub。