Jailbreak attacks aim to bypass the LLMs' safeguards.While researchers have proposed different jailbreak attacks in depth, they have done so in isolation-either with unaligned settings or comparing a limited range of methods.To fill this gap, we present a large-scale evaluation of various jailbreak attacks.We collect 17 representative jailbreak attacks, summarize their features, and establish a novel jailbreak attack taxonomy.Then we conduct comprehensive measurement and ablation studies across nine aligned LLMs on 160 forbidden questions from 16 violation categories.Also, we test jailbreak attacks under eight advanced defenses.Based on our taxonomy and experiments, we identify some important patterns, such as heuristicbased attacks could achieve high attack success rates but are easy to mitigate by defenses, causing low practicality.Our study offers valuable insights for future research on jailbreak attacks and defenses.We hope our work could help the community avoid incremental work and serve as an effective benchmark tool for practitioners.

Generative models have garnered significant interest in the realm of machine learning but are costly to produce and face growing regulatory constraints, requiring resource-heavy training and collaboration with various stakeholders, especially data providers. Such collaborative environments have given rise to a new threat known as model hijacking attacks. Adversaries can tamper with the training process to embed a hidden task, so that train/hijack high-end models at minimal costs or even sidestep regulations. In this paper, we extend the scope of model hijacking from classifiers to generative models by introducing the first model hijacking attack tailored for Generative Adversarial Networks (GANs), namely Neeko. Neeko is based on a novel U-Net-based Disguiser and allows a compromised GAN to generate authentic-looking images from its original distribution, but when downscaled, these images are visually changed to be from the hijacking dataset distribution. Through experiments on different image benchmark datasets, we demonstrate the efficacy and stealthiness of Neeko. Neeko poses security and accountability risks associated with training public GANs on potentially malicious or illegal datasets and raises concerns about evading those regulations addressing deepfakes and synthetic images.

Significant advancements have recently been made in large language models, represented by GPT models.Users frequently have multiround private conversations with cloud-hosted GPT models for task optimization.Yet, this operational paradigm introduces additional attack surfaces, particularly in custom GPTs and hijacked chat sessions.In this paper, we introduce a straightforward yet potent Conversation Reconstruction Attack.This attack targets the contents of previous conversations between GPT models and benign users, i.e., the benign users' input contents during their interaction with GPT models.The adversary could induce GPT models to leak such contents by querying them with designed malicious prompts.Our comprehensive examination of privacy risks during the interactions with GPT models under this attack reveals GPT-4's considerable resilience.We present two advanced attacks targeting improved reconstruction of past conversations, demonstrating significant privacy leakage across all models under these advanced techniques.Evaluating various defense mechanisms, we find them ineffective against these attacks.Our findings highlight the ease with which privacy can be compromised in interactions with GPT models, urging the community to safeguard against potential abuses of these models' capabilities.

Esports, short for electronic sports, is a form of competition using video games and has attracted more than 530 million audiences worldwide. To watch esports, people utilize online livestreaming platforms. Recently, a novel interaction method, namely "bullet chats," has been introduced on these platforms. Different from conventional comments, bullet chats are scrolling comments posted by audiences that are synchronized to the livestreaming timeline, enabling audiences to share and communicate their immediate perspectives. The real-time nature of bullet chats, therefore, brings a new perspective to esports analysis. In this paper, we conduct the first empirical study on the bullet chats for esports, focusing on one of the most popular video games, i.e., League of Legends (LoL). Specifically, we collect 21 million bullet chats of LoL from Jan. 2023 to Mar. 2023 across two mainstream platforms (Bilibili and Huya). By performing quantitative analysis, we reveal how the quantity and toxicity of bullet chats distribute (and change) w.r.t. three aspects, i.e., the season, the team, and the match. Our findings show that teams with higher rankings tend to attract a greater quantity of bullet chats, and these chats are often characterized by a higher degree of toxicity. We then utilize topic modeling to identify topics among bullet chats. Interestingly, we find that a considerable portion of topics (14.14% on Bilibili and 22.94% on Huya) discuss themes beyond the game, including genders, entertainment stars, non-esports athletes, and so on. Besides, by further modeling topics on toxic bullet chats, we find hateful speech targeting different social groups, ranging from professions, regions, etc. To the best of our knowledge, this work is the first measurement of bullet chats on esports livestreaming. We believe our study can shed light on esports research from the perspective of bullet chats.

The information explosion drives enterprises and individuals to rent cloud computing infrastructure for their applications in the cloud. However, the agreements between cloud computing providers and clients are often inefficient. We propose an agent-based auto-negotiation system for resource scheduling using fuzzy logic. Our method completes a one-to-one auto-negotiation process and generates optimal offers for providers and clients. We compare the impact of different member functions, fuzzy rule sets, and negotiation scenarios on the offers to optimize the system. Our proposed method efficiently utilizes resources and offers interpretability, high flexibility, and customization. We successfully train machine learning models to replace the fuzzy negotiation system, improving processing speed. The article also highlights potential future improvements to the proposed system and machine learning models. All codes and data are available as an open source repository.