Generating Less Certain Adversarial Examples Improves Robust Generalization

Recent studies have shown that deep neural networks are vulnerable to adversarial examples. Numerous defenses have been proposed to improve model robustness, among which adversarial training is most successful. In this work, we revisit the robust overfitting phenomenon. In particular, we argue that overconfident models produced during adversarial training could be a potential cause, supported by the empirical observation that the predicted labels of adversarial examples generated by models with better robust generalization ability tend to have significantly more even distributions. Based on the proposed definition of adversarial certainty, we incorporate an extragradient step in the adversarial training framework to search for models that can generate adversarially perturbed inputs with lower certainty, further improving robust generalization. Our approach is general and can be easily combined with other variants of adversarial training methods. Extensive experiments on image benchmarks demonstrate that our method effectively alleviates robust overfitting and is able to produce models with consistently improved robustness.

"Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models

The misuse of large language models (LLMs) has garnered significant attention from the general public and LLM vendors. In response, efforts have been made to align LLMs with human values and intent use. However, a particular type of adversarial prompts, known as jailbreak prompt, has emerged and continuously evolved to bypass the safeguards and elicit harmful content from LLMs. In this paper, we conduct the first measurement study on jailbreak prompts in the wild, with 6,387 prompts collected from four platforms over six months. Leveraging natural language processing technologies and graph-based community detection methods, we discover unique characteristics of jailbreak prompts and their major attack strategies, such as prompt injection and privilege escalation. We also observe that jailbreak prompts increasingly shift from public platforms to private ones, posing new challenges for LLM vendors in proactive detection. To assess the potential harm caused by jailbreak prompts, we create a question set comprising 46,800 samples across 13 forbidden scenarios. Our experiments show that current LLMs and safeguards cannot adequately defend jailbreak prompts in all scenarios. Particularly, we identify two highly effective jailbreak prompts which achieve 0.99 attack success rates on ChatGPT (GPT-3.5) and GPT-4, and they have persisted online for over 100 days. Our work sheds light on the severe and evolving threat landscape of jailbreak prompts. We hope our study can facilitate the research community and LLM vendors in promoting safer and regulated LLMs.

Mondrian: Prompt Abstraction Attack Against Large Language Models for Cheaper API Pricing

The Machine Learning as a Service (MLaaS) market is rapidly expanding and becoming more mature. For example, OpenAI's ChatGPT is an advanced large language model (LLM) that generates responses for various queries with associated fees. Although these models can deliver satisfactory performance, they are far from perfect. Researchers have long studied the vulnerabilities and limitations of LLMs, such as adversarial attacks and model toxicity. Inevitably, commercial ML models are also not exempt from such issues, which can be problematic as MLaaS continues to grow. In this paper, we discover a new attack strategy against LLM APIs, namely the prompt abstraction attack. Specifically, we propose Mondrian, a simple and straightforward method that abstracts sentences, which can lower the cost of using LLM APIs. In this approach, the adversary first creates a pseudo API (with a lower established price) to serve as the proxy of the target API (with a higher established price). Next, the pseudo API leverages Mondrian to modify the user query, obtain the abstracted response from the target API, and forward it back to the end user. Our results show that Mondrian successfully reduces user queries' token length ranging from 13% to 23% across various tasks, including text classification, generation, and question answering. Meanwhile, these abstracted queries do not significantly affect the utility of task-specific and general language models like ChatGPT. Mondrian also reduces instruction prompts' token length by at least 11% without compromising output quality. As a result, the prompt abstraction attack enables the adversary to profit without bearing the cost of API development and deployment.

Generative Watermarking Against Unauthorized Subject-Driven Image Synthesis

Large text-to-image models have shown remarkable performance in synthesizing high-quality images. In particular, the subject-driven model makes it possible to personalize the image synthesis for a specific subject, e.g., a human face or an artistic style, by fine-tuning the generic text-to-image model with a few images from that subject. Nevertheless, misuse of subject-driven image synthesis may violate the authority of subject owners. For example, malicious users may use subject-driven synthesis to mimic specific artistic styles or to create fake facial images without authorization. To protect subject owners against such misuse, recent attempts have commonly relied on adversarial examples to indiscriminately disrupt subject-driven image synthesis. However, this essentially prevents any benign use of subject-driven synthesis based on protected images. In this paper, we take a different angle and aim at protection without sacrificing the utility of protected images for general synthesis purposes. Specifically, we propose GenWatermark, a novel watermark system based on jointly learning a watermark generator and a detector. In particular, to help the watermark survive the subject-driven synthesis, we incorporate the synthesis process in learning GenWatermark by fine-tuning the detector with synthesized images for a specific subject. This operation is shown to largely improve the watermark detection accuracy and also ensure the uniqueness of the watermark for each individual subject. Extensive experiments validate the effectiveness of GenWatermark, especially in practical scenarios with unknown models and text prompts (74% Acc.), as well as partial data watermarking (80% Acc. for 1/4 watermarking). We also demonstrate the robustness of GenWatermark to two potential countermeasures that substantially degrade the synthesis quality.

Generated Graph Detection

Graph generative models become increasingly effective for data distribution approximation and data augmentation. While they have aroused public concerns about their malicious misuses or misinformation broadcasts, just as what Deepfake visual and auditory media has been delivering to society. Hence it is essential to regulate the prevalence of generated graphs. To tackle this problem, we pioneer the formulation of the generated graph detection problem to distinguish generated graphs from real ones. We propose the first framework to systematically investigate a set of sophisticated models and their performance in four classification scenarios. Each scenario switches between seen and unseen datasets/generators during testing to get closer to real-world settings and progressively challenge the classifiers. Extensive experiments evidence that all the models are qualified for generated graph detection, with specific models having advantages in specific scenarios. Resulting from the validated generality and oblivion of the classifiers to unseen datasets/generators, we draw a safe conclusion that our solution can sustain for a decent while to curb generated graph misuses.

Unsafe Diffusion: On the Generation of Unsafe Images and Hateful Memes From Text-To-Image Models

State-of-the-art Text-to-Image models like Stable Diffusion and DALLE$\cdot$2 are revolutionizing how people generate visual content. At the same time, society has serious concerns about how adversaries can exploit such models to generate unsafe images. In this work, we focus on demystifying the generation of unsafe images and hateful memes from Text-to-Image models. We first construct a typology of unsafe images consisting of five categories (sexually explicit, violent, disturbing, hateful, and political). Then, we assess the proportion of unsafe images generated by four advanced Text-to-Image models using four prompt datasets. We find that these models can generate a substantial percentage of unsafe images; across four models and four prompt datasets, 14.56% of all generated images are unsafe. When comparing the four models, we find different risk levels, with Stable Diffusion being the most prone to generating unsafe content (18.92% of all generated images are unsafe). Given Stable Diffusion's tendency to generate more unsafe content, we evaluate its potential to generate hateful meme variants if exploited by an adversary to attack a specific individual or community. We employ three image editing methods, DreamBooth, Textual Inversion, and SDEdit, which are supported by Stable Diffusion. Our evaluation result shows that 24% of the generated images using DreamBooth are hateful meme variants that present the features of the original hateful meme and the target individual/community; these generated images are comparable to hateful meme variants collected from the real world. Overall, our results demonstrate that the danger of large-scale generation of unsafe images is imminent. We discuss several mitigating measures, such as curating training data, regulating prompts, and implementing safety filters, and encourage better safeguard tools to be developed to prevent unsafe generation.

Two-in-One: A Model Hijacking Attack Against Text Generation Models

Machine learning has progressed significantly in various applications ranging from face recognition to text generation. However, its success has been accompanied by different attacks. Recently a new attack has been proposed which raises both accountability and parasitic computing risks, namely the model hijacking attack. Nevertheless, this attack has only focused on image classification tasks. In this work, we broaden the scope of this attack to include text generation and classification models, hence showing its broader applicability. More concretely, we propose a new model hijacking attack, Ditto, that can hijack different text classification tasks into multiple generation ones, e.g., language translation, text summarization, and language modeling. We use a range of text benchmark datasets such as SST-2, TweetEval, AGnews, QNLI, and IMDB to evaluate the performance of our attacks. Our results show that by using Ditto, an adversary can successfully hijack text generation models without jeopardizing their utility.

In ChatGPT We Trust? Measuring and Characterizing the Reliability of ChatGPT

The way users acquire information is undergoing a paradigm shift with the advent of ChatGPT. Unlike conventional search engines, ChatGPT retrieves knowledge from the model itself and generates answers for users. ChatGPT's impressive question-answering (QA) capability has attracted more than 100 million users within a short period of time but has also raised concerns regarding its reliability. In this paper, we perform the first large-scale measurement of ChatGPT's reliability in the generic QA scenario with a carefully curated set of 5,695 questions across ten datasets and eight domains. We find that ChatGPT's reliability varies across different domains, especially underperforming in law and science questions. We also demonstrate that system roles, originally designed by OpenAI to allow users to steer ChatGPT's behavior, can impact ChatGPT's reliability. We further show that ChatGPT is vulnerable to adversarial examples, and even a single character change can negatively affect its reliability in certain cases. We believe that our study provides valuable insights into ChatGPT's reliability and underscores the need for strengthening the reliability and security of large language models (LLMs).

FACE-AUDITOR: Data Auditing in Facial Recognition Systems

Few-shot-based facial recognition systems have gained increasing attention due to their scalability and ability to work with a few face images during the model deployment phase. However, the power of facial recognition systems enables entities with moderate resources to canvas the Internet and build well-performed facial recognition models without people's awareness and consent. To prevent the face images from being misused, one straightforward approach is to modify the raw face images before sharing them, which inevitably destroys the semantic information, increases the difficulty of retroactivity, and is still prone to adaptive attacks. Therefore, an auditing method that does not interfere with the facial recognition model's utility and cannot be quickly bypassed is urgently needed. In this paper, we formulate the auditing process as a user-level membership inference problem and propose a complete toolkit FACE-AUDITOR that can carefully choose the probing set to query the few-shot-based facial recognition model and determine whether any of a user's face images is used in training the model. We further propose to use the similarity scores between the original face images as reference information to improve the auditing performance. Extensive experiments on multiple real-world face image datasets show that FACE-AUDITOR can achieve auditing accuracy of up to $99\%$. Finally, we show that FACE-AUDITOR is robust in the presence of several perturbation mechanisms to the training images or the target models. The source code of our experiments can be found at \url{https://github.com/MinChen00/Face-Auditor}.

MGTBench: Benchmarking Machine-Generated Text Detection

Nowadays large language models (LLMs) have shown revolutionary power in a variety of natural language processing (NLP) tasks such as text classification, sentiment analysis, language translation, and question-answering. In this way, detecting machine-generated texts (MGTs) is becoming increasingly important as LLMs become more advanced and prevalent. These models can generate human-like language that can be difficult to distinguish from text written by a human, which raises concerns about authenticity, accountability, and potential bias. However, existing detection methods against MGTs are evaluated under different model architectures, datasets, and experimental settings, resulting in a lack of a comprehensive evaluation framework across different methodologies In this paper, we fill this gap by proposing the first benchmark framework for MGT detection, named MGTBench. Extensive evaluations on public datasets with curated answers generated by ChatGPT (the most representative and powerful LLMs thus far) show that most of the current detection methods perform less satisfactorily against MGTs. An exceptional case is ChatGPT Detector, which is trained with ChatGPT-generated texts and shows great performance in detecting MGTs. Nonetheless, we note that only a small fraction of adversarial-crafted perturbations on MGTs can evade the ChatGPT Detector, thus highlighting the need for more robust MGT detection methods. We envision that MGTBench will serve as a benchmark tool to accelerate future investigations involving the evaluation of state-of-the-art MGT detection methods on their respective datasets and the development of more advanced MGT detection methods. Our source code and datasets are available at https://github.com/xinleihe/MGTBench.