Fast Adversarial Attacks on Language Models In One GPU Minute

In this paper, we introduce a novel class of fast, beam search-based adversarial attack (BEAST) for Language Models (LMs). BEAST employs interpretable parameters, enabling attackers to balance between attack speed, success rate, and the readability of adversarial prompts. The computational efficiency of BEAST facilitates us to investigate its applications on LMs for jailbreaking, eliciting hallucinations, and privacy attacks. Our gradient-free targeted attack can jailbreak aligned LMs with high attack success rates within one minute. For instance, BEAST can jailbreak Vicuna-7B-v1.5 under one minute with a success rate of 89% when compared to a gradient-based baseline that takes over an hour to achieve 70% success rate using a single Nvidia RTX A6000 48GB GPU. Additionally, we discover a unique outcome wherein our untargeted attack induces hallucinations in LM chatbots. Through human evaluations, we find that our untargeted attack causes Vicuna-7B-v1.5 to produce ~15% more incorrect outputs when compared to LM outputs in the absence of our attack. We also learn that 22% of the time, BEAST causes Vicuna to generate outputs that are not relevant to the original prompt. Further, we use BEAST to generate adversarial prompts in a few seconds that can boost the performance of existing membership inference attacks for LMs. We believe that our fast attack, BEAST, has the potential to accelerate research in LM security and privacy. Our codebase is publicly available at https://github.com/vinusankars/BEAST.

Contrastive Self-Supervised Learning Based Approach for Patient Similarity: A Case Study on Atrial Fibrillation Detection from PPG Signal

In this paper, we propose a novel contrastive learning based deep learning framework for patient similarity search using physiological signals. We use a contrastive learning based approach to learn similar embeddings of patients with similar physiological signal data. We also introduce a number of neighbor selection algorithms to determine the patients with the highest similarity on the generated embeddings. To validate the effectiveness of our framework for measuring patient similarity, we select the detection of Atrial Fibrillation (AF) through photoplethysmography (PPG) signals obtained from smartwatch devices as our case study. We present extensive experimentation of our framework on a dataset of over 170 individuals and compare the performance of our framework with other baseline methods on this dataset.

Adversarial Robustness of Learning-based Static Malware Classifiers

Malware detection has long been a stage for an ongoing arms race between malware authors and anti-virus systems. Solutions that utilize machine learning (ML) gain traction as the scale of this arms race increases. This trend, however, makes performing attacks directly on ML an attractive prospect for adversaries. We study this arms race from both perspectives in the context of MalConv, a popular convolutional neural network-based malware classifier that operates on raw bytes of files. First, we show that MalConv is vulnerable to adversarial patch attacks: appending a byte-level patch to malware files bypasses detection 94.3% of the time. Moreover, we develop a universal adversarial patch (UAP) attack where a single patch can drop the detection rate in constant time of any malware file that contains it by 80%. These patches are effective even being relatively small with respect to the original file size -- between 2%-8%. As a countermeasure, we then perform window ablation that allows us to apply de-randomized smoothing, a modern certified defense to patch attacks in vision tasks, to raw files. The resulting `smoothed-MalConv' can detect over 80% of malware that contains the universal patch and provides certified robustness up to 66%, outlining a promising step towards robust malware detection. To our knowledge, we are the first to apply universal adversarial patch attack and certified defense using ablations on byte level in the malware field.