How Robust is Google's Bard to Adversarial Image Attacks?

Multimodal Large Language Models (MLLMs) that integrate text and other modalities (especially vision) have achieved unprecedented performance in various multimodal tasks. However, due to the unsolved adversarial robustness problem of vision models, MLLMs can have more severe safety and security risks by introducing the vision inputs. In this work, we study the adversarial robustness of Google's Bard, a competitive chatbot to ChatGPT that released its multimodal capability recently, to better understand the vulnerabilities of commercial MLLMs. By attacking white-box surrogate vision encoders or MLLMs, the generated adversarial examples can mislead Bard to output wrong image descriptions with a 22% success rate based solely on the transferability. We show that the adversarial examples can also attack other MLLMs, e.g., a 26% attack success rate against Bing Chat and a 86% attack success rate against ERNIE bot. Moreover, we identify two defense mechanisms of Bard, including face detection and toxicity detection of images. We design corresponding attacks to evade these defenses, demonstrating that the current defenses of Bard are also vulnerable. We hope this work can deepen our understanding on the robustness of MLLMs and facilitate future research on defenses. Our code is available at https://github.com/thu-ml/Attack-Bard.

Approximate better, Attack stronger: Adversarial Example Generation via Asymptotically Gaussian Mixture Distribution

Strong adversarial examples are the keys to evaluating and enhancing the robustness of deep neural networks. The popular adversarial attack algorithms maximize the non-concave loss function using the gradient ascent. However, the performance of each attack is usually sensitive to, for instance, minor image transformations due to insufficient information (only one input example, few white-box source models and unknown defense strategies). Hence, the crafted adversarial examples are prone to overfit the source model, which limits their transferability to unidentified architectures. In this paper, we propose Multiple Asymptotically Normal Distribution Attacks (MultiANDA), a novel method that explicitly characterizes adversarial perturbations from a learned distribution. Specifically, we approximate the posterior distribution over the perturbations by taking advantage of the asymptotic normality property of stochastic gradient ascent (SGA), then apply the ensemble strategy on this procedure to estimate a Gaussian mixture model for a better exploration of the potential optimization space. Drawing perturbations from the learned distribution allow us to generate any number of adversarial examples for each input. The approximated posterior essentially describes the stationary distribution of SGA iterations, which captures the geometric information around the local optimum. Thus, the samples drawn from the distribution reliably maintain the transferability. Our proposed method outperforms nine state-of-the-art black-box attacks on deep learning models with or without defenses through extensive experiments on seven normally trained and seven defence models.