How Robust is Google's Bard to Adversarial Image Attacks? (2309.11751v2)

Abstract: Multimodal LLMs (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. Update: GPT-4V is available at October 2023. We further evaluate its robustness under the same set of adversarial examples, achieving a 45% attack success rate.

Adversarial Robustness of Google's Bard in Image Processing Tasks

The paper titled "How Robust is Google's Bard to Adversarial Image Attacks?" by Dong et al. offers an assessment of the vulnerability of Google's Bard—a prominent Multimodal LLM (MLLM) in the domain of text and vision integration. This investigation specifically explores the susceptibility of Bard to adversarial image attacks, a critical concern as MLLMs become more prevalent in commercial applications.

Key Insights and Observations

1. Adversarial Vulnerability of MLLMs: The paper identifies an inherent vulnerability in multimodal systems like Bard, which, due to their reliance on vision models, are susceptible to adversarial perturbations. These perturbations can cause incorrect image processing, leading to erroneous outputs, posing a substantial threat to security and safety.
2. Attack Success and Transferability: The authors creatively developed adversarial examples using surrogate models that can deceive Bard with an attack success rate of up to 22%. These examples exhibit significant transferability, successfully misleading other commercial multimodal systems like Bing Chat and ERNIE Bot with success rates of 26% and 86%, respectively, and a 45% success rate against GPT-4V.
3. Defense Mechanisms of Bard: Bard ostensibly utilizes face detection and toxicity detection mechanisms as safeguards. However, the paper demonstrates that these defenses are not impervious to adversarial attacks. By generating specific adversarial examples, the authors achieved evasion of Bard's defenses, undermining its capacity to prevent face and toxicity misclassification.

Implications for Future Research and Applications

The findings from this paper cast a spotlight on several critical areas needing attention within AI research and development:

• Model Robustness: As MLLMs grow in notoriety and scale, improving robustness against adversarial attacks becomes imperative. This requires innovations in adversarial training paradigms, possibly integrating more sophisticated pre-processing defenses or architectural enhancements designed to withstand perturbations.
• Defense Strategies: The realization that certain defense strategies (such as face and toxicity detection) are susceptible to adversarial examples prompts a need for more dynamic and adaptable defense mechanisms that can accommodate variability in image perturbations.
• Security Concerns in Commercial AI: As these vulnerabilities are demonstrated in widely used commercial models, it accentuates the importance of robust security analysis and mitigation strategies in the deployment of AI systems in real-world environments.

Conclusion

In summarizing, the research conducted by Dong et al. exposes critical gaps in the adversarial robustness of Google's Bard among other MLLMs, prompting crucial considerations for security in AI deployments. By detailing specific vulnerabilities and offering a perspective on attack strategies, the work lays foundational groundwork for subsequent investigations aimed at bolstering the resilience of AI models against adversarial influences. The implications extend beyond academic inquiry, pressing upon developers and companies to prioritize robustness in their AI technologies.