PBI-Attack: Prior-Guided Bimodal Interactive Black-Box Jailbreak Attack for Toxicity Maximization (2412.05892v3)

Abstract: Understanding the vulnerabilities of Large Vision LLMs (LVLMs) to jailbreak attacks is essential for their responsible real-world deployment. Most previous work requires access to model gradients, or is based on human knowledge (prompt engineering) to complete jailbreak, and they hardly consider the interaction of images and text, resulting in inability to jailbreak in black box scenarios or poor performance. To overcome these limitations, we propose a Prior-Guided Bimodal Interactive Black-Box Jailbreak Attack for toxicity maximization, referred to as PBI-Attack. Our method begins by extracting malicious features from a harmful corpus using an alternative LVLM and embedding these features into a benign image as prior information. Subsequently, we enhance these features through bidirectional cross-modal interaction optimization, which iteratively optimizes the bimodal perturbations in an alternating manner through greedy search, aiming to maximize the toxicity of the generated response. The toxicity level is quantified using a well-trained evaluation model. Experiments demonstrate that PBI-Attack outperforms previous state-of-the-art jailbreak methods, achieving an average attack success rate of 92.5% across three open-source LVLMs and around 67.3% on three closed-source LVLMs. Disclaimer: This paper contains potentially disturbing and offensive content.

• The paper introduces a dual-stage methodology that integrates image perturbations with text prompts to maximize toxicity in LVLMs.
• It achieves an impressive 92.5% attack success rate on open-source models, outperforming traditional jailbreak methods.
• The study highlights critical security vulnerabilities in multimodal AI systems, urging the development of adaptive defense strategies.

An Expert Analysis of "PBI-Attack: Prior-Guided Bimodal Interactive Black-Box Jailbreak Attack for Toxicity Maximization"

The paper "PBI-Attack: Prior-Guided Bimodal Interactive Black-Box Jailbreak Attack for Toxicity Maximization" presents a novel approach to exploiting vulnerabilities in Large Vision LLMs (LVLMs) through a black-box jailbreak attack, referred to as PBI-Attack. The primary objective of this attack is to maximize the toxicity output of these models, which is essential for understanding potential risks associated with their deployment in real-world applications.

Methodology Overview

The authors introduce a two-stage method that effectively combines image and text modalities to execute a jailbreak attack in a black-box setting. This approach addresses the limitations of previous methods that either relied heavily on white-box access or focused purely on prompt engineering and unimodal attacks.

1. Prior Perturbation Generation:
   • A unique element of this methodology is the integration of malicious features from a harmful corpus into a benign image via an alternative LVLM. This stage employs Projected Gradient Descent (PGD) to generate perturbations that are superimposed onto the image, thereby encoding potential toxic behavior.
2. Bimodal Adversarial Optimization Loop:
   • Following the initial image perturbation, an iterative optimization loop refines the adversarial inputs. The optimization process involves alternating updates between text prompt suffixes and image perturbations, guided by a toxicity scoring mechanism sourced from Perspective API or other evaluation models.

Numerical Results

The empirical evaluation of the PBI-Attack method stands out particularly for its high Attack Success Rate (ASR). The experiments demonstrated an impressive ASR of 92.5% on open-source LVLMs, as compared to around 67.3% on closed-source models. These results highlight the robustness and transferability of the method, which outperforms existing jailbreak attacks, such as UMK, GCG, and Arondight, in both open and closed-source environments.

Discussion

The implications of this research are multifaceted. Practically, it sheds light on the security vulnerabilities of LVLMs, emphasizing the need for stronger defense mechanisms and robust model training techniques to mitigate the risks of malicious exploitation. Theoretically, it paves the way for further exploration into cross-modal threat vectors, which could extend to other machine learning systems integrating varied data inputs.

Moreover, the success of PBI-Attack in black-box settings suggests that adversaries can effectively subvert systems even without direct model access, which poses significant challenges for secure model deployment. This underscores an urgent call for the development of adaptive security frameworks that can dynamically respond to such sophisticated attacks.

Future Developments

Looking forward, the approach introduced in this paper could inspire advancements in counter-jailbreak mechanisms that leverage multi-modal robustness strategies, potentially incorporating real-time monitoring and adaptive retraining to detect and counteract adversarial inputs.

Additionally, the adaptability of PBI-Attack across different LVLM architectures suggests potential applications beyond toxicity maximization, such as information concealment or content obfuscation, which could be explored in scenarios demanding privacy-preserving computations.

In conclusion, the PBI-Attack paper contributes significantly to the literature on adversarial machine learning by highlighting vulnerabilities inherent in current AI models and setting the stage for enhanced protective measures against sophisticated cyber threats.