JailBreakV-28K: A Comprehensive Benchmark for Assessing the Robustness of Multimodal Large Language Models against Diverse Jailbreak Attacks

핵심 개념

Multimodal Large Language Models (MLLMs) are vulnerable to jailbreak attacks that can induce them to provide harmful content, and techniques that successfully jailbreak Large Language Models (LLMs) can be effectively transferred to attack MLLMs.

초록

The paper introduces JailBreakV-28K, a comprehensive benchmark designed to evaluate the transferability of LLM jailbreak attacks to MLLMs and assess the robustness of MLLMs against diverse jailbreak attacks.
Key highlights:

The RedTeam-2K dataset is created, containing 2,000 malicious queries spanning 16 safety policies, to serve as the foundation for the benchmark.
JailBreakV-28K is constructed by generating 20,000 text-based LLM transfer jailbreak attacks and 8,000 image-based MLLM jailbreak attacks, covering a wide range of attack methodologies and image types.
Experiments on 10 open-source MLLMs reveal a notably high Attack Success Rate (ASR) for attacks transferred from LLMs, highlighting a critical vulnerability in MLLMs that stems from their text-processing capabilities.
The effectiveness of text-based jailbreak attacks is found to be largely independent of the image input, underscoring the need to address alignment vulnerabilities in MLLMs from both textual and visual inputs.

통계

"Textual jailbreak prompts capable of compromising LLMs are also likely to be effective against MLLMs, regardless of the foundational model employed by the MLLMs."
"The effectiveness of these textual jailbreak prompts does not depend on the image input. Whether the image input is blank, consists of noise, or is a random natural image, the jailbreak still occurs."

인용구

"Textual jailbreak prompts capable of compromising LLMs are also likely to be effective against MLLMs, regardless of the foundational model employed by the MLLMs."
"The effectiveness of these textual jailbreak prompts does not depend on the image input. Whether the image input is blank, consists of noise, or is a random natural image, the jailbreak still occurs."

핵심 통찰 요약

JailBreakV-28K

by Weidi Luo,Si... 게시일 arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03027.pdf

더 깊은 질문

How can the community develop robust defense mechanisms to mitigate the dual risks posed by textual and visual jailbreak vulnerabilities in MLLMs?

To address the dual risks posed by textual and visual jailbreak vulnerabilities in Multimodal Large Language Models (MLLMs), the community can implement several strategies:

Enhanced Training Data: Incorporate diverse and comprehensive training data that includes a wide range of harmful queries and images to improve the model's understanding of potential threats.

Adversarial Training: Implement adversarial training techniques to expose the MLLMs to potential attacks during the training phase, helping them learn to recognize and defend against such threats.

Regular Audits and Updates: Conduct regular audits of the model's responses to ensure alignment with safety policies and update the model's defenses based on new attack strategies.

Multi-Modal Alignment Checks: Develop mechanisms to ensure that the model's responses align with both textual and visual inputs, detecting discrepancies that may indicate a potential jailbreak attempt.

Collaborative Efforts: Foster collaboration within the research community to share insights, techniques, and best practices for enhancing the security and alignment of MLLMs against jailbreak attacks.

By implementing these strategies and continuously improving defense mechanisms, the community can work towards mitigating the dual risks posed by textual and visual jailbreak vulnerabilities in MLLMs.

What are the potential implications of the discovered vulnerabilities on the real-world deployment and adoption of MLLMs?

The discovered vulnerabilities in MLLMs, particularly the high Attack Success Rates (ASRs) of jailbreak attacks transferred from Large Language Models (LLMs), have significant implications for the real-world deployment and adoption of MLLMs:

Trust and Safety Concerns: The vulnerabilities highlight the potential for MLLMs to provide harmful or inappropriate responses, raising concerns about trust and safety in applications that rely on these models for decision-making or content generation.

Regulatory Compliance: Organizations deploying MLLMs may face regulatory challenges related to content moderation, privacy violations, and alignment with ethical guidelines, necessitating robust compliance measures.

Reputational Risks: Instances of successful jailbreak attacks on MLLMs could lead to reputational damage for organizations using these models, impacting user trust and brand reputation.

Legal Ramifications: In cases where MLLMs generate harmful content due to vulnerabilities, organizations may face legal liabilities, especially in sensitive domains such as healthcare, finance, or public safety.

Adoption Hurdles: Concerns about the security and alignment of MLLMs may create adoption hurdles, with stakeholders hesitant to fully leverage these models in critical applications until robust defenses are in place.

Addressing these vulnerabilities is crucial to ensuring the responsible deployment and widespread adoption of MLLMs across various industries and applications.

How can the insights from this study inform the design of future multimodal language models to ensure stronger alignment with human values and safety?

The insights from this study can inform the design of future multimodal language models in the following ways:

Enhanced Alignment Mechanisms: Future models can incorporate advanced alignment mechanisms that consider both textual and visual inputs, ensuring that responses are consistent and aligned with human values across modalities.

Robust Defense Strategies: Design models with robust defense strategies against jailbreak attacks, including adversarial training, multi-modal alignment checks, and continuous monitoring for potential vulnerabilities.

Ethical Guidelines Integration: Integrate ethical guidelines and safety policies directly into the model architecture to guide decision-making and content generation processes, promoting responsible AI practices.

Transparency and Explainability: Prioritize transparency and explainability in model decisions, enabling users to understand how responses are generated and facilitating trust in the model's outputs.

Collaborative Research Efforts: Encourage collaborative research efforts to share insights, best practices, and methodologies for enhancing the safety and alignment of future multimodal language models, fostering a community-driven approach to model development.

By incorporating these insights into the design and development of future multimodal language models, researchers can ensure stronger alignment with human values and safety, promoting the responsible and ethical deployment of AI technologies.