MRJ-Agent: An Effective Jailbreak Agent for Multi-Round Dialogue (2411.03814v2)

Abstract: LLMs demonstrate outstanding performance in their reservoir of knowledge and understanding capabilities, but they have also been shown to be prone to illegal or unethical reactions when subjected to jailbreak attacks. To ensure their responsible deployment in critical applications, it is crucial to understand the safety capabilities and vulnerabilities of LLMs. Previous works mainly focus on jailbreak in single-round dialogue, overlooking the potential jailbreak risks in multi-round dialogues, which are a vital way humans interact with and extract information from LLMs. Some studies have increasingly concentrated on the risks associated with jailbreak in multi-round dialogues. These efforts typically involve the use of manually crafted templates or prompt engineering techniques. However, due to the inherent complexity of multi-round dialogues, their jailbreak performance is limited. To solve this problem, we propose a novel multi-round dialogue jailbreaking agent, emphasizing the importance of stealthiness in identifying and mitigating potential threats to human values posed by LLMs. We propose a risk decomposition strategy that distributes risks across multiple rounds of queries and utilizes psychological strategies to enhance attack strength. Extensive experiments show that our proposed method surpasses other attack methods and achieves state-of-the-art attack success rate. We will make the corresponding code and dataset available for future research. The code will be released soon.

• The paper demonstrates that MRJ-Agent effectively exploits vulnerabilities in multi-round dialogues of LLMs.
• It employs an information-based control and psychological induction strategy to spread harmful intents stealthily across rounds.
• MRJ-Agent outperforms existing methods, underlining the need for robust AI safety and defense mechanisms.

Analysis of "MRJ-Agent: An Effective Jailbreak Agent for Multi-Round Dialogue"

The research paper titled "MRJ-Agent: An Effective Jailbreak Agent for Multi-Round Dialogue" addresses the vulnerabilities of LLMs, specifically in their susceptibility to jailbreak attacks during multi-round dialogues. This research is pivotal due to the increasing integration of LLMs into applications that hold significant influence over human decision-making processes. The paper presents MRJ-Agent, a sophisticated red-teaming tool designed to highlight and exploit these vulnerabilities effectively.

LLMs, such as GPT-4, have demonstrated remarkable capabilities in understanding and generating human-like text. While they have shown exceptional promise, they also harbor the potential for misuse when manipulated through crafted prompts, a practice colloquially known as 'jailbreaking.' The focus of this research extends beyond the single-round dialogue attacks that have been the focus of previous studies to encompass multi-round dialogues, which more accurately emulate real-world human interactions with AI systems.

Methodology

MRJ-Agent introduces a novel multi-round dialogue attack strategy conceptualized as a heuristic search problem. The researchers inscribe a risk decomposition technique that disperses harmful intents over several inquiry rounds, thus reducing detection likelihood. The methodology underlines psychological tactics to reinforce the attack's stealth and efficacy.

Two primary strategies underpin MRJ-Agent's design:

1. Information-Based Control Strategy: This strategy regulates each inquiry's similarity to the original harmful query, ensuring that no single query reveals the underlying malicious intent but collectively prompts the model towards harmful content.
2. Psychological Induction Strategy: Leveraging psychological principles, this strategy incorporates tactics designed to lower resistance from the LLMs, such as social influence and positive framing. These tactics improve the likelihood of the model responding in alignment with the underlying harmful intent.

Additionally, the researchers trained MRJ-Agent as a red-team model—a heuristics-driven approach enabling it to dynamically adjust its inquiry tactics based on the target model’s responses, thereby achieving an effective and iterative attack pathway.

Evaluation and Results

The experimental evaluation demonstrates that MRJ-Agent surpasses existing jailbreak methods, achieving unprecedented success rates. When evaluated against both open-source and proprietary LLMs, such as GPT-3.5 and GPT-4, MRJ-Agent consistently elicited harmful outputs where other methods faltered. Notably, the researchers also evaluated MRJ-Agent in the context of defense methods, such as prompt detection and system prompt safeguards, where MRJ-Agent maintained high adaptability and success rates.

The paper extended the evaluation scope beyond text-to-text interactions, employing MRJ-Agent in tasks including text-to-image (using models like DALLE-3) and image-to-text tasks. The results underscore MRJ-Agent's generalizability across different domains and task types, illustrating widespread vulnerabilities in current AI models.

Implications and Future Direction

The implications of this research are significant for the field of AI safety. By illustrating how sophisticated prompts can bypass existing safeguards in LLMs, the paper calls for a reevaluation of current defense mechanisms. The methodological advancements presented through MRJ-Agent suggest potential pathways for developing robust countermeasures against multi-round dialogue attacks.

Future developments could focus on further enhancing AI model defenses through improved alignment techniques and more sophisticated deterrence mechanisms. Additionally, MRJ-Agent's approach of information-based and psychological strategy synthesis presents a compelling avenue for research into AI cognition and interaction schema, potentially informing both attack and defense strategy development.

In conclusion, the MRJ-Agent represents a comprehensive paper into the vulnerabilities of LLMs within multi-round dialogue contexts, demonstrating the importance of continuous enhancement in AI security measures as models evolve in complexity and application.