AutoDefense: Multi-Agent LLM Defense against Jailbreak Attacks

Core Concepts

AutoDefense proposes a response-filtering based multi-agent defense framework to enhance the robustness of large language models (LLMs) against jailbreak attacks. By assigning specialized roles to LLM agents, the framework collaboratively filters harmful responses while maintaining performance at normal user requests.

Abstract

AutoDefense introduces a multi-agent defense framework to combat jailbreak attacks on large language models (LLMs). The framework divides tasks among LLM agents, enhancing instruction-following and content understanding. Extensive experiments validate its effectiveness in reducing Attack Success Rate (ASR) while maintaining low false positive rates.
Despite efforts in pre-training and fine-tuning LLMs for safety, jailbreak attacks remain a concern. AutoDefense's response-filtering mechanism and multi-agent approach show promising results in defending against harmful prompts. The framework is flexible and integrates other defense components efficiently.

Stats

ASR of GPT-3.5 with no defense: 55.74%
ASR of AutoDefense with 3 agents: 7.95%
FPR of Single Agent Defense with CoT: 17.16%
FPR of Multi-Agent Defense using LLaMA-2-7b introducing Llama Guard as an agent: 6.80%

Quotes

"Large Language Models remain vulnerable to jailbreak attacks despite extensive pre-training."
"AutoDefense employs multiple LLM agents with specialized roles to filter harmful responses collaboratively."

Key Insights Distilled From

AutoDefense

by Yifan Zeng,Y... at arxiv.org 03-11-2024

https://arxiv.org/pdf/2403.04783.pdf

Deeper Inquiries

How can dynamic communication patterns enhance the efficiency of multi-agent systems like AutoDefense?

Dynamic communication patterns in multi-agent systems, such as AutoDefense, can significantly enhance their efficiency by allowing for adaptive and flexible interactions among agents. Here are some ways in which dynamic communication patterns can improve the performance of a multi-agent system like AutoDefense:

Adaptive Task Allocation: Dynamic communication patterns enable agents to dynamically allocate tasks based on real-time requirements and agent capabilities. This flexibility ensures that each agent is assigned tasks that align with its strengths, leading to optimized task completion.

Real-Time Coordination: Agents can adjust their coordination strategies based on changing circumstances or new information received during the defense process. This adaptability allows for more efficient decision-making and problem-solving within the system.

Efficient Information Sharing: Dynamic communication patterns facilitate timely sharing of critical information among agents, ensuring that all agents have access to relevant data when making decisions or performing analysis tasks.

Improved Problem-Solving: By enabling agents to communicate dynamically, the system can leverage diverse perspectives and expertise from different agents to collaboratively solve complex problems more effectively than static communication structures would allow.

Enhanced Resilience: In dynamic systems, if one agent encounters difficulties or fails in a task, other agents can quickly adapt and redistribute responsibilities without disrupting the overall defense mechanism's functionality.

In summary, dynamic communication patterns promote agility, adaptability, and effective collaboration among multiple agents within a system like AutoDefense, ultimately enhancing its efficiency in defending against jailbreak attacks.

What are the implications of integrating various defense methods into the AutoDefense framework?

Integrating various defense methods into the AutoDefense framework offers several significant implications for enhancing its effectiveness in mitigating threats posed by jailbreak attacks:

Comprehensive Defense Coverage: By incorporating multiple defense methods into AutoDefense, it broadens the range of protection mechanisms available to counter different types of attack vectors effectively.

Synergistic Defense Strategies: Integration allows for synergies between different defense components within AutoDefense; each method may complement others' weaknesses while leveraging their strengths collectively.

Increased Robustness: The combination of diverse defense techniques enhances resilience against sophisticated adversarial tactics designed to bypass individual defenses.

Customizable Defense Configurations: Integrating various methods provides flexibility to tailor specific combinations according to unique threat landscapes or LLM characteristics encountered during deployment.

5 .Reduced False Positives: Different defensive approaches may help cross-verify responses flagged as harmful by one method alone—reducing false positives while maintaining high accuracy levels.
6 .Continuous Improvement: Continuous integration enables ongoing refinement and optimization based on evolving attack strategies or emerging vulnerabilities identified through real-world usage scenarios.

How does AutoDefense contribute to broader development ethical AI systems beyond mitigating jailbreak attacks?

AutoDefence makes significant contributions towards advancing ethical AI systems beyond just addressing jailbreak attacks:
1 .Ethical Guardrails Implementation:
AutoDefence establishes robust guardrails around LLMs' behavior not only concerning malicious prompts but also ensuring alignment with ethical guidelines across all user interactions—a crucial aspect in promoting responsible AI use.
2 .Transparency & Accountability:
By employing a structured multi-agent approach with clear roles assigned per task,
AutoDefence promotes transparency regarding how decisions are made within an AI system—enhancing accountability standards essential for trustworthy AI applications
3 .Bias Mitigation & Fairness:
The collaborative nature of Multi-Agent Systems (MAS) fosters diversity in viewpoints
and reasoning processes—helping identify biases inherent in models’ responses
and supporting fairer outcomes across varied user inputs
4 .User Protection & Privacy Safeguards:
Beyond thwarting external threats like jailbreaking attempts,
AutoDefence reinforces safeguards protecting users from inadvertently harmful content generated by LLMs—prioritizing user well-being alongside model security
5 ．Scalable Ethical Frameworks:
The adaptable architecture of MAS frameworks like
AutoDefence facilitates scalability when implementing evolving ethical standards,
enabling seamless incorporation of future regulatory requirements or societal norms into AI operations