Sep 1, 2024
CAMARA: A Comprehensive & Adaptive Multi-Agent framework for Red-Teaming and Adversarial Defense
Vishnu Vardhan Lanka, Era Sarda, Raghav Ravishankar
Summary
The CAMARA project presents a cutting-edge, adaptive multi-agent framework designed to significantly bolster AI safety by identifying and mitigating vulnerabilities in AI systems such as Large Language Models. As AI integration deepens across critical sectors, CAMARA addresses the increasing risks of exploitation by advanced adversaries. The framework utilizes a network of specialized agents that not only perform traditional red-teaming tasks but also execute sophisticated adversarial attacks, such as token manipulation and gradient-based strategies. These agents collaborate through a shared knowledge base, allowing them to learn from each other's experiences and coordinate more complex, effective attacks. By ensuring comprehensive testing of both standalone AI models and multi-agent systems, CAMARA targets vulnerabilities arising from interactions between multiple agents, a critical area often overlooked in current AI safety efforts. The framework's adaptability and collaborative learning mechanisms provide a proactive defense, capable of evolving alongside emerging AI technologies. Through this dual focus, CAMARA not only strengthens AI systems against external threats but also aligns them with ethical standards, ensuring safer deployment in real-world applications. It has a high scope of providing advanced AI security solutions in high-stake environments like defense and governance.
Cite this work:
@misc {
title={
CAMARA: A Comprehensive & Adaptive Multi-Agent framework for Red-Teaming and Adversarial Defense
},
author={
Vishnu Vardhan Lanka, Era Sarda, Raghav Ravishankar
},
date={
9/1/24
},
organization={Apart Research},
note={Research submission to the research sprint hosted by Apart.},
howpublished={https://apartresearch.com}
}
