Research is fundamental to our mission of providing the most safe, secure, and capable AI available.
Artificial intelligence (AI) is progressively weaving itself into the fabric of our daily lives, the importance of ensuring these systems operate safely and transparently has never been greater. Over the past year, our research team has been at the forefront of tackling some of the most pressing challenges in AI safety and security. Through groundbreaking studies, we have not only uncovered critical vulnerabilities but have also pioneered innovative methodologies to enhance the robustness and transparency of AI models.
In July 2023, we published the first-ever automated jailbreaking method on large language models (LLMs) and exposed their susceptibility to adversarial attacks. By demonstrating that specific character sequences could bypass sophisticated safeguards, we highlighted a significant vulnerability that has urgent implications for widely-used AI systems. In its wake, adversarial robustness garnered renewed attention, sparking a gold rush of research dedicated to both jailbreaking and defense.
Building on our initial findings, we ventured into the realm of AI interpretability and control with the introduction of Representation Engineering (RepE). Drawing inspiration from cognitive neuroscience, we developed techniques that enable researchers to 'read' and 'control' the 'minds' of AI models. This approach represented a monumental advancement in demystifying the inner workings of AI, making it possible to tackle issues such as truthfulness and power-seeking behaviors head-on.
To address the urgent concerns raised by our attack from last July and the numerous jailbreaks that came after, we introduce Circuit Breaking, a novel approach inspired by representation engineering, designed to robustly prevent AI systems from generating harmful content by directly altering harmful model representations. The family of circuit-breaking methods provide an alternative to refusal and adversarial training, protecting both LLMs and multimodal models from strong, unseen adversarial attacks without compromising model capability.
What binds these distinct yet interconnected research endeavors is our unwavering commitment to advancing the safety and integrity of AI technologies. By systematically investigating vulnerabilities, developing novel transparency techniques, and enhancing robustness, we have laid down a comprehensive framework that addresses the multifaceted challenges of AI safety.
As we look forward to the future, our research continues to evolve at an exciting pace. Our team is already deep into exploring new ideas and techniques that promise to further revolutionize the field of AI safety. The insight sand methodologies developed over the past year serve as a solid foundation upon which we will build ever more sophisticated and reliable AI systems. Our journey is far from over; in fact, it's just beginning. We will be pushing the frontiers of what's possible in AI safety and transparency.
For those eager to dive deeper into our previous works and follow our ongoing projects, we invite you to explore our Research. Here, you'll find a comprehensive archive of our past research, papers, and updates, offering a detailed view of our contributions to the field.
Rigorous protection to make sure that AI does not veer off course.
Finding out what can go wrong, before it can cause problems.
Enhancing reliability against external threats.
Explore our published research to learn how the latest advances in AI safety and security give Gray Swan the edge against evolving threats.
To address the urgent concerns raised by our attack from last July and the numerous jailbreaks that came after, we introduce Circuit Breaking, a novel approach inspired by representation engineering, designed to robustly prevent AI systems from generating harmful content by directly altering harmful model representations. The family of circuit-breaking methods provide an alternative to refusal and adversarial training, protecting both LLMs and multimodal models from strong, unseen adversarial attacks without compromising model capability.
Building on our initial findings, we ventured into the realm of AI interpretability and control with the introduction of Representation Engineering (RepE). Drawing inspiration from cognitive neuroscience, we developed techniques that enable researchers to 'read' and 'control' the 'minds' of AI models. This approach represented a monumental advancement in demystifying the inner workings of AI, making it possible to tackle issues such as truthfulness and power-seeking behaviors head-on.
In July 2023, we published the first-ever automated jailbreaking method on large language models (LLMs) and exposed their susceptibility to adversarial attacks. By demonstrating that specific character sequences could bypass sophisticated safeguards, we highlighted a significant vulnerability that has urgent implications for widely-used AI systems. In its wake, adversarial robustness garnered renewed attention, sparking a gold rush of research dedicated to both jailbreaking and defense.
Keep up to date on all things Gray Swan AI and AI Security.