Security

The Security Working Group is responsible for advancing adversarial attack evaluation standards and benchmarks.

Purpose


AILuminate’s security benchmarks are designed to test systems against actively malicious users. They evaluate an AI system’s readiness to guard against a series of “jailbreak” attacks and to continue providing safe responses while under attack. This is distinct from AILuminate’s safety benchmarks, which measure a system’s responses to a naive malicious user – one asking for something the system shouldn’t give them, but not working to get around its safeguards.

Deliverables


2026 goals for the Security working group is working on: 


Build consensus around the MLCommons Taxonomy of LLM Attacks The workstream is bringing together industry, academia, standards organizations, and civil society to create a consensus around a mechanism-based taxonomy of LLM attacks.


Continued development of the MLCommons AILuminate Jailbreak Benchmark from our public launch in October to continued iterations as more jailbreaks are discovered.  In addition, the group is expanding to multi-turn jailbreak methods.

    Meeting Schedule

    Thursdays Weekly on Thursdays 10am – 10:45am ET

    The Security workstream is one of our authenticated workstreams, meaning anyone is welcome to join, however, they must first register with the working group.

    How to Join and Access Resources




    Working Group Chairs

    Florian Tramer

    Florian Tramer is an assistant professor of Computer Science at ETH Zürich where he leads the SPY Lab. His research interests lie in Computer Security, Machine Learning and Cryptography. In his current work, Florian studies the worst-case behavior of Large Language Models from an adversarial perspective, to understand and mitigate long-term threats to the safety and privacy of users.

    Jonathan Petit, PhD

    Dr. Jonathan Petit leads the Artificial Intelligence Security Research & Standards team at Qualcomm. His research spans adversarial machine learning, robustness of perception systems, and security of on-device AI across the hardware-software stack. He is known for pioneering research that exposed vulnerabilities in automated driving and AI-based perception systems, helping shape the field of automotive and AI cybersecurity. In addition to his research leadership, he drives AI security standardization efforts across major international organizations, including CEN/CENELEC, ISO, SAE, ETSI, NIST, and the FCC, helping advance security, safety, and trustworthiness practices for AI systems worldwide. Dr. Petit holds a PhD in Computer Science from Paul Sabatier University (Toulouse III), France.

    Sean McGregor, PhD

    Sean McGregor is cofounder and lead research engineer with the AI Verification and Evaluation Research Institute, founder and executive director for the AI Incident Database, and a fellow of the Berkman Klein Center at Harvard University. With an applications-centered research program spanning reinforcement learning for wildfire suppression and privacy-enhancing technologies, Sean has covered a wide range of safety critical domains. Sean’s open source development work has earned media attention in the Atlantic, Der Spiegel, Wired, Venture Beat, Vice, and Time Magazine while his technical publications have appeared in a variety of machine learning, HCI, ethics, and application-centered proceedings.

    Questions?

    Reach out to us at [email protected]


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