Daniel Kang
I have two broad areas.
Security:
I am interested in building demonstrations for hacking real-world AI deployments to show that they are not secure. The goal is to force companies to invest in alignment techniques that can solve the underlying security issues.
Verification:
Verification via TEEs or ZKPs
Stream overview
For security:
You will focus on hacking real-world AI deployments to show that they are not secure.
For verification: TEEs or ZKPs
Light-touch (.5 hours of weekly 1:1s)
SF Bay Area
Indifferent
Strong preference
Mentors
Daniel is a professor of computer science at UIUC, where he studies the progress of AI, with a particular focus on dangerous capabilities of AI agents. His work includes:
- CVE-Bench, an award winning benchmark (SafeBench award, ICML spotlight) that is used by frontier labs and governments to measure AI agents' ability to find and exploit real-world vulnerabilities.
- Agent Benchmark Checklist, an award winning work (Berkeley AI summit, 1st place Benchmarks & Evaluations track) that highlights major issues in existing benchmarks.
- InjecAgent, one of the first AI agent safety benchmarks, used by governments and major labs.
Fellows we are looking for
For security:
You should have a strong security mindset, having demonstrated the willingness to be creative on this. I would like to see past demonstration of willingness to get your hands dirty and try many different systems.
For benchmarks:
As creative as possible, willingness to work on the nitty gritty, willingness to work really hard on problems other people find boring. Interests as far away from SF-related interests as possible.
Experience in TEEs or ZKPs
Project selection
Mentor(s) will talk through project ideas with scholar