Systems Security
Research in this track covers software and infrastructure-level hardware security mechanisms for monitoring and securing AI development and deployment, including side-channel analysis, cluster security, and physical-layer verification. Importantly, this track is distinct from the broader "AI security" framing that refers to adversarial robustness or jailbreaking; the focus here is on literally securing the systems that advanced AI runs on, including data centers, hardware supply chains, compute clusters, and model weights.
- Initial application: No track-specific questions.
- Stream applications & follow-up: Apply to individual streams; follow-up includes interviews and/or additional assessments depending on the stream.
Epoch AI
This stream will work on gathering and analyzing data in order to shed light on the driving forces behind AI and monitor its impacts.
In this project, we will explore GPU side-channel attacks to extract information about model usage. A simple example is to observe (via radio, power fluctuations, acoustics, etc.) which experts were used in each forward pass of an MOE model, then use those observations to guess which tokens were produced.
Implementing SL4/5 and searching for differentially defense-favored security tools.
This stream focuses on AI policy, especially technical governance topics. Tentative project options include: technical projects for verifying AI treaties, metascience for AI safety and governance, and proposals for tracking AI-caused job loss. Scholars can also propose their own projects.