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 securing the systems that advanced AI runs on, including data centers, hardware supply chains, compute clusters, and model weights.
- Stage 1: Complete the general application
- Stage 2: Stream selection questions alongside possibly reference requests
- Stage 3: Interviews and work-tests
The track focuses on the hardware, software, and infrastructure that frontier AI systems run on. Even if alignment research succeeds, the safety properties it produces only matter in practice if model weights aren't stolen, training and inference compute can't be tampered with, and the systems running advanced AI can be audited and verified. This track exists to build the technical foundations for that. The streams within this track cover model weight protection, side-channel analysis, secure enclaves, hardware supply chain assurance, data center and cluster security, physical-layer verification of compute use, and the technical components that make compute governance and export controls enforceable.
We are looking for fellows with real depth in systems or security work, and fluency in at least one of these areas: security engineering, vulnerability research, cryptography, operating systems and other low-level software, hardware security (e.g., chips, FPGAs, embedded systems), or systems engineering at the infrastructure layer. ML expertise is preferred but not required. What matters is the ability to reason carefully about adversaries, side channels, and trust boundaries. Strong candidates from past cohorts have come from security teams at tech companies, hardware and chip design, cryptography research, government security work, CTF and vulnerability research backgrounds, and embedded systems engineering.
Fellows are matched to mentors based on fit, and projects produce concrete artifacts (e.g., security audits, technical specifications, prototype defenses, attack demonstrations, or proposals for verification protocols) by the end of the program. Target audiences for the work produced in this track include lab security and infrastructure teams, hardware vendors, and the policy actors who need technical groundwork to enforce compute governance and export controls.
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
We will explore side-channels and covert channels in data center hardware such as GPUs, network switches, power supplies, and chillers. Broadly, we want to demonstrate information entering or leaving hardware in unexpected or surprising ways, especially to steal information, monitor for compliance, or establish covert communication channels. This work supports advancements in international verification, nation-state security of weights & tokens, and containment against loss of control.
This stream develops and analyzes protocols for verifying AI compute usage, including technical approaches to verification and their implications for AI development.
The SL5 Task Force will build out a prototype SL5 datacenter this year together with frontier AI labs. This will be a massive research and engineering project with many avenues for spinning out new organizations and research programs. This project is urgent due to this technology being needed in the next 1 to 2 years.
Our stream focuses on AI verification, as in how actors can check that the use of AI compute is compliant with policy, especially for enabling international agreements on AI. This sense of verification is much broader than formal verification.
Backing projects focused on product development and organization building in the areas of AI safety and alignment, biosecurity, and critical cybersecurity. Looking for fellows who are self starters, default to action, and have a desire to create.