Asymmetric Security
This stream focuses on building realistic defensive cybersecurity benchmarks utilizing data from Asymmetric Security's work on real-world incidents.
Stream overview
Existing cybersecurity benchmarks lack realism, rarely testing how models behave in realistic security scenarios. This is especially challenging in cybersecurity because most relevant data is private.
Asymmetric Security responds to real cyber incidents and therefore holds data not available in the public domain. We would like to work with MATS scholars to build realistic benchmarks grounded in these real cyber incidents.
SF Bay Area
Mentors
Zainab 是 Asymmetric Security 的联合创始人。此前，她曾在 Stroz Friedberg 担任网络安全分析师，调查了过去十年中一些影响重大的网络安全事件，例如 Cambridge Analytica 事件。她还曾在 NeurIPS 发表关于 AI 网络安全评估的研究。Zainab 拥有牛津大学物理学硕士学位。
Fellows we are looking for
Essential:
- Experience implementing AI model evaluations.
Preferred:
- At least one year of professional software engineering experience.
- Strong interest in AI cybersecurity.
Project selection
We will assign the project direction; scholars will have significant tactical freedom.