Note: The schedule below will be updated as we progress through the course. Please check the table regularly to stay on track.
| Week | Lecture | Date | Topic | Reading | |
|---|---|---|---|---|---|
| 1 | 1 | Wed, Aug 26, 26 | Introduction to the Course | ||
| 2 | Fri, Aug 28, 26 | Security Fundamentals: Policies & Threat Models | |||
| 2 | 3 | Mon, Aug 31, 26 | Security Fundamentals: Trust & Access Control | ||
| 4 | Wed, Sep 2, 26 | Cryptography Overview | |||
| 5 | Fri, Sep 4, 26 | Web Security I: Core Concepts & Vulnerabilities | |||
| 3 | Mon, Sep 7, 26 | Labor Day (no classes) | |||
| 6 | Wed, Sep 9, 26 | Web Security II: Attacks & Defenses | |||
| 7 | Fri, Sep 11, 26 | Software Security I: Memory Safety & Buffer Overflows | |||
| 4 | 8 | Mon, Sep 14, 26 | Software Security II: Code Reuse & Control-Flow Attacks | ||
| 9 | Wed, Sep 16, 26 | Introduction to Machine Learning & AI | |||
| 10 | Fri, Sep 18, 26 | Adversarial Machine Learning | |||
| 5 | 11 | Mon, Sep 21, 26 | Modern AI & Security: Attack Surfaces & Exploitation | ||
| 12 | Wed, Sep 23, 26 | Modern AI & Security: Prompt Injection & Jailbreaking | |||
| 13 | Fri, Sep 25, 26 | Paper presentation: TBD | |||
| 6 | 14 | Mon, Sep 28, 26 | Paper presentation: TBD | ||
| 15 | Wed, Sep 30, 26 | Paper presentation: TBD | |||
| 16 | Fri, Oct 2, 26 | Paper presentation: TBD | |||
| 7 | 17 | Mon, Oct 5, 26 | Paper presentation: TBD | ||
| 18 | Wed, Oct 7, 26 | Paper presentation: TBD | |||
| Fri, Oct 9, 26 | Fall Break (no classes) | ||||
| 8 | 19 | Mon, Oct 12, 26 | Paper presentation: TBD | ||
| 20 | Wed, Oct 14, 26 | Paper presentation: TBD | |||
| 21 | Fri, Oct 16, 26 | Paper presentation: TBD | |||
| 9 | 22 | Mon, Oct 19, 26 | Paper presentation: TBD | ||
| 23 | Wed, Oct 21, 26 | Paper presentation: TBD | |||
| 24 | Fri, Oct 23, 26 | Paper presentation: TBD | |||
| 10 | 25 | Mon, Oct 26, 26 | Paper presentation: TBD | ||
| 26 | Wed, Oct 28, 26 | Paper presentation: TBD | |||
| 27 | Fri, Oct 30, 26 | Paper presentation: TBD | |||
| 11 | 28 | Mon, Nov 2, 26 | Paper presentation: TBD | ||
| 29 | Wed, Nov 4, 26 | Paper presentation: TBD | |||
| 30 | Fri, Nov 6, 26 | Paper presentation: TBD | |||
| 12 | 31 | Mon, Nov 9, 26 | Paper presentation: TBD | ||
| 32 | Wed, Nov 11, 26 | Paper presentation: TBD | |||
| 33 | Fri, Nov 13, 26 | Paper presentation: TBD | |||
| 13 | 34 | Mon, Nov 16, 26 | Paper presentation: TBD | ||
| 35 | Wed, Nov 18, 26 | Paper presentation: TBD | |||
| 36 | Fri, Nov 20, 26 | Paper presentation: TBD | |||
| 14 | 37 | Mon, Nov 23, 26 | Paper presentation: TBD (on Zoom) | ||
| Wed, Nov 25, 26 | Thanksgiving Break (no classes) | ||||
| Fri, Nov 27, 26 | Thanksgiving Break (no classes) | ||||
| 15 | 38 | Mon, Nov 30, 26 | Paper presentation: TBD | ||
| 39 | Wed, Dec 2, 26 | Paper presentation: TBD | |||
| 40 | Fri, Dec 4, 26 | Paper presentation: TBD (Last day of classes) | |||
| Finals | Dec 7–15, 26 | Project presentations |
This graduate seminar investigates how artificial intelligence transforms modern systems security. The course focuses on two core pillars: AI as a security tool (for both offensive and defensive operations), and the systemic vulnerabilities introduced by integrating AI into software and hardware infrastructure.
Topics include adversarial machine learning, defensive ML applications, agentic exploits, prompt injection, and the low-level attack surfaces of AI platforms. Necessary background in security fundamentals, systems, and core machine learning concepts will be provided throughout the seminar.
The foundational background will be presented through lectures, complemented by discussions of the most impactful and recent academic literature. Students will read and review papers in the AI security field, participate actively in class discussions, and present selected papers. Additionally, students will complete either a term project or an in-depth survey paper on a topic of their choice, presenting their findings in class.
Basic understanding of computer systems concepts and general programming experience. No prior background in security or machine learning is required, as the necessary foundations will be introduced in class.