Security of Agentic AI Systems
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InstructorCourse DescriptionAgentic AI systems are autonomous entities capable of perceiving, reasoning, learning, and acting toward goals using large language models (LLMs) with minimal human oversight. While these systems offer significant potential advantages, they also introduce systemic risks. Misaligned or poorly defined objectives can drive agents to take unsafe shortcuts, bypass safeguards, or behave deceptively. As AI agents become increasingly embedded in real-world applications, ensuring their security, reliability, and alignment is becoming a critical priority. In this class we will study architectures and applications of agentic AI systems, understand threat models and attacks against them, and study existing proposed defenses. The objectives of the course are the following:
GradeThe grade will be based on paper review submission (PR), participation in paper discussions in class (PD), presentations of papers in class and discussion lead (PL), one programming assignment (PA) and a research project (RP). Paper reviews are due by 9pm the day before the lecture when the paper is discussed. Submission is through Gradescope. Grade is computed as follows: Academic IntegrityAcademic Honesty and Ethical behavior are required in this course, as it is in all courses at Northeastern University. There is zero tolerance to cheating. You are encouraged to talk with the professor about any questions you have about what is permitted on any particular assignment. Resources
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A tentative schedule is posted below for public access. Class platform is Canvas available through mynortheastern. All additional material for the class and all class communication will take place on Canvas. For the most updated information check Canvas.
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