SL402570-Winter 2027-CIS*6670 ST: AI and LLM Security and Privacy

Sessional Lecturer Work Assignment
Sessional Lecturer, Unit 2
Academic Unit: 
School of Computer Science
Semester(s) of Assignment(s): 
Winter 2027
Number of Available Work Assignment(s) / Sections: 
1
Level of Work Assignment(s): 
1
Right of First Refusal (RoFR)
A Sessional Lecturer holds a RoFR (i.e., for a particular course) if they have successfully taught the course in the past six (6) semesters. A SL who holds a RoFR to this course is required to exercise their right by way of the online hiring system. Also see: What is Right of First Refusal (RoFR)?
A Sessional Lecturer Currently Holds a Right of First Refusal for this Course: 
No
Course Details
Course Number: 
CIS*6670
Course Name: 
ST: AI and LLM Security and Privacy
Course Format: 
In-Person
Course Description: 
See Course Calendar
Other Course Description or Assignment Information: 
Large language models (LLMs) and other AI systems are now deployed in critical areas such as health, finance and cybersecurity, and they introduce new security and privacy risks. This offering of Special Topics examines attacks on machine learning and LLM systems, the defences against them, and the governance needed to deploy them safely. It moves from classical adversarial machine learning to LLM-specific risks, then to privacy, provenance and governance. Topics include: - threat modelling for ML and LLM systems (NIST AI 100-2 taxonomy); - evasion, poisoning and backdoor attacks; - privacy attacks, including membership inference and training-data extraction; - jailbreaking and safety alignment; - direct and indirect prompt injection and their defences; - poisoning of retrieval-augmented generation (RAG) and the model supply chain; - security and privacy of LLM agents; - privacy-preserving machine learning (differential privacy, DP-SGD, privacy auditing, federated learning); - machine unlearning, watermarking and provenance of AI-generated content; - LLMs for security and their dual-use risks; - AI governance and risk management (NIST AI RMF, EU AI Act, OWASP Top 10 for LLM Applications and the Canadian regulatory context).
Projected Class Enrolment: 
60
Anticipated Duties and Responsibilities
Anticipated Duties and Responsibilities: 
Orientation-Training
Office Hours
Preparation
Student Consultation
Lecturing
Email Correspondence/Monitoring
Conducting Labs/Seminars
TA Coordination Meetings
Invigilating Exams
Grading
Qualifications
Required Qualifications
Degree: 
PhD related to field
Other
Masters in Computer Science/Engineering or related field
Prior Teaching Experience: 
Successful teaching related to field at college or university level
Required competence, capability, skill and ability related to course content: 
Research and practical experience in AI and LLM security and privacy, including experience with: - threat modelling of ML/LLM systems; - adversarial machine learning (evasion attacks, data poisoning, backdoors); - privacy attacks (membership inference, training-data extraction); - LLM jailbreaking and prompt injection (direct and indirect); - RAG systems and LLM agent security; - differential privacy and DP-SGD, privacy auditing and federated learning; - machine unlearning and watermarking of LLM outputs; - deep learning frameworks and model ecosystems (Python, PyTorch, Hugging Face); - deploying and evaluating open-weight LLMs; - AI risk and governance frameworks (NIST AI RMF, NIST AI 100-2, EU AI Act, OWASP Top 10 for LLM Applications).
Preferred Qualifications
Degree: 
PhD and expert in course content
Other
PhD in Computer Science, Computer Engineering or a related field
Prior Teaching Experience: 
Many years of successful teaching related to contents of the course.
Other
Successful teaching of graduate courses in AI security, machine learning security or privacy
Specific Preferred competence, capability, skill and ability related to course content: 
- Experience with LLM red-teaming and evaluation tools (e.g., garak, PyRIT, AgentDojo, HarmBench) and privacy-preserving ML libraries (e.g., Opacus); - Experience applying LLMs to security operations (e.g., SOC analytics, vulnerability analysis); - Industry or applied experience securing deployed AI systems will be considered an asset.
Days Required and Wages
Days and Times Required: 
Dates and times have not been finalized, but will be discussed at the interview phase of the process. This is an in-class course, so an interested applicant should assume at least three hours of teaching on campus per week.
Period of the Work Agreement (Start Date and End Date): 
January 7, 2027 to May 7, 2027
Wages (per semester, per full-load): 
minimum $7,758.48 (effective 2022/23)
Other Posting Information
Application Deadline (All postings will automatically expire at 11:59 pm on this day): 
Thursday, November 19, 2026
Posting Email Contact: 
socspost@uoguelph.ca
Hiring Contact Information: 
Christine Harris, Instructional Support Coordinator - teachingsupport@socs.uoguelph.ca

At the University of Guelph, fostering a culture of inclusion is an institutional imperative. The University invites and encourages applications from all qualified individuals, including from groups that are traditionally underrepresented in employment, who may contribute to further diversification of our Institution. For more information, the Office of Diversity and Human Rights (DHR) is a welcoming, safe and confidential one-stop shop for information, training and support on issues relating to diversity and human rights on our campus.
SL work assignments are unionized with CUPE3913 and their terms and conditions of work are covered by the Unit 2 Collective Agreement between the University and CUPE 3913 (email contact: president@cupe3913.on.ca).

All applicants must be eligible to work in Canada specifically at the University of Guelph before applying for an academic work assignment. All successful applicants must perform their work in Ontario and must be able to attend on-campus in-person meetings as required.