VulGen'26 Workshop at ACM SIGIR 2026
The International Workshop on Vulnerabilities in Generative Systems for Information Retrieval (VulGen'26) will be held as a full-day workshop at the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'26) in Naarm/Melbourne, Australia, on 24 July 2026.
Venue: Melbourne Convention and Exhibition Centre, 1 Convention Centre Place, South Wharf 3006.
The VulGen'26 brings together the IR community and related disciplines (e.g., cyber security) to foster cross-disciplinary engagement and advance understanding of vulnerabilities in generative systems used for IR. Through VulGen’26, we aim to (i) understand under what circumstances the generative systems could “go wrong” (i.e., vulnerabilities), in both current state and future development; and (ii) build a dedicated research community to address the potential downsides of generative systems that could lead to unexpected or harmful outcomes.
You can expect a full-day dynamic program packed with keynote and panel sessions, structured breakout discussions, extended abstract presentations, and engaging poster and demonstration sessions.
See submission details: Call for Submissions
See VulGen'26 workshop details: About VulGen'26
Important Dates
All key dates are by the end of day in Anywhere on Earth (AOE).
- Extended abstract submission - 27 April 2026
- Notification - 21 May 2026
- Camera-ready submission - 15 June 2026
- Workshop co-located with SIGIR´26 - 24 July 2026
If you have any questions, please contact us via
vulgen-workshop-sigir2026@easychair.org
Keynote Speaker
Prof Chris Leckie
University of Melbourne, Australia
University Profile · ADM+S Profile · Google Scholar
Prof Leckie has a strong interest in developing AI techniques for a variety of applications in telecommunications, such as cyber security, network management, fault diagnosis and the Internet-of-Things.
He also has an interest in robust and scalable machine learning algorithms for problems such as clustering and anomaly detection, with a focus on adversarial machine learning.
Sponsor
ARC Centre of Excellence for Automated Decision-Making and Society (ADM+S)