Description du poste
Summary• We, the Secure AI Futures Lab (SAFL) team, ran 5 sessions between 8 Aug and 3 Oct 2026 at the Secure AI Hub in Bengaluru to help technical professionals from different backgrounds upskill in AI red teaming. • We structured the sessions to go beyond paper reading and discussion by including exercises such as predicting results and using Colab notebooks to reproduce key results with smaller models.• Two guest talks. • Mohammad Taufeeque (FAR.AI) gave a talk for the workshop on 22 Aug, covering, among other things, what red teaming looks like at FAR.AI. • Pulkit Verma (IIT Madras) gave the first talk in our AI Futures Dialogue series on 21 Aug, on how users can assess AI agents after deployment.• 20 different people attended, with 38 attendances in total. A core group of 6 came to 3 or more sessions.• Mean overall experience: 9.6/10; NPS +80 from 25 responses.• Main output: • A list of open problems in jailbreaking for participants to contribute to • 5 sets of session notebooks and shared docs, available on request.• Top request from participants: a capstone research project or paper (8 of 15 suggestions).What we set out to doMost people in India who want to work on AI red teaming are on their own and don’t have a shared place to practice. The workshop was for technical professionals in Bengaluru, with or without a security background, who wanted to understand why model safeguards fail and how to measure that. Each session paired a key paper with hands-on work on small open models. By the end, we aimed for participants to be able to judge whether a reported jailbreak result is believable and test a claim themselves.
The plan was to have 8 sessions, one a week, 3 hours each. We made design choices such as making sessions standalone, requiring no dedicated setup for hands-on work; and designing sessions for active engagement.
Success criteriaWe judge the workshop against two objectives, each with expected and stretch outcomes.
• P50 is the outcome we expected to