Huge thanks to Pratiksha for writing a thread about our works here!! Highly encourage you to check it out. For details on tomorrow’s presentations: 10:30-12:15 - Position: Preventing AI-Generated CSAM Necessitates New Approaches to AI Safety (Spotlight) - Hall A #3013 Co-led this work with Rebecca Portnoff from @thorn, and will be presenting with her colleague and our coauthor Michael Simpson! We outline 15 open problems in AI safety that remain critically unaddressed by current work, and are research barriers to preventing AIG-CSAM. These range from concept fusion and data cleaning to unlearning and robust watermarking. Hope to see you there and spark some great discussions, about what can be done to attack this problem as ML researchers. Michael is highly knowledgeable about the policy and governance landscape surrounding this problem, so encourage anyone interested to come ask those questions too! (Link to all 15 papers in thread). 2:30 - 4:15 - Membership Inference Attacks for Unseen Classes - Hall A #4004 Co-led this work with @prthaker_ who was the driving force behind the initial idea. We’ve been working on this paper almost since I first joined CMU in 2024, and I’m really happy to finally present it here on behalf of our small team! In addition to advancing our understanding of MIAs (and showing we could audit models for CSAM in training data without accessing CSAM), this paper was one of my first ML research experiences and taught me so much about producing high -quality research, putting theory first, and how to persevere on a seemingly impossible research problem - detecting whether a model was trained on data without any in-distribution samples! Come check out the poster, super stoked to share more. Huge thanks to my research advisor @gingsmith whose guidance was so insightful on both papers, and all coauthors involved - @prthaker_, @zstevenwu, @chhaviyadav_, Kevin Kuo - and Rob Wang, Michael Simpson, and Rebecca Portnoff from Thorn! These works were truly cross-domain team efforts. See you all tomorrow!

🇰🇷 Excited to present 2 papers on AI safety at ICML 2026 in Seoul next week, including a first-author spotlight! (TW) Both papers revolve around preventing AI-generated child sexual abuse material. (1) explains how existing AI safety work fails to address the real considerations of working with AIG-CSAM, such as data access restrictions. (2) explores how quantile membership inference attacks (QMIA) can allow us to audit models without ever accessing in-distribution samples, e.g., audit models for CSAM w/o data access. Stay tuned, will be posting more about these papers over the coming days!

[10/n] Paper: papers.ssrn.com/sol3/papers.… Website: aichildsafety.github.io Joint work between @thorn and @mldcmu: @neilkale*, R. Portnoff*, @prthaker_ , M. Simpson, R. Wang, K. Kuo, @chhaviyadav_ , @gingsmith We welcome collaboration. Reach out if you're working on these problems!