3 papers
cs.CL2026
Learning the Signature of Memorization in Autoregressive Language Models
David Ilić, Kostadin Cvejoski, David Stanojević +1
All prior membership inference attacks for fine-tuned language models use hand-crafted heuristics (e.g., loss thresholding, Min-K\%, reference calibration), each bounded by the des…
cs.CR2026
Protecting Private Code in IDE Autocomplete using Differential Privacy
Evgeny Grigorenko, David Stanojević, David Ilić +2
Modern Integrated Development Environments (IDEs) increasingly leverage Large Language Models (LLMs) to provide advanced features like code autocomplete. While powerful, training t…
cs.CL2026
Powerful Training-Free Membership Inference Against Autoregressive Language Models
David Ilić, David Stanojević, Kostadin Cvejoski
Fine-tuned language models pose significant privacy risks, as they may memorize and expose sensitive information from their training data. Membership inference attacks (MIAs) provi…