10 papers
MuPPET: A Benchmark for Contextual Privacy of LLM Assistants in Multi-Party Conversations
Elena Sofia Ruzzetti, Cornelius Emde, Sangdoo Yun +2
LLM agents are increasingly deployed in multi-party environments, handling sensitive personal data on behalf of individual users, for instance in group chats. When such an agent di…
Predicting Future Behaviors in Reasoning Models Enables Better Steering
Evgenii Kortukov, Piotr Komorowski, Florian Klein +5
Deployed large reasoning models (LRMs) often behave unexpectedly. Test-time steering controls LRM outputs by intervening on their hidden representations, but it can degrade output…
It Takes Two: Complementary Self-Distillation for Contextual Integrity in LLMs
Sangwoo Park, Woongyeong Yeo, Seanie Lee +6
Contextual Integrity (CI) defines privacy not merely as keeping information hidden, but as governing information flows according to the norms of a given context. As large language…
Dr.LLM: Dynamic Layer Routing in LLMs
Ahmed Heakl, Martin Gubri, Salman Khan +2
Large Language Models (LLMs) process every token through all layers of a transformer stack, causing wasted computation on simple queries and insufficient flexibility for harder one…
MEME: Multi-entity & Evolving Memory Evaluation
Seokwon Jung, Alexander Rubinstein, Arnas Uselis +2
LLM-based agents increasingly operate in persistent environments where they must store, update, and reason over information across many sessions. While prior benchmarks evaluate on…
Privacy Collapse: Benign Fine-Tuning Can Break Contextual Privacy in Language Models
Anmol Goel, Cornelius Emde, Sangdoo Yun +2
We identify a novel phenomenon in language models: benign fine-tuning of frontier models can lead to privacy collapse. We find that diverse, subtle patterns in training data can de…