6 papers
Graphs Don't Stay Secret: Practical Subgraph Reconstruction Attacks on Defended Graph RAG
Minkyoo Song, Jaehan Kim, Myungchul Kang +3
Graph-based retrieval-augmented generation (Graph RAG) is increasingly deployed to support LLM applications by augmenting user queries with structured knowledge retrieved from a kn…
No Caption, No Problem: Caption-Free Membership Inference via Model-Fitted Embeddings
Joonsung Jeon, Woo Jae Kim, Suhyeon Ha +2
Latent diffusion models have achieved remarkable success in high-fidelity text-to-image generation, but their tendency to memorize training data raises critical privacy and intelle…
Discovering Universal Activation Directions for PII Leakage in Language Models
Leo Marchyok, Zachary Coalson, Sungho Keum +2
Modern language models exhibit rich internal structure, yet little is known about how privacy-sensitive behaviors, such as personally identifiable information (PII) leakage, are re…
AegisRF: Adversarial Perturbations Guided with Sensitivity for Protecting Intellectual Property of Neural Radiance Fields
Woo Jae Kim, Kyu Beom Han, Yoonki Cho +4
As Neural Radiance Fields (NeRFs) have emerged as a powerful tool for 3D scene representation and novel view synthesis, protecting their intellectual property (IP) from unauthorize…
Defending MoE LLMs against Harmful Fine-Tuning via Safety Routing Alignment
Jaehan Kim, Minkyoo Song, Seungwon Shin +1
Recent large language models (LLMs) have increasingly adopted the Mixture-of-Experts (MoE) architecture for efficiency. MoE-based LLMs heavily depend on a superficial safety mechan…
AdvPaint: Protecting Images from Inpainting Manipulation via Adversarial Attention Disruption
Joonsung Jeon, Woo Jae Kim, Suhyeon Ha +2
The outstanding capability of diffusion models in generating high-quality images poses significant threats when misused by adversaries. In particular, we assume malicious adversari…