9 papers
(A)iSpy: Parasitic Trojans for Machine Learning Infrastructure
Habibur Rahaman, Qipan Xu, Zafaryab Haider +3
Modern machine learning (ML) pipelines depend heavily on third party libraries for graph compilation and hardware acceleration. While current practices audit data and model artifac…
ImageAuditor: Membership Inference Attack against Image-based Retrieval-Augmented Generation
Jinghuai Zhang, Pengyue Yu, Zhexiao Lin +3
Image-based Retrieval-Augmented Generation (IRAG) conditions a frozen generator on reference images retrieved from an external database, supporting both text-to-image (T2I) and que…
RogueMerge: Robust and Unified Attacks against LLM Model Merging
Jinghuai Zhang, Yetian He, Kunlin Cai +3
Model merging composes specialized capabilities into a single LLM by aggregating task vectors sourced from unverified public platforms, exposing a critical supply-chain attack surf…
Token Inflation: How Dishonest Providers Can Overcharge for Large Language Model Usage
Shahinul Hoque, Jinghuai Zhang, Jinyuan Sun +1
Per-token billing is now the standard pricing model for commercial large language models (LLMs), so the honesty of reported token counts directly affects what users pay. We show th…
What-If World: A Causal Benchmark for General World Models in Embodied Scenarios
Kunlin Cai, Rui Song, Jinghuai Zhang +7
Video generation models are increasingly used as world simulators for tasks like driving and robotic manipulation. What matters in these settings is not whether a single video look…
DASH: A Meta-Attack Framework for Synthesizing Effective and Stealthy Adversarial Examples
Abdullah Al Nomaan Nafi, Habibur Rahaman, Zafaryab Haider +4
Numerous techniques have been proposed for generating adversarial examples in white-box settings under strict Lp-norm constraints. However, such norm-bounded examples often fail to…