18 citations · 43 across the 19 of their papers we have counts for
9 papers · 1 filter
PROVISO: Provenance-Informed Action Selection for Secure LLM Agents
Bo Yan, Qi Zhang, Ziwen Kan +2
LLM agents act on the external world through tools and third-party skills, so a single executed action can cause irreversible harm. Existing defenses judge a proposed action withou…
Greedy Decoding Is Not Precision-Invariant: Cross-Precision Output Divergence in LLM Inference
Gaoyuan Du, Anam Nawaz Khan, Rex Zhou +4
Greedy decoding from large language models is commonly treated as deterministic. We show it is not precision-invariant: the same model, prompt, and decoding algorithm produce diffe…
Hearing the Whispers: Black-Box Membership Inference Attacks on Finetuned TTS Models
Kunlin Cai, Kaiyuan Zhang, Zihang Xiang +4
Text-to-Speech (TTS) foundation models are increasingly fine-tuned on private datasets to synthesize highly personalized voices, introducing severe privacy risks by exposing both b…
(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-specific LLMs sourced from unverified public platforms, exposing a critical supply-chain attac…
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…
LASH: Adaptive Semantic Hybridization for Black-Box Jailbreaking of Large Language Models
Abdullah Al Nomaan Nafi, Fnu Suya, Swarup Bhunia +1
Jailbreak attacks expose a persistent gap between the intended safety behavior of aligned large language models and their behavior under adversarial prompting. Existing automated m…