18 citations · 43 across the 19 of their papers we have counts for
12 papers · 1 filter
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…
HAMLOCK: HArdware-Model LOgically Combined attacK
Sanskar Amgain, Daniel Lobo, Atri Chatterjee +2
The growing use of third-party hardware accelerators (e.g., FPGAs, ASICs) for deep neural networks (DNNs) introduces new security vulnerabilities. Conventional model-level backdoor…
Adversarial Hubness in Multi-Modal Retrieval
Tingwei Zhang, Fnu Suya, Rishi Jha +2
Hubness is a phenomenon in high-dimensional vector spaces where a point from the natural distribution is unusually close to many other points. This is a well-known problem in infor…
SoK: Pitfalls in Evaluating Black-Box Attacks
Fnu Suya, Anshuman Suri, Tingwei Zhang +3
Numerous works study black-box attacks on image classifiers. However, these works make different assumptions on the adversary's knowledge and current literature lacks a cohesive or…
Stealthy Backdoors as Compression Artifacts
Yulong Tian, Fnu Suya, Fengyuan Xu +1
In a backdoor attack on a machine learning model, an adversary produces a model that performs well on normal inputs but outputs targeted misclassifications on inputs containing a s…
Scalable Attack on Graph Data by Injecting Vicious Nodes
Jihong Wang, Minnan Luo, Fnu Suya +3
Recent studies have shown that graph convolution networks (GCNs) are vulnerable to carefully designed attacks, which aim to cause misclassification of a specific node on the graph…
Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries
Fnu Suya, Jianfeng Chi, David Evans +1
We study adversarial examples in a black-box setting where the adversary only has API access to the target model and each query is expensive. Prior work on black-box adversarial ex…
Query-limited Black-box Attacks to Classifiers
Fnu Suya, Yuan Tian, David Evans +1
We study black-box attacks on machine learning classifiers where each query to the model incurs some cost or risk of detection to the adversary. We focus explicitly on minimizing t…