6 citations · 16 across the 13 of their papers we have counts for
16 papers
Building Trustworthy Household Robots through Open-World Safety
Yu-Lin Tsai, Muxi Lyu, Roei Herzig +3
Foundation models are expanding robot capabilities, enabling robots to handle more diverse objects, follow sophisticated instructions, and operate in complex environments. These ad…
Repeat-After-Me: Black-Box Adaptive Visual Prompt Injection
Sizhe Chen, Yu-Lin Tsai, Ivan Evtimov +4
Prompt injection is widely recognized as a major security threat to AI agents that interact with untrusted external data, such as websites, documents, and emails. Prior work has sh…
Daydreaming: Stealing Hidden Agent Skills through Black-Box Task Interaction
Yu-Lin Tsai, Yu-An Lu, Ci-Yang Tsai +3
Agent skills bundle instructions, reference data, and executable helpers that let a general agent perform specialized tasks. Hosted providers can keep these files secret while sell…
ShannonProver: Towards Automating Formal Cryptographic Proofs
Yiping Ma, Yu-Lin Tsai, Mayank Rathee +4
Cryptographic proofs are produced at a scale that increasingly exceeds the community's ability to verify them manually. Machine-checked proofs offer a path toward scalable proof ve…
Hidden Thoughts Are Not Secret: Reasoning Trace Exposure in LLMs
Yu-An Lu, Ci-Yang Tsai, Yu-Lin Tsai +2
Reasoning traces have become a valuable form of learning signals for improving and transferring the capabilities of large language models. In particular, detailed traces can help d…
Filtering Memorization from Parameter-Space in Diffusion Models
Yu Zhe, Yang Jiayan, Wei Junhao +2
Low-Rank Adaptation (LoRA) has become a widely used mechanism for customizing diffusion models, enabling users to inject new visual concepts or styles through lightweight parameter…
VP-NTK: Exploring the Benefits of Visual Prompting in Differentially Private Data Synthesis
Chia-Yi Hsu, Jia-You Chen, Yu-Lin Tsai +4
Differentially private (DP) synthetic data has become the de facto standard for releasing sensitive data. However, many DP generative models suffer from the low utility of syntheti…
Data Poisoning Attacks to Locally Differentially Private Range Query Protocols
Ting-Wei Liao, Chih-Hsun Lin, Yu-Lin Tsai +5
Local Differential Privacy (LDP) has been widely adopted to protect user privacy in decentralized data collection. However, recent studies have revealed that LDP protocols are vuln…
BADTV: Unveiling Backdoor Threats in Third-Party Task Vectors
Chia-Yi Hsu, Yu-Lin Tsai, Yu Zhe +6
Task arithmetic in large-scale pre-trained models enables agile adaptation to diverse downstream tasks without extensive retraining. By leveraging task vectors (TVs), users can per…
Differentially Private Fine-Tuning of Diffusion Models
Yu-Lin Tsai, Yizhe Li, Zekai Chen +4
The integration of Differential Privacy (DP) with diffusion models (DMs) presents a promising yet challenging frontier, particularly due to the substantial memorization capabilitie…
Safe LoRA: the Silver Lining of Reducing Safety Risks when Fine-tuning Large Language Models
Chia-Yi Hsu, Yu-Lin Tsai, Chih-Hsun Lin +3
While large language models (LLMs) such as Llama-2 or GPT-4 have shown impressive zero-shot performance, fine-tuning is still necessary to enhance their performance for customized…
Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
Yu-Lin Tsai, Chia-Yi Hsu, Chulin Xie +6
Diffusion models for text-to-image (T2I) synthesis, such as Stable Diffusion (SD), have recently demonstrated exceptional capabilities for generating high-quality content. However,…
Exploring the Benefits of Visual Prompting in Differential Privacy
Yizhe Li, Yu-Lin Tsai, Xuebin Ren +2
Visual Prompting (VP) is an emerging and powerful technique that allows sample-efficient adaptation to downstream tasks by engineering a well-trained frozen source model. In this w…
Certified Robustness of Quantum Classifiers against Adversarial Examples through Quantum Noise
Jhih-Cing Huang, Yu-Lin Tsai, Chao-Han Huck Yang +4
Recently, quantum classifiers have been found to be vulnerable to adversarial attacks, in which quantum classifiers are deceived by imperceptible noises, leading to misclassificati…
Formalizing Generalization and Robustness of Neural Networks to Weight Perturbations
Yu-Lin Tsai, Chia-Yi Hsu, Chia-Mu Yu +1
Studying the sensitivity of weight perturbation in neural networks and its impacts on model performance, including generalization and robustness, is an active research topic due to…
Non-Singular Adversarial Robustness of Neural Networks
Yu-Lin Tsai, Chia-Yi Hsu, Chia-Mu Yu +1
Adversarial robustness has become an emerging challenge for neural network owing to its over-sensitivity to small input perturbations. While being critical, we argue that solving t…