4 citations · 12 across the 19 of their papers we have counts for
23 papers
Diagnose Before You Refine: Multi-Dimensional Judge Feedback for Query-Efficient Jailbreak Attacks
Yixin Tan, Chia-Yi Hsu, Jiayang Liu +2
Automated jailbreak attacks rely on evaluation feedback to optimize adversarial prompts, but existing methods often lack fine-grained diagnostic information. We propose Jev-Guided…
Encrypted Skills Can Become New Security Exploits If AI Agents Know How to Decipher
Yan-Lun Chen, Pin-Yu Chen, Chia-Mu Yu
Third-party skills can expose LLM agents to malicious instructions hidden by encoding. We introduce Ciphered Skill Injection (CSI), which embeds such instructions in skill files fo…
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…
RAS: Measuring LLM Safety Through Refusal Alignment
Chang-Chieh Huang, Yan-Lun Chen, Chia-Mu Yu +1
Safety evaluation of large language models (LLMs) is commonly performed by querying models with unsafe or jailbreak prompts and judging whether their outputs violate a safety polic…
CodeSentinel: A Three-Layer Defense Against Indirect Prompt Injection in Code Contexts
Po-Han Cheng, Chia-Mu Yu, Ying-Dar Lin +2
Code large language models increasingly retrieve external code context from repositories, documentation, issue threads, and coding-agent environments, creating an indirect prompt-i…
Tracing Target Answers in Poisoned Retrieval Corpora via Token Influence Attribution
Yan-Lun Chen, Pin-Yu Chen, Chia-Mu Yu +3
Retrieval-Augmented Generation (RAG) systems are vulnerable to corpus poisoning attacks that manipulate model outputs through malicious retrieved documents. Existing detection meth…
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…
Trust Me, Import This: Dependency Steering Attacks via Malicious Agent Skills
Yiyong Liu, Chia-Yi Hsu, Chun-Ying Huang +3
LLM-powered coding agents increasingly make software supply chain decisions. They generate imports, recommend packages, and write installation commands. Prior work showed that thes…
Model Reprogramming Demystified: A Neural Tangent Kernel Perspective
Ming-Yu Chung, Jiashuo Fan, Hancheng Ye +5
Model Reprogramming (MR) is a resource-efficient framework that adapts large pre-trained models to new tasks with minimal additional parameters and data, offering a promising solut…
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…
Layer-Aware Task Arithmetic: Disentangling Task-Specific and Instruction-Following Knowledge
Yan-Lun Chen, Yi-Ru Wei, Chia-Yi Hsu +5
Large language models (LLMs) demonstrate strong task-specific capabilities through fine-tuning, but merging multiple fine-tuned models often leads to degraded performance due to ov…
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…
DiffuseKronA: A Parameter Efficient Fine-tuning Method for Personalized Diffusion Models
Shyam Marjit, Harshit Singh, Nityanand Mathur +3
In the realm of subject-driven text-to-image (T2I) generative models, recent developments like DreamBooth and BLIP-Diffusion have led to impressive results yet encounter limitation…
Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective
Ming-Yu Chung, Sheng-Yen Chou, Chia-Mu Yu +3
Dataset distillation offers a potential means to enhance data efficiency in deep learning. Recent studies have shown its ability to counteract backdoor risks present in original tr…
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 Differentially Private Pre-training and Parameter-Efficient Fine-tuning for Table Transformers
Xilong Wang, Chia-Mu Yu, Pin-Yu Chen
For machine learning with tabular data, Table Transformer (TabTransformer) is a state-of-the-art neural network model, while Differential Privacy (DP) is an essential component to…
DPAF: Image Synthesis via Differentially Private Aggregation in Forward Phase
Chih-Hsun Lin, Chia-Yi Hsu, Chia-Mu Yu +2
Differentially private synthetic data is a promising alternative for sensitive data release. Many differentially private generative models have been proposed in the literature. Unf…
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…
Meta Adversarial Perturbations
Chia-Hung Yuan, Pin-Yu Chen, Chia-Mu Yu
A plethora of attack methods have been proposed to generate adversarial examples, among which the iterative methods have been demonstrated the ability to find a strong attack. Howe…
Real-World Adversarial Examples involving Makeup Application
Chang-Sheng Lin, Chia-Yi Hsu, Pin-Yu Chen +1
Deep neural networks have developed rapidly and have achieved outstanding performance in several tasks, such as image classification and natural language processing. However, recen…