1 citations · 1 across the 3 of their papers we have counts for
5 papers
Where Do LLMs Decide to Break the Rules? Mechanistic Localization of Prompt Injection Compliance
Rui Wen, Jiayang Liu, Zeyu Yang +2
When a prompt injection attack succeeds, a Large Language Model (LLM) abandons its assigned system role to comply with an adversarial instruction. While prior work has extensively…
No Free Efficiency: Revisiting the Trade-off Between Training Efficiency and Model Vulnerability
Yiyong Liu, Jun Sakuma, Michael Backes +1
Training efficiency has become the central driver of recent progress in foundation models. To overcome the massive computational and data requirements of large-scale training, rese…
Harmless Yet Harmful: Neutral Prompting Attacks for Stealthy Hallucination Steering in Agent Skills
Chia-Yi Hsu, Chia-Mu Yu, Chun-Ying Huang +1
LLM-powered coding agents increasingly participate in software development workflows by generating code, selecting dependencies, and producing package installation commands. This c…
One Leak Away: How Pretrained Model Exposure Amplifies Jailbreak Risks in Finetuned LLMs
Yixin Tan, Zhe Yu, Rui Wen +1
Finetuning pretrained large language models (LLMs) has become the standard paradigm for developing downstream applications. However, its security implications remain unclear, parti…
Remembering Transformer for Continual Learning
Yuwei Sun, Ippei Fujisawa, Arthur Juliani +2
Neural networks encounter the challenge of Catastrophic Forgetting (CF) in continual learning, where new task learning interferes with previously learned knowledge. Existing data f…