3 papers
cs.CL2025
Estimating LLM Consistency: A User Baseline vs Surrogate Metrics
Xiaoyuan Wu, Weiran Lin, Omer Akgul +1
Large language models (LLMs) are prone to hallucinations and sensitive to prompt perturbations, often resulting in inconsistent or unreliable generated text. Different methods have…
cs.CR2025
Attacking Autonomous Driving Agents with Adversarial Machine Learning: A Holistic Evaluation with the CARLA Leaderboard
Henry Wong, Clement Fung, Weiran Lin +3
To autonomously control vehicles, driving agents use outputs from a combination of machine-learning (ML) models, controller logic, and custom modules. Although numerous prior works…
cs.CR2025
LLM Whisperer: An Inconspicuous Attack to Bias LLM Responses
Weiran Lin, Anna Gerchanovsky, Omer Akgul +3
Writing effective prompts for large language models (LLM) can be unintuitive and burdensome. In response, services that optimize or suggest prompts have emerged. While such service…