3 citations · 4 across the 5 of their papers we have counts for
6 papers
SpinDoctor: Finding Innocuous Prompts for Biased Image Generation
Anna Gerchanovsky, Omer Akgul, Lujo Bauer +1
We introduce SpinDoctor, a method for discovering innocuous prompts for text-to-image models that result in images with a chosen target bias, while retaining the quality of the ima…
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…
Perry: A High-level Framework for Accelerating Cyber Deception Experimentation
Brian Singer, Yusuf Saquib, Lujo Bauer +1
Cyber deception aims to distract, delay, and detect network attackers with fake assets such as honeypots, decoy credentials, or decoy files. However, today, it is difficult for ope…
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…
Incalmo: An Autonomous LLM-assisted System for Red Teaming Multi-Host Networks
Brian Singer, Keane Lucas, Lakshmi Adiga +3
Security operators use red teams to simulate real attackers and proactively find defense gaps. In realistic enterprise settings, this involves executing multi-host network attacks…
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…