20 citations · 22 across the 4 of their papers we have counts for
4 papers
Prmpt: Sanitizing Sensitive Prompts for LLMs
Amrita Roy Chowdhury, David Glukhov, Divyam Anshumaan +4
The rise of large language models (LLMs) has introduced new privacy challenges, particularly during inference where sensitive information in prompts may be exposed to proprietary L…
Breach By A Thousand Leaks: Unsafe Information Leakage in `Safe' AI Responses
David Glukhov, Ziwen Han, Ilia Shumailov +2
Vulnerability of Frontier language models to misuse and jailbreaks has prompted the development of safety measures like filters and alignment training in an effort to ensure safety…
LLM Censorship: A Machine Learning Challenge or a Computer Security Problem?
David Glukhov, Ilia Shumailov, Yarin Gal +2
Large language models (LLMs) have exhibited impressive capabilities in comprehending complex instructions. However, their blind adherence to provided instructions has led to concer…
Augment then Smooth: Reconciling Differential Privacy with Certified Robustness
Jiapeng Wu, Atiyeh Ashari Ghomi, David Glukhov +3
Machine learning models are susceptible to a variety of attacks that can erode trust, including attacks against the privacy of training data, and adversarial examples that jeopardi…