2 papers
cs.LG2024
VLMGuard: Bootstrapping Malicious Prompt Detectors from Unlabeled Vision-Language Prompts in the Wild
Junlin Fang, Wenyu Chen, Reshmi Ghosh +7
Vision-language Models (VLMs) are essential for contextual understanding of both visual and textual information. However, their vulnerability to adversarially manipulated inputs pr…
cs.CR2024
Dataset and Lessons Learned from the 2024 SaTML LLM Capture-the-Flag Competition
Edoardo Debenedetti, Javier Rando, Daniel Paleka +18
Large language model systems face important security risks from maliciously crafted messages that aim to overwrite the system's original instructions or leak private data. To study…