2 papers
cs.LG2026
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.LG2026
Are My Optimized Prompts Compromised? Exploring Vulnerabilities of LLM-based Optimizers
Andrew Zhao, Reshmi Ghosh, Vitor Carvalho +4
Large language model (LLM) systems increasingly power everyday AI applications such as chatbots, computer-use assistants, and autonomous robots, where performance often depends on…