Large language models (LLMs) are vulnerable to universal jailbreaks-prompting strategies
that systematically bypass model safeguards and enable users to carry out harmful …

In the past decade, considerable research effort has been devoted to securing machine
learning (ML) models that operate in adversarial settings. Yet, progress has been slow even …

Federated learning (FL) has attracted considerable interest in the medical domain due to its
capacity to facilitate collaborative model training while maintaining data privacy. However …

Vision-language models (VLMs), which process image and text inputs, are increasingly
integrated into chat assistants and other consumer AI applications. Without proper …

Human-AI conversations have gained increasing attention since the era of large language
models. Consequently, more techniques, such as input/output guardrails and safety …

Recent advances in generative AI have led to large multi-modal models (LMMs) capable of
simultaneously processing inputs of various modalities such as text, images, video, and …

Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that
induce harmful outputs. Existing safety benchmarks for VLMs primarily rely on automated …

Unsafe prompts pose significant safety risks to large language models (LLMs). Existing
methods for detecting unsafe prompts rely on data-driven fine-tuning to train guardrail …

We propose a universal adversarial attack on multimodal Large Language Models (LLMs)
that leverages a single optimized image to override alignment safeguards across diverse …

The rapid advancement of Large Language Models (LLMs) has introduced significant
challenges in moderating user-model interactions. While LLMs demonstrate remarkable …