This paper provides a comprehensive review of the future of cybersecurity through
Generative AI and Large Language Models (LLMs). We explore LLM applications across …

This work identifies 18 foundational challenges in assuring the alignment and safety of large
language models (LLMs). These challenges are organized into three different categories …

The advent of the Internet of Things (IoT) has brought forth an era of unprecedented
connectivity, with an estimated 80 billion smart devices expected to be in operation by the …

While existing backdoor attacks have successfully infected multimodal contrastive learning
models such as CLIP they can be easily countered by specialized backdoor defenses for …

Data poisoning attacks manipulate training data to introduce unexpected behaviors into
machine learning models at training time. For text-to-image generative models with massive …

Large Language Models (LLMs) are increasingly deployed as the backend for a variety of
real-world applications called LLM-Integrated Applications. Multiple recent works showed …

Deep Neural Networks (DNNs) are vulnerable to backdoor attacks, which poison the training
set to alter the model prediction over samples with a specific trigger. While existing efforts …

Instruction tuning enhances large vision-language models (LVLMs) but raises security risks
through potential backdoor attacks due to their openness. Previous backdoor studies focus …

The rapid advancement of large language models (LLMs) has revolutionized artificial
intelligence, introducing unprecedented capabilities in natural language processing and …

Backdoor attacks are commonly executed by contaminating training data, such that a trigger
can activate predetermined harmful effects during the test phase. In this work, we present …