Optimizing large language models (LLMs) for downstream use cases often involves the
customization of pre-trained LLMs through further fine-tuning. Meta's open release of Llama …

Since the emergence of security concerns in artificial intelligence (AI), there has been
significant attention devoted to the examination of backdoor attacks. Attackers can utilize …

Backdoor attack intends to embed hidden backdoors into deep neural networks (DNNs), so
that the attacked models perform well on benign samples, whereas their predictions will be …

The Large Language Models (LLMs) are poised to offer efficient and intelligent services for
future mobile communication networks, owing to their exceptional capabilities in language …

The advancement of large language models (LLMs) has significantly enhanced the ability to
effectively tackle various downstream NLP tasks and unify these tasks into generative …

We investigate a new threat to neural sequence-to-sequence (seq2seq) models: training-
time attacks that cause models to “spin” their outputs so as to support an adversary-chosen …

Large scale language models (LLM) have received significant attention and found diverse
applications across various domains, but their development encounters challenges in real …

Prompt-based learning is vulnerable to backdoor attacks. Existing backdoor attacks against
prompt-based models consider injecting backdoors into the entire embedding layers or word …

With the emergence of foundation models (FMs) that are trained on large amounts of data at
scale and adaptable to a wide range of downstream applications, AI is experiencing a …

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 …