With the growing incorporation of deep neural network (DNN) models into modern software systems, the prohibitive construction costs of DNN models have become a significant challenge in software development. To address this challenge, model reuse has been widely applied to reduce model training costs; however, indiscriminately reusing an entire model may incur significant inference overhead. Consequently, DNN modularization—borrowing the idea of modularization in software engineering—has increasingly gained attention, enabling module reuse by decomposing a DNN model into modules. In particular, the emerging modularizing-while-training (MwT) paradigm, which outperforms modularizing-after-training by incorporating modularization into the model’s training process, has been demonstrated as a more effective approach for DNN modularization. However, existing MwT approaches focus on small-scale convolutional neural network (CNN) models at the convolutional kernel level. They struggle to handle diverse DNNs and large-scale models, particularly Transformer-based models, which consistently achieve state-of-the-art results across various tasks. To address these limitations, we propose NeMo, a scalable and more generalizable MwT approach. NeMo operates at the neuron level—a fundamental component common to all DNNs—thereby ensuring applicability to Transformers and various DNN architectures. Moreover, we design a contrastive learning-based modular training method, equipped with an effective composite loss function, hence being scalable to large-scale models. Comprehensive experiments on two Transformer-based models and four CNN models across two widely used classification datasets demonstrate NeMo’s superiority over the state-of-the-art MwT method. Results show average performance gains of 1.72% in module classification accuracy and a 58.10% reduction in module size. Our findings demonstrate that NeMo exhibits efficacy across both CNN and large-scale Transformer-based models. Moreover, a case study based on open source projects demonstrates the potential benefits of NeMo in practical scenarios, offering a promising approach for achieving scalable and generalizable DNN modularization.

Driven by the rapid development of Large Language Models (LLMs), LLM-based agents have been developed to handle various real-world applications, including finance, healthcare, and shopping, etc. It is crucial to ensure the reliability and security of LLM-based agents during applications. However, the safety issues of LLM-based agents are currently under-explored. In this work, we take the first step to investigate one of the typical safety threats, backdoor attack, to LLM-based agents. We first formulate a general framework of agent backdoor attacks, then we present a thorough analysis of different forms of agent backdoor attacks. Specifically, compared with traditional backdoor attacks on LLMs that are only able to manipulate the user inputs and model outputs, agent backdoor attacks exhibit more diverse and covert forms: (1) From the perspective of the final attacking outcomes, the agent backdoor attacker can not only choose to manipulate the final output distribution, but also introduce the malicious behavior in an intermediate reasoning step only, while keeping the final output correct. (2) Furthermore, the former category can be divided into two subcategories based on trigger locations, in which the backdoor trigger can either be hidden in the user query or appear in an intermediate observation returned by the external environment. We implement the above variations of agent backdoor attacks on two typical agent tasks including web shopping and tool utilization. Extensive experiments show that LLM-based agents suffer severely from backdoor attacks and such backdoor vulnerability cannot be easily mitigated by current textual backdoor defense algorithms. This indicates an urgent need for further research on the development of targeted defenses against backdoor attacks on LLM-based agents. Warning: This paper may contain biased content.