Abstract Recent studies have indicated that large language models (LLMs) remain susceptible to adversarial attacks, despite enhanced robustness through the chain-of-thought (CoT) capability. However, this capability also introduces the potential for more covert and effective adversarial attack methods. This paper proposes a CoT Transfer Adversarial attack framework (CTTA) for general LLMs. Initially, we utilize a pre-trained model based on the transformer architecture and fine-tune it on various tasks to serve as a surrogate model. Subsequently, different levels of adversarial attack algorithms are utilized, and the generated adversarial samples are used as transfer samples. A thought chain-based adversarial transfer attack framework is constructed using transfer samples and thought chain techniques. Finally, various indicators are utilized to assess the performance of the general LLMs in response to this attack. The results demonstrate that the attack framework surpasses current state-of-the-art research. Numerous experiments on LLMs with varying performance and parameter sizes have validated the effectiveness, stability, and generalizability of this attack. The model’s error response and the superiority of this attack are thoroughly examined using attention by gradient technology, confirming the security threats posed by LLMs when leveraging CoT capability. This has significant implications for enhancing the security and robustness of LLMs.

Digital disinformation, such as those occurring on online social networks (OSNs), can influence public opinion, create mistrust and division, and impact decision- and policy-making. In this study, we propose a disinformation diffusion trend analysis and identification method, which uses social situation analytics and a multilevel attention network. First, we present a division and feature representation approach of social user circle based on the content sequence (internal driving factor) and social contextual information (external driving factor) of users associated with disinformation. Second, disinformation content feature, crowd response feature, and time-series feature are represented using embedding layer and bidirectional long short-term memory neural networks (Bi-LSTMs). We also present an attention mechanism model based on multifeature fusion, which can dynamically adjust the weight of each feature. On this foundation, the fused features are fed into the multilayer perceptron to identify the propagation quantity trend. According to the experimental results of real-world OSNs and social situation metadata, we conclude that while disinformation occurs across OSN platforms, the disinformation is more likely to spread widely in the original OSN platform. We also identify four typical disinformation propagation trends based on propagation patterns and propagation peak times. Findings from our experiments demonstrate that our proposed approach accurately identifies and predicts the diffusion trend of disinformation, which can then be used to inform mitigation strategy.

The dissemination of digital disinformation in online social networks (OSNs) has been the subject of extensive research, although many challenges remain, including the analysis and control of disinformation dissemination across different platforms (i.e., cross-platform). In this article, we investigate and analyze the spreading patterns and regularities of disinformation both within a single platform and across platforms. To explore the complex relationship between user propagation desire and behaviour within the same group, a user propagation desire inference model based on propagation characteristics (behaviour characteristics and time characteristics) and a bidirectional backpropagation (B-BP) deep neural network are constructed. Then, to avoid overfitting due to the interaction of users’ propagation behaviour and the correlation among propagation characteristics, a novel adaptive weighted particle swarm optimization evolutionary algorithm is utilized to further optimize the B-BP deep neural network. We design and conduct a series of evaluation experiments on the current global hot topics including but not limited to novel coronavirus-19 pandemic (COVID-19), food safety, medical and health, and environmental protection. By using a real-world social platform and its social situation metadata analysis, the experimental results show that the proposed method not only accurately predicts the level of user propagation desire under multiple behaviour interactions but also facilitates social platform managers in handling disinformation disseminators. Our findings reveal that the intensity of social users’ desires to spread disinformation is related to the topics and groups that users are interested in, while the propagation motivation of social users is not strong under topics that users are not interested in. Our studies also demonstrate that social users with propagation desires tend to utilize their familiar social platforms and local circles for communication, and the behaviour and desire to spread disinformation to the cross-platform are not strong. We posit that these findings can help inform online and, fine-grained governance and mitigation strategies other than “one size fits all” approaches (e.g., “account prohibition and deletion”), and hopefully minimize disinformation dissemination.