Beijing University of Posts and Telecommunications

Phishing attacks continue to grow in scale and sophistication, causing substantial financial losses and privacy breaches worldwide. Recent advances in large language models (LLMs) have brought significant changes to the generation and detection of phishing content. This study systematically investigates the dual role of LLMs in facilitating phishing attacks and strengthening countermeasures. Using the PRISMA methodology, authors screened 142 records published between January 2023 and April 2025 and identified 36 eligible studies from major academic databases, including IEEE Xplore, ScienceDirect, ACM Digital Library, Web of Science, and Scopus. A comprehensive and rigorous analysis was conducted of research trends/themes over time, dataset characteristics, and the LLM architectures/models employed. The findings reveal that most studies relied on manually generated datasets rather than publicly available benchmark datasets, and that GPT-based models received considerably more attention than other LLM architectures. The review demonstrates that LLMs substantially enhance the generation of phishing content by producing coherent, contextually relevant, and persuasive email and website content. This capability lowers the technical barrier for attackers and potentially increases attack effectiveness. Conversely, LLMs also strengthen defensive strategies by enabling more effective analysis of textual and visual content for phishing detection. In many cases, LLM-based approaches outperform traditional machine learning and deep learning methods and, in certain contexts, approach or match human-level performance. Overall, the findings suggest that LLMs have accelerated and automated phishing-related processes, simultaneously intensifying the threat landscape and advancing defensive capabilities. • The first in-depth study to review LLMs usage in Phishing attack generation and detection. • The study reveals LLMs have accelerated and automated phishing-related processes, elevating both threats and defence mechanisms. • GenAI-based multimodal phishing attacks are on the rise due to the wider adoption of GenAI tools in general.

Abstract A numerical simulation model is developed for a Yb:YAG Innoslab amplifier. By unfolding the multi-pass folded optical path into a one-dimensional slicing model, the model employs differential iterative calculations. This enables high-precision characterization of key physical processes, including spot size evolution, pump laser saturated absorption distribution, Yb:YAG reabsorption effect, and spatial dynamic coupling between pump and seed beam. Notably, a novel correction mechanism for the small-signal gain coefficient in the overlap regions of adjacent passes is proposed for the first time, which specifically resolves the energy re-extraction issue neglected in previous models. This correction significantly enhances the computational accuracy and physical fidelity of the model. Validations against existing experimental data demonstrate high consistency between simulated and measured results, confirming the model’s excellent applicability and reliability. The work provides a reliable theoretical basis for the structural optimization and parameter tuning of Innoslab amplifiers.

In-context segmentation, also known as one-shot segmentation, aims to segment objects based on a single labeled example. While the Segment Anything Model (SAM) excels in interactive segmentation, it is not inherently designed for in-context tasks. To bridge this gap, we propose a new Dual Consistency SAM (DC-SAM), a prompt-tuning framework that adapts SAM and SAM2 for both image and video in-context segmentation. Instead of relying solely on pre-trained backbones, DC-SAM enhances the prompt encoder by generating high-quality visual prompts through feature fusion. Furthermore, we introduce a novel cycle-consistent cross-attention mechanism to enforce alignment between fused features and visual prompts, complemented by a dual-branch design incorporating discriminative positive and negative prompts. Additionally, we extend DC-SAM to the video domain via a novel mask-tube training strategy. To facilitate research, we curate the first In-Context Video Object Segmentation (IC-VOS) benchmark. Extensive experiments demonstrate that DC-SAM achieves state-of-the-art performance, yielding 55.5 mIoU (+1.4) on COCO-20$^{i}$i, 73.0 (+1.1) mIoU on PASCAL-5$^{i}$i, and a $\mathcal {J} { \& amp;} \mathcal{F}$J&F score of 71.52 on IC-VOS.