OpenRaiser/Intern-Atlas huggingface dataset:
A Comprehensive Security Evaluation Framework for Chinese Large Language Models
Authors: Yuli Yang, Zhenhua Huang, Zhaohong Jia, Junfeng Fang
Venue: WWW
Year: 2025

This paper presents a pioneering security evaluation framework for Chinese generative large language models (LLMs), addressing a significant gap in the field. The framework is supported by a comprehensive dataset of over 12,000 samples, systematically organized across four key dimensions: value evaluation, robustness, training data security, and prompt injection attacks. Each dimension is further subdivided into detailed subcategories. Our two-phase progressive attack strategy first assesses the models' baseline defenses using basic prompts. In the second phase, the attack complexity is escalated through semantic perturbations, formatting disturbances, contextual interference, and scenario design. This approach methodically uncovers initial vulnerabilities and effectively exposes deeper security flaws, demonstrating superior efficacy compared to existing methods in challenging both basic and advanced model defenses. This work underscores the critical need for such a framework, enhancing the security and reliability of AI systems. In the domain of ''Personal Intelligence,'' where accurate user modeling and preference alignment are essential, it contributes to the development of safer, more reliable AI applications for real-world deployment.
LUSTER: Link Prediction Utilizing Shared-Latent Space Representation in Multi-Layer Networks
Authors: Ruohan Yang, Muhammad Asif Ali, Huan Wang, Junyang Chen, Di Wang
Venue: WWW
Year: 2025

Link prediction in multi-layer networks is a longstanding issue that predicts missing links based on the observed structures across all layers. Existing link prediction methods in multi-layer network typically merge the multi-layer network into a single-layer network and/or perform explicit calculations using intra-layer and inter-layer similarity metrics. However, these approaches often overlook the role of coupling in multi-layer networks, specifically the shared information and latent relationships between layers, which in turn limits prediction performance. This calls the need for methods that can extract representations in a shared-latent space to enhance inter-layer information sharing and prediction performance. In this paper, we propose a novel end-to-end framework namely: Link prediction Utilizing Shared-laTent spacE Representation (LUSTER) in multi-layer networks. LUSTER consists of four key modules: the representation extractor, the latent space learner, the complementary enhancer, and the link predictor. The representation extractor focuses on learning the intra-layer representations of each layer, capturing the data characteristics within the layer. The latent space learner extracts representations from the shared-latent space across different network layers through adversarial training. The complementary enhancer combines the intra-layer representations and the shared-latent space representations through orthogonal fusion, providing comprehensive information. Finally, the link predictor uses the enhanced representations to predict missing links. Extensive experimental analyses demonstrate that LUSTER outperforms state-of-the-art methods for link prediction in multi-layer networks, improving the AUC metric by up to 15.87%.
Construction of Domain-Specific Knowledge Graph for Advanced Persistent Threat Behaviour Analysis and Detection
Authors: Yitian Yang, Huaming Chen
Venue: WWW
Year: 2025

Advanced Persistent Threat (APT) represents a sophisticated and targeted attack campaign, often orchestrated by well-resourced organizations. Understanding APT is crucial for adapting to their evolving tactics and effectively mitigating their infiltration methods. A key approach to accurately analysing and detecting APTs involves studying the behaviour, identifying their attack stage and uncovering the employed components. Existing works for APT detection heavily rely on network traffic analysis, limiting their practical applicability in real-world scenarios. This paper introduces a novel method to analyse and detect APT behaviours by constructing a domain-specific knowledge graph (APT-KG), utilising the MITRE ATT&CK framework as its foundation. A hierarchical clustering-based model is proposed to uncover the correlations among various network attack techniques. It first vectorizes the attack techniques according to ATT&CK standards, filtering out low-frequency techniques to optimise dimensionality reduction. Parent-child relationships within the ATT&CK classification further facilitate this process. We observe that this strategy can reveal fully connected relationships among high-frequency techniques, while connections are preserved within clusters between high and low-frequency techniques. This structured representation enables the inference of potential attack patterns, achieving a prediction accuracy of 88.11%. We then integrate the identified associations with APT-KG. Experimental validation demonstrates that APT-KG significantly enhances understanding of attack interrelations and improves the efficiency of APT detection and response mechanisms.
TransBox: EL++-closed Ontology Embedding
Authors: Hui Yang, Jiaoyan Chen, Uli Sattler
Venue: WWW
Year: 2025

OWL (Web Ontology Language) ontologies, which are able to represent both relational and type facts as standard knowledge graphs and complex domain knowledge in Description Logic (DL) axioms, are widely adopted in domains such as healthcare and bioinformatics. Inspired by the success of knowledge graph embeddings, embedding OWL ontologies has gained significant attention in recent years. Current methods primarily focus on learning embeddings for atomic concepts and roles, enabling the evaluation based on normalized axioms through specially designed score functions. However, they often neglect the embedding of complex concepts, making it difficult to infer with more intricate axioms. This limitation reduces their effectiveness in advanced reasoning tasks, such as Ontology Learning and ontology-mediated Query Answering. In this paper, we propose EL++-closed ontology embeddings which are able to represent any logical expressions in DL EL++ via composition. Furthermore, we develop TransBox, an effective EL++ -closed ontology embedding method that can handle many-to-one, one-to-many and many-to-many relations. Our extensive experiments demonstrate that TransBox often achieves state-of-the-art performance across various real-world datasets for predicting complex axioms.
Enhancing Web Service Anomaly Detection via Fine-grained Multi-modal Association and Frequency Domain Analysis
Authors: Xixuan Yang, Xin Huang, Chiming Duan, Tong Jia, Shandong Dong, Ying Li, Gang Huang
Venue: WWW
Year: 2025

Anomaly detection is crucial for ensuring the stability and reliability of web service systems. Logs and metrics contain multiple information that can reflect the system's operational state and potential anomalies. Thus, existing anomaly detection methods use logs and metrics to detect web service systems' anomalies through data fusion approaches. They associate logs and metrics using coarse-grained time window alignment and capture the normal patterns of system operation through reconstruction. However, these methods have two issues that limit their performance in anomaly detection. First, due to asynchrony between logs and metrics, coarse-grained time window alignment cannot achieve a precise association between the two modalities. Second, reconstruction-based methods suffer from severe overgeneralization problems, resulting in anomalies being accurately reconstructed. In this paper, we propose a novel anomaly detection method named FFAD to address these two issues. On the one hand, FFAD employs graph-based alignment to mine and extract associations between the modalities from the constructed log-metric relation graph, achieving precise associations between logs and metrics. On the other hand, we improve the model's fit to normal data distributions through Fourier Frequency Focus, thereby enhancing the effectiveness of anomaly detection. We validated the effectiveness of our model on two real-world industrial datasets and one open-source dataset. The results show that our method achieves an average anomaly detection F1-score of 93.6%, representing an 8.8% improvement over previous state-of-the-art methods.
EdgeThemis: Ensuring Model Integrity for Edge Intelligence
Authors: Jiyu Yang, Qiang He, Zheyu Zhou, Xiaohai Dai, Feifei Chen, Cong Tian, Yun Yang
Venue: WWW
Year: 2025

Machine learning (ML) models are widely deployed on edge nodes, such as mobile phones and edge servers, to power a wide range of AI applications over the web. Ensuring the integrity of these edge models is paramount, as they are subject to corruption caused by software/hardware exceptions and malicious tampering, which may undermine model performance, incur economic losses, and pose health risks. Existing data integrity mechanisms designed for files stored on disks cannot properly verify the integrity of models running in GPUs or mitigate the new integrity threats against edge models. This paper proposes EdgeThemis, a novel mechanism for verifying the integrity of edge models through sentinel verification. To enable verifiability for a model M, EdgeThemis embeds a sentinel backdoor and a verification module into M. Then, a challenger can send verification requests to the edge node hosting M to verify its integrity. Next, the sentinel activates the verification module to generate a unique integrity proof tied to the identity of the edge node for verification. Finally, the challenger can verify the integrity proof to detect model corruption. Theoretical analysis proves that EdgeThemis can properly mitigate potential integrity threats against edge models. Experiments demonstrate that EdgeThemis achieves a verification accuracy of 100.00% across various models and different types of model corruption with robustness against replay attacks, theft attacks, and replacement attacks.
Training Large Recommendation Models via Graph-Language Tokens Alignment
Authors: Mingdai Yang, Zhiwei Liu, Liangwei Yang, Xiaolong Liu, Chen Wang, Hao Peng, Philip S. Yu
Venue: WWW
Year: 2025

Recommender systems (RS) have become essential tools for helping users efficiently navigate the overwhelming amount of information on e-commerce and social platforms. However, traditional RS relying on Collaborative Filtering (CF) struggles to integrate the rich semantic information from textual data. Meanwhile, large language models (LLMs) have shown promising results in natural language processing, but directly using LLMs for recommendation introduces challenges, such as ambiguity in generating item predictions and inefficiencies in scalability. In this paper, we propose a novel framework to train Large Recommendation models via Graph-Language Token Alignment. By aligning item and user nodes from the interaction graph with pretrained LLM tokens, GLTA effectively leverages the reasoning abilities of LLMs. Furthermore, we introduce Graph-Language Logits Matching (GLLM) to optimize token alignment for end-to-end item prediction, eliminating ambiguity in the free-form text as recommendation results. Extensive experiments on three benchmark datasets demonstrate the effectiveness of GLTA, with ablation studies validating each component.
LLM as Auto-Prompt Engineer: Automated NER Prompt Optimisation
Authors: Can Yang, Bernardo Pereira Nunes, Sergio Rodríguez Méndez
Venue: WWW
Year: 2025

The emergence of Large Language Models (LLMs) has revolutionised natural language processing capabilities. However, despite these advances, effectively optimising prompts for knowledge extraction tasks like Named Entity Recognition (NER) remains challenging. This paper presents a zero-shot automated prompt engineering approach that decomposes the NER task into two phases: entity boundary detection and entity classification. Our method incorporates structured task analysis, automated prompt generation, test case generation, and iterative optimisation, requiring no labelled training examples. This decomposition allows for more precise entity recognition while maintaining the efficiency. Through experimentation on the CoNLL-2003 dataset using standard exact-match evaluation metrics, our approach demonstrates improvements over unified methods, achieving a 75.39% F1 score compared to baseline approaches (72.90%). The key contributions include: (1) A structured pipeline for zero-shot automated prompt engineering in NER tasks that addresses the challenges of prompt design and optimisation; (2) A two-phase approach to NER tasks that separates boundary detection from entity classification; and (3) Experimental results demonstrating the effectiveness of our approach compared to existing zero-shot approaches in NER tasks.
PCL: Prompt-based Continual Learning for User Modeling in Recommender Systems
Authors: Mingdai Yang, Fan Yang, Yanhui Guo, Shaoyuan Xu, Tianchen Zhou, Yetian Chen, Simone Shao, Jia Liu, Yan Gao
Venue: WWW
Year: 2025

User modeling in large e-commerce platforms aims to optimize user experiences by incorporating various customer activities. Traditional models targeting a single task often focus on specific business metrics, neglecting the comprehensive user behavior, and thus limiting their effectiveness. To develop more generalized user representations, some existing work adopts Multi-task Learning (MTL) approaches. But they all face the challenges of optimization imbalance and inefficiency in adapting to new tasks. Continual Learning (CL), which allows models to learn new tasks incrementally and independently, has emerged as a solution to MTL's limitations. However, CL faces the challenge of catastrophic forgetting, where previously learned knowledge is lost when the model is learning the new task. Inspired by the success of prompt tuning in Pretrained Language Models (PLMs), we propose PCL, a Prompt-based Continual Learning framework for user modeling, which utilizes position-wise prompts as external memory for each task, preserving knowledge and mitigating catastrophic forgetting. Additionally, we design contextual prompts to capture and leverage inter-task relationships during prompt tuning. We conduct extensive experiments on real-world datasets to demonstrate PCL's effectiveness.
Towards Multimodal Inductive Learning: Adaptively Embedding MMKG via Prototypes
Authors: Shundong Yang, Jing Yang, Xiaowen Jiang, Yuan Gao, Laurence T. Yang, Ruikun Luo, Jieming Yang
Venue: WWW
Year: 2025

Multimodal Knowledge Graphs (MMKG) models integrate multimodal contexts to improve link prediction performance. All existing MMKG models follow the transductive setting with a fixed predefined set, meaning that all the entities, relations, and multimodal information in the test graph are observed during training. This hinders their generalization to real-world MMKG with unseen entities and relations. Intuitively, a MMKG model trained on DBpedia cannot infer on Freebase. To address above limitations, we make the first attempt towards inductive learning for MMKG and propose a multimodal Inductive MMKG model (IndMKG) that is universal and transferable to any MMKG. Distinct from existing transductive methods, our model does not rely on specific trained embeddings; instead, IndMKG generates adaptive embeddings conditioned on any new MMKG via multimodal prototypes. Specifically, we construct class-adaptive prototypes to appropriately characterize the multimodal feature distribution of the given graph and equip IndMKG with robust adaptability to multimodal information across MMKGs. In addition, IndMKG learns non-specific structural embeddings based on meta relations. Such strategies tackle the challenge of notable multimodal feature discrepancies in cross-graph induction and allow the pre-trained IndMKG model to effectively zero-shot generalize to any MMKG. The strong performance in both inductive and transductive settings, across more than 20+ different scenarios, confirms the effectiveness and robustness of IndMKG. Our code is released at https://github.com/MMKGer/IndMKG/.
Grad: Guided Relation Diffusion Generation for Graph Augmentation in Graph Fraud Detection
Authors: Jie Yang, Rui Zhang, Ziyang Cheng, Dawei Cheng, Guang Yang, Bo Wang
Venue: WWW
Year: 2025

Nowadays, Graph Fraud Detection (GFD) in financial scenarios has become an urgent research topic to protect online payment security. However, as organized crime groups are becoming more professional in real-world scenarios, fraudsters are employing more sophisticated camouflage strategies. Specifically, fraudsters disguise themselves by mimicking the behavioral data collected by platforms, ensuring that their key characteristics are consistent with those of benign users to a high degree, which we call Adaptive Camouflage. Consequently, this narrows the differences in behavioral traits between them and benign users within the platform's database, thereby making current GFD models lose efficiency. To address this problem, we propose a relation diffusion-based graph augmentation model Grad. In detail, Grad leverages a supervised graph contrastive learning module to enhance the fraud-benign difference and employs a guided relation diffusion generator to generate auxiliary homophilic relations from scratch. Based on these, weak fraudulent signals would be enhanced during the aggregation process, thus being obvious enough to be captured. Extensive experiments have been conducted on two real-world datasets provided by WeChat Pay, one of the largest online payment platforms with billions of users, and three public datasets. The results show that our proposed model Grad outperforms SOTA methods in both various scenarios, achieving at most 11.10% and 43.95% increases in AUC and AP, respectively.
CascadeRCG: Retrieval-Augmented Generation for Enhancing Professionalism and Knowledgeability in Online Mental Health Support
Authors: Di Yang, Jingwei Zhu, Haihong Wu, Minghuan Tan, Chengming Li, Min Yang
Venue: WWW
Year: 2025

Online mental health support(OMHS) plays a crucial role in promoting well-being, but the shortage of mental health professionals necessitates automated systems to address complex care needs. While large language models (LLMs) are widely adopted, they often fall short in OMHS settings due to the complexity and ambiguity of the questions posed. Additionally, providing accurate answers requires extensive knowledge, which LLMs may lack, leading to responses that often lack depth, professionalism, and critical detail. To address these limitations, we introduce a new task tailored to OMHS scenarios, focusing on enhancing the professionalism and knowledgeability of generated responses. Furthermore, we propose a comprehensive benchmark designed to systematically evaluate the quality of responses. Building on these foundations, we propose the CascadeRCG framework, an optimized approach based on Retrieval-Augmented Generation (RAG). This framework first employs a knowledge management strategy, then introduces a two-stage cross-iterative Retrieval mechanism and a Clustering-then-summarizing module, followed by the final Generation stage. Experimental results on both single-turn and multi-turn psychological dialogue datasets, compared to other RAG-based baselines across different LLMs, show significant improvements in response professionalism and knowledge depth. This enhancement in response quality provides an effective methodology and strategy for further improving OMHS systems. Our code is available at https://github.com/CAS-SIAT-XinHai/CascadeRCG.
Do Not Trust What They Tell: Exposing Malicious Accomplices in Tor via Anomalous Circuit Detection
Authors: Yixuan Yao, Ming Yang, Zixia Liu, Kai Dong, Xiaodan Gu, Chunmian Wang
Venue: WWW
Year: 2025

The Tor network, while offering anonymity through traffic routing across volunteer-operated nodes, remains vulnerable to attacks that aim to deanonymize users by correlating traffic patterns between colluded entry and exit nodes in circuits. This paper presents a novel approach for detecting anomalous circuits in the Tor network, and for the first time provides a more comprehensive identification of potential malicious accomplice nodes in Tor by taking roles of nodes in anomalous circuits into consideration. Our method strategically utilizes modified middle nodes to capture traffic data, followed by a novel circuit classification based on traffic patterns to pinpoint concerned circuits. Two kinds of anomalies are identified: routing anomalies and usage anomalies, that respectively represent the anomalies with explicit or implicit violation of Tor's circuit construction guidelines. This leads to a successful revealing of totally 1,960 anomalous nodes in Tor. Furthermore, we apply clustering analysis with considering corresponding anomalous circuits and other key characteristics to the detected anomalous nodes, revealing potential hidden organizations behind these nodes that can threaten the network's security. Our findings highlight the necessity for the Tor project to adopt targeted mitigation strategies to enhance overall network security and privacy.
Trustworthy AI under Imperfect Web Data
Authors: Jiangchao Yao, Feng Liu, Bo Han
Venue: WWW
Year: 2025

Trustworthy AI is crucial for web applications, since it ensures user data privacy, enhances security, and fosters user confidence. As web applications increasingly rely on AI for personalization and decision-making, maintaining transparency and accountability becomes essential to prevent bias, misinformation, and unethical practices. By building trust, developers can create safer and more reliable experiences, ultimately promoting user engagement and satisfaction. However, when dataset sizes grow bigger with the rapid web data collection, it is laborious and expensive to obtain perfect data (e.g., clean, safe, and balanced data). As a result, the volume of imperfect data becomes enormous, e.g., web-scale image and speech data with noisy labels, images with specific noise, and long-tail-distributed data. However, standard learning methods assumes that the supervised information is fully correct and intact. Therefore, imperfect data harms the performance of most of the standard learning algorithms, and sometimes even makes existing algorithms break down. In this tutorial, we focus on the algorithmic design of trustworthy AI when facing three types of imperfect data: noisy data, adversarial data, and long-tailed data in the real-world web applications.
Intelligent Legal Assistant: An Interactive Clarification System for Legal Question Answering
Authors: Rujing Yao, Yiquan Wu, Tong Zhang, Xuhui Zhang, Yuting Huang, Yang Wu, Jiayin Yang, Changlong Sun, Fang Wang, Xiaozhong Liu
Venue: WWW
Year: 2025

The rise of large language models has opened new avenues for users seeking legal advice. However, users often lack professional legal knowledge, which can lead to questions that omit critical information. This deficiency makes it challenging for traditional legal question-answering systems to accurately identify users' actual needs, often resulting in imprecise or generalized advice. In this work, we develop a legal question-answering system called Intelligent Legal Assistant, which interacts with users to precisely capture their needs. When a user poses a question, the system requests that the user select their geographical location to pinpoint the applicable laws. It then generates clarifying questions and options based on the key information missing from the user's initial question. This allows the user to select and provide the necessary details. Once all necessary information is provided, the system produces an in-depth legal analysis encompassing three aspects: overall conclusion, jurisprudential analysis, and resolution suggestions. More materials of the system can be accessed at https://github.com/RujingYao/Intelligent-Legal-Assistant.