The paper introduces LLM4Trust, a benchmark framework that evaluates large language models (LLMs) for trust assessment in cybersecurity. It constructs trust graphs to represent five fundamental trust properties, designs property‑understanding tasks, and tests eight LLMs across nine prompting strategies. The study identifies effective LLM‑prompt combinations, applies them to real datasets, proposes methods to handle context limits, and demonstrates that LLMs can understand trust properties while highlighting vulnerabilities and high inference costs, for which defense mechanisms and batch inference are suggested.
By Jie Wang, Yanbo Sun, Zheng Yan, Jiahe Lan, Elisa Bertino
arXiv:2607. 05952v1 Announce Type: cross Abstract: While signed social recommendation has shown great potential by modeling both trust and distrust relations, its effectiveness is often hindered by structural noise and data sparsity.
By Zifan Wang, Siyu Chen, Wenzhuo Song
Evi-VN is a new framework for graph neural network (GNN) fraud detection that injects evidence from diverse modalities—structured records, text, images, and audio—into virtual node representations. It targets shared hard regions where standard GNNs fail to distinguish well‑disguised fraudsters from legitimate users, correcting blind spots without disrupting reliable predictions. Experiments on bot, fake‑review, refund‑evidence, and telecom‑fraud datasets demonstrate its effectiveness across generic, fraud‑specific, and unseen GNNs.
By Jiran Tao, Yifan Wu, Binyan Jiang
The article surveys fake review detection research, focusing on how pre‑trained language models (PLMs) and large language models (LLMs) influence both the generation of deceptive reviews and their detection. It reviews 211 studies from 2018 to early 2026, categorizing methods by evidence source—such as review text, sentiment, rating behavior, temporal metadata, user‑product graphs, multimodal content, external knowledge, and LLM‑generated signals—and by fusion level. The survey traces the evolution from traditional machine learning to PLM‑based and LLM‑based approaches, evaluates performance on Amazon, Yelp, and OpSpam benchmarks, and highlights open challenges including adversarial generation, cross‑domain transfer, uncertainty‑aware fusion, robustness to missing sources, interpretability, and trustworthy evaluation of AI‑generated deceptive content.
By Fanji Yang (Guizhou University of Finance and Economics), Huiyao Chen (Harbin Institute of Technology), Xi Yu (Guizhou University of Finance and Economics), Meishan Zhang (Harbin Institute of Technology), Xiaohong Xiao (Guizhou University of Commerce), Mingsen Deng (Guizhou University of Finance and Economics)
TH-GNN is a heterogeneous temporal graph neural network designed to detect shilling attacks generated by large language model (LLM) agents. It combines a two‑layer Heterogeneous Graph Transformer with per‑type and per‑relation attention, learnable sinusoidal temporal encodings, cross‑modal attention that fuses user embeddings with frozen RoBERTa representations of reviews and item descriptions, and a GRU that models log inter‑arrival times. Across five attack families and four benchmark datasets, TH‑GNN achieves a grand‑mean F1 score of 0.870, surpassing the best text‑only baseline on Agent4SR attacks by 10.9 percentage points and 11.5 percentage points at the lowest injection rate.
By Shivam Swarup, Divya Prakash Shrivastava, Rakesh Thakur
The paper introduces Graph‑Agentic Retrieval‑Augmented Generation (RAG), a system that blends structured evidence with adaptive agents capable of planning retrieval, navigating relations, verifying claims, delegating tasks, and employing tools. It highlights how defects in graph construction can propagate through retrieval and control decisions, potentially leading to significant outcomes. To address these risks, the authors propose an assurance‑by‑construction framework with five interface contracts—evidence, retrieval, reasoning, capability & delegation, and outcome—that make provenance, validity, authorization, uncertainty, and recoverability explicit, and outline an evaluation agenda for social‑good applications.
By Vijay Bommireddy, Raviteja Bommireddy