The paper introduces ORACLE, an online reasoning framework designed to anticipate smartphone scams early by analyzing partial, streaming app‑usage trajectories. It presents a real‑world benchmark of 12 scam types across 95 apps and 15‑day periods, and employs a self‑evolving context manager plus an on‑policy self‑distillation scheme to consolidate fragmented evidence and enhance early detection. Experiments demonstrate that ORACLE consistently improves early scam anticipation, providing timely warnings while reducing false alerts in realistic streaming scenarios.
arXiv:2510. 08948v4 Announce Type: replace-cross Abstract: Effective e-commerce risk management requires in-depth case investigations to identify emerging fraud patterns in highly adversarial environments.
The paper introduces VirusCascade, a black‑box attack that exploits the collaborative reflection mechanism in LLM‑powered recommender agents. By leveraging reflective persistence and cross‑agent propagation, the attack promotes a target item through semantic rationalisation and structural positioning, achieving state‑of‑the‑art targeted exposure on four real‑world datasets. The study demonstrates that existing static‑pipeline attacks cannot exploit this recurrent, multi‑agent amplification pathway.
arXiv:2606. 26277v1 Announce Type: cross Abstract: Sequential user behavior modeling is widely adopted in industrial recommender systems; however, significant gaps remain in financial services, where pre-login web interactions and authenticated in-app experiences differ drastically.
The paper introduces MechScam, a benchmark of 6,614 videos that covers diverse scam mechanisms and non‑scam content, providing evidence‑supported explanations and annotations of scam elements. It also proposes ScamCourt, a debate framework where prosecution and defense debaters argue over verified citations, and a judge decides based on the transcript. Experiments show that current large multimodal language models struggle to differentiate scam videos from similar non‑scam content, while ScamCourt reduces false positives and better resists misleading arguments.
The paper presents a cumulative turn‑based risk assessment framework for detecting financial scams targeting older adults, which aggregates conversational turns and updates risk estimates at each step. A multi‑turn dialogue dataset covering investment, charity, and tech support scams is created, with annotations for risk level, score, rationale, and safety recommendation at every cumulative stage. Four small language models (Phi‑4, LLaMA‑3.2, DeepSeek‑R1, Qwen3) are fine‑tuned; Phi‑4 and LLaMA‑3.2 outperform others in turn‑aware risk estimation, demonstrating that compact models can effectively support incremental scam detection in resource‑constrained, privacy‑aware deployments.
ReDence is an open‑source hybrid framework that combines a multimodal agentic Investigator model with a specialized Decider model to detect modern social engineering scams. It automates investigative planning, evidence interpretation, and decision‑making across multiple scam categories, achieving 96% accuracy and operating at least four times faster than comparable agentic models. The system outputs structured, evidence‑grounded reports that enable analysts to audit decisions and support mitigation efforts.
arXiv:2606. 02946v1 Announce Type: new Abstract: Live streaming has emerged as a primary medium for social interaction and digital commerce, yet it is increasingly plagued by sophisticated risks.
The paper introduces a dataset of 10,015 real scam and spam phone calls collected over 53 days using an active voice‑agent honeypot. Each call is recorded, transcribed, and automatically labeled, yielding 328,869 turn‑level transcripts and 895 hours of audio from 5,665 distinct numbers. The corpus distinguishes between predatory‑but‑legal lead generation and outright scams, with labels validated by human review and technical checks on realism.
TxnQA is a new analytical QA benchmark that tests reasoning and evidence grounding over transaction event sequences. It contains 27 task families with 81 templates and a 5‑scenario fraud‑risk reasoning suite built on synthetic, privacy‑mitigated transaction data. The benchmark evaluates models using format‑following rate, conditional content accuracy, and end‑to‑end correctness, revealing distinct bottlenecks in schema compliance, semantic calculation, and evidence attribution.
The study analyzes 10,211 real scam and spam calls collected by an AI voice‑agent honeypot, revealing that scammers operate on a templated, office‑hour schedule and use disposable numbers to recycle scripts. Callers predominantly seek identity anchors such as home addresses and dates of birth, and the amount of conversation increases with the target’s age, though the requested information remains unchanged. Early detection is feasible, with escalation predictability reaching 0.87 ROC‑AUC by the eighth line using simple bag‑of‑words models.
SR‑Fraud is a framework that uses a frozen, stateless LLM agent to score transactions in real time while an offline reflection agent proposes boundary hypotheses based on matured errors. The system then verifies these hypotheses deterministically before updating its knowledge state. On a production payment‑fraud benchmark, SR‑Fraud outperforms both static and periodically retrained CatBoost models and successfully detects an emerging fraud burst.
Telephone fraud is pervasive and costly, but its inner workings are rarely observed at scale. We analyze a complete corpus of 10,211 inbound scam and spam calls -- 913 hours of audio and 330,956 trans...