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ReDence: A Hybrid Agentic and Decision-Model Framework for Rapid, Evidence-Grounded Detection of Modern Social Engineering Scams

Jaishnoor Kaur, Josef Horacek, Ian Waskom, Sayak Saha Roy · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.30963 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

检测在线社交工程(SE)骗局的努力往往需要检查网站工作流程,对外部来源进行审计,并在多个页面上连接证据。现有检测器为这些调查提供有限的支持,让分析师手动收集和验证证证据,这不可避免地延迟了这些攻击的检测和后续取消。为了解决这一差距,我们介绍了ReDence,一个开放源代码的证据混合框架,用于适应性、基于证据的检测,在各种SE骗局类别中提供有限的支持。ReDence结合了两个本地托管的模型,并承担了补充责任:一个精心调整的多式代理调查员(Qwen3.5-4B),他执行了调查计划、证据解释和分析,以及一个专门的决策者(

Machine translation (Meta M2M100); the English original follows.

Abstract

Detecting online social engineering (SE) scams often requires examining website workflows, verifying claims against external sources, and connecting evidence across multiple pages. Existing detectors provide limited support for these investigations, leaving analysts to manually gather and validate evidence, which inevitably delays detection and subsequent takedown of these attacks. To address this gap, we present ReDence, an open-source hybrid framework for adaptive, evidence-grounded detection across diverse SE scam categories. ReDence combines two locally hosted models with complementary responsibilities: a fine-tuned multimodal agentic Investigator (Qwen3.5-4B) that performs investigative planning, evidence interpretation, and reporting, and a specialized Decider (Tev1) that handles binary and multiple-choice tasks, including browser-action selection, evidence assessment, and decisions about whether to continue an investigation. By delegating discrete decisions to a specialized model, ReDence reduces reliance on lengthy language-model responses, while preserving the reasoning capabilities required for complex investigations. Our evaluation demonstrates that ReDence achieves 96\% accuracy in detecting scams across twelve SE scam categories, substantially outperforming existing detectors while being at least four times faster than comparable agentic models without compromising investigative depth. ReDence also produces structured, evidence-grounded reports that document investigative actions, supporting and contradictory evidence, and collected artifacts, which allow analysts to independently audit detection decisions and utilize the collected evidence to support subsequent mitigation and takedown efforts.

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Topics: Audit, Computer science, Data science, Social engineering (security), Computer security, Internet privacy

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange