Job-based smishing scams, where victims are recruited under the guise of remote job opportunities, represent a rapidly growing and understudied threat within the broader landscape of online fraud. In this paper, we present Anansi, the first scalable, end-to-end measurement pipeline designed to systematically engage with, analyze, and characterize job scams in the wild. Anansi combines large language models (LLMs), automated browser agents, and infrastructure fingerprinting tools to collect over 29,000 scam messages, interact with more than 1900 scammers, and extract behavioral, financial, and infrastructural signals at scale. We detail the operational workflows of scammers, uncover extensive reuse of message templates, domains, and cryptocurrency wallets, and identify the social engineering tactics used to defraud victims. Our analysis reveals millions of dollars in cryptocurrency losses, highlighting the use of deceptive techniques such as domain fronting and impersonation of well-known brands. Anansi demonstrates the feasibility and value of automating the engagement with scammers and the analysis of infrastructure, offering a new methodological foundation for studying large-scale fraud ecosystems.

翻译：基于招聘的短信钓鱼诈骗，即受害者以远程工作机会为幌子被招募，是更广泛的在线欺诈领域中一个快速增长且研究不足的威胁。本文提出了Anansi，这是首个可扩展的端到端测量管道，旨在系统地参与、分析并刻画现实世界中的招聘诈骗。Anansi结合了大型语言模型（LLMs）、自动化浏览器代理和基础设施指纹识别工具，收集了超过29,000条诈骗消息，与超过1900名诈骗者进行了交互，并大规模提取了行为、财务和基础设施信号。我们详细描述了诈骗者的操作流程，揭示了消息模板、域名和加密货币钱包的广泛重复使用，并识别了用于欺诈受害者的社会工程学策略。我们的分析揭示了数百万美元的加密货币损失，凸显了域名伪装和冒充知名品牌等欺骗性技术的使用。Anansi证明了自动化与诈骗者交互及基础设施分析的可行性和价值，为研究大规模欺诈生态系统提供了新的方法论基础。