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An Explorative Study of Pig Butchering Scams

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arxiv 2412.15423 v1 pith:A6U52SPC submitted 2024-12-19 cs.CR

classification cs.CR
keywords scamsvictimssocialmediapig-butcheringplatformsabusemultiple
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

In the recent past, so-called pig-butchering scams are on the rise. This term is based on a translation of the Chinese term "Sha Zhu Pan", where scammers refer to victims as "pig" which are to be "fattened up before slaughter" so that scammer can siphon off as much monetary value as possible. In this type of scam, attackers perform social engineering tricks on victims over an extended period to build credibility or relationships. After a certain period, when victims transfer larger amounts of money to scammers, the fraudsters' platforms or profiles go permanently offline and the victims' money is lost. In this work, we provide the first comprehensive study of pig-butchering scams from multiple vantage points. Our study analyzes the direct victims' narratives shared on multiple social media platforms, public abuse report databases, and case studies from news outlets. Between March 2024 to October 2024, we collected data related to pig butchering scams from (i) four social media platforms comprised of more than 430,000 social media accounts and 770,000 posts; (ii) more than 3,200 public abuse reports narratives, and (iii) about 1,000 news articles. Through automated and qualitative evaluation, we provide an evaluation of victims of pig-butchering scams, finding 146 social media scammed users, 2,570 abuse reports narratives, and 50 case studies of 834 souls from news outlets. In total, we approximated losses of over \$521 million related to such scams. To complement this analysis, we performed a survey on crowdsourcing platforms with 584 users to broaden the insights on comparative analysis of pig-butchering scams with other types of scams. Our research highlights that these attacks are sophisticated and often require multiple entities, including policymakers and law enforcement, to work together alongside user education to create a proactive detection of such scams.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. An Explainable Agentic System for Detection of Conversational Scams with Summary-Based Memory

    cs.MA 2026-07 conditional novelty 6.0 of 10

    An LLM multi-agent system with summary-based memory detects slow-building conversational scams, achieving 100% recall on LoveFraud02 and 97.8% accuracy on the new ConScamBench-278 benchmark.

  2. PiMRef: Detecting and Explaining Ever-evolving Spear Phishing Emails with Knowledge Base Invariants

    cs.CR 2025-07 conditional novelty 6.0 of 10

    PiMRef flags spear phishing by verifying that an email's claimed sender identity matches its actual domain in a knowledge base, and that it contains a call to action.

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