"Hello, is this Anna?": A First Look at Pig-Butchering Scams
- URL: http://arxiv.org/abs/2503.20821v1
- Date: Tue, 25 Mar 2025 23:15:48 GMT
- Title: "Hello, is this Anna?": A First Look at Pig-Butchering Scams
- Authors: Rajvardhan Oak, Zubair Shafiq,
- Abstract summary: Pig-butchering scams, or Sha Zhu Pan, have emerged as a complex form of cyber-enabled financial fraud.<n>We present the first qualitative analysis of pig-butchering scams, informed by in-depth semi-structured interviews with N=26 victims.
- Score: 22.349368438615304
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Pig-butchering scams, or Sha Zhu Pan, have emerged as a complex form of cyber-enabled financial fraud that combines elements of romance, investment fraud, and advanced social engineering tactics to systematically exploit victims. In this paper, we present the first qualitative analysis of pig-butchering scams, informed by in-depth semi-structured interviews with N=26 victims. We capture nuanced, first-hand accounts from victims across multiple regions, providing insight into the lifecycle of pig-butchering scams and the complex emotional and financial manipulation involved. We systematically analyze each phase of the scam, revealing that perpetrators employ tactics such as staged trust-building, fraudulent financial platforms, fabricated investment returns, and repeated high-pressure tactics, all designed to exploit victims' trust and financial resources over extended periods. Our findings reveal an organized scam lifecycle characterized by emotional manipulation, staged financial exploitation, and persistent re-engagement efforts that amplify victim losses. We also find complex psychological and financial impacts on victims, including heightened vulnerability to secondary scams. Finally, we propose actionable intervention points for social media and financial platforms to curb the prevalence of these scams and highlight the need for non-stigmatizing terminology to encourage victims to report and seek assistance.
Related papers
- "It Warned Me Just at the Right Moment": Exploring LLM-based Real-time Detection of Phone Scams [21.992539308179126]
We propose a framework for modeling scam calls and introduce an LLM-based real-time detection approach.<n>We evaluate the method's performance and analyze key factors influencing its effectiveness.
arXiv Detail & Related papers (2025-02-06T10:57:05Z) - Illusions of Relevance: Using Content Injection Attacks to Deceive Retrievers, Rerankers, and LLM Judges [52.96987928118327]
We find that embedding models for retrieval, rerankers, and large language model (LLM) relevance judges are vulnerable to content injection attacks.
We identify two primary threats: (1) inserting unrelated or harmful content within passages that still appear deceptively "relevant", and (2) inserting entire queries or key query terms into passages to boost their perceived relevance.
Our study systematically examines the factors that influence an attack's success, such as the placement of injected content and the balance between relevant and non-relevant material.
arXiv Detail & Related papers (2025-01-30T18:02:15Z) - An Explorative Study of Pig Butchering Scams [18.980991664884556]
We provide the first comprehensive study of pig-butchering scams from multiple vantage points.<n>Our study analyzes the direct victims' narratives shared on multiple social media platforms, public abuse report databases, and case studies from news outlets.<n>In total, we approximated losses of over $521 million related to such scams.
arXiv Detail & Related papers (2024-12-19T22:15:50Z) - FaceTracer: Unveiling Source Identities from Swapped Face Images and Videos for Fraud Prevention [68.07489215110894]
FaceTracer is a framework specifically designed to trace the identity of the source person from swapped face images or videos.
In experiments, FaceTracer successfully identified the source person in swapped content and enabling the tracing of malicious actors involved in fraudulent activities.
arXiv Detail & Related papers (2024-12-11T04:00:17Z) - Combating Phone Scams with LLM-based Detection: Where Do We Stand? [1.8979188847659796]
This research explores the potential of large language models (LLMs) to provide detection of fraudulent phone calls.
LLMs-based detectors can identify potential scams as they occur, offering immediate protection to users.
arXiv Detail & Related papers (2024-09-18T02:14:30Z) - The Anatomy of Deception: Technical and Human Perspectives on a Large-scale Phishing Campaign [4.369550829556578]
This study takes an unprecedented deep dive into large-scale phishing campaigns aimed at Meta's users.
Analysing data from over 25,000 victims worldwide, we highlight the nuances of these campaigns.
Through the application of advanced computational techniques, including natural language processing and machine learning, this work unveils critical insights into the psyche of victims.
arXiv Detail & Related papers (2023-10-05T12:24:24Z) - Designing an attack-defense game: how to increase robustness of
financial transaction models via a competition [69.08339915577206]
Given the escalating risks of malicious attacks in the finance sector, understanding adversarial strategies and robust defense mechanisms for machine learning models is critical.
We aim to investigate the current state and dynamics of adversarial attacks and defenses for neural network models that use sequential financial data as the input.
We have designed a competition that allows realistic and detailed investigation of problems in modern financial transaction data.
The participants compete directly against each other, so possible attacks and defenses are examined in close-to-real-life conditions.
arXiv Detail & Related papers (2023-08-22T12:53:09Z) - Tainted Love: A Systematic Review of Online Romance Fraud [68.8204255655161]
Romance fraud involves cybercriminals engineering a romantic relationship on online dating platforms.
We characterise the literary landscape on romance fraud, advancing the understanding of researchers and practitioners.
Three main contributions were identified: profiles of romance scams, countermeasures for mitigating romance scams, and factors that predispose an individual to become a scammer or a victim.
arXiv Detail & Related papers (2023-02-28T20:34:07Z) - Fighting Money Laundering with Statistics and Machine Learning [95.42181254494287]
There is little scientific literature on statistical and machine learning methods for anti-money laundering.
We propose a unifying terminology with two central elements: (i) client risk profiling and (ii) suspicious behavior flagging.
arXiv Detail & Related papers (2022-01-11T21:31:18Z) - Fragments of the Past: Curating Peer Support with Perpetrators of
Domestic Violence [88.37416552778178]
We report on a ten-month study where we worked with six support workers and eighteen perpetrators in the design and deployment of Fragments of the Past.
We share how crafting digitally-augmented artefacts - 'fragments' - of experiences of desisting from violence can translate messages for motivation and rapport between peers.
These insights provide the basis for practical considerations for future network design with challenging populations.
arXiv Detail & Related papers (2021-07-09T22:57:43Z) - Understanding Underground Incentivized Review Services [26.402818153734035]
We study review fraud on e-commerce platforms through an HCI lens.
We uncover sophisticated recruitment, execution, and reporting mechanisms fraudsters use to scale their operation.
Countermeasures that crack down on communication channels through which these services operate are effective in combating incentivized reviews.
arXiv Detail & Related papers (2021-01-20T05:30:14Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.