Victim as a Service: Designing a System for Engaging with Interactive Scammers

• URL: http://arxiv.org/abs/2510.23927v1
• Date: Mon, 27 Oct 2025 23:19:29 GMT
• Title: Victim as a Service: Designing a System for Engaging with Interactive Scammers
• Authors: Daniel Spokoyny, Nikolai Vogler, Xin Gao, Tianyi Zheng, Yufei Weng, Jonghyun Park, Jiajun Jiao, Geoffrey M. Voelker, Stefan Savage, Taylor Berg-Kirkpatrick,
• Abstract summary: We describe the motivation, design, implementation, and experience with CHATTERBOX, an LLM-based system that automates long-term engagement with online scammers.<n>We describe the techniques we have developed to attract scam attempts, the system and LLM-engineering required to convincingly engage with scammers, and the necessary capabilities required to satisfy or evade "milestones" in scammers' workflow.
• Score: 29.43320237202651
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pig butchering, and similar interactive online scams, lower their victims' defenses by building trust over extended periods of conversation - sometimes weeks or months. They have become increasingly public losses (at least $75B by one recent study). However, because of their long-term conversational nature, they are extremely challenging to investigate at scale. In this paper, we describe the motivation, design, implementation, and experience with CHATTERBOX, an LLM-based system that automates long-term engagement with online scammers, making large-scale investigations of their tactics possible. We describe the techniques we have developed to attract scam attempts, the system and LLM-engineering required to convincingly engage with scammers, and the necessary capabilities required to satisfy or evade "milestones" in scammers' workflow.

Related papers

• Love, Lies, and Language Models: Investigating AI's Role in Romance-Baiting Scams [4.75107240674109]
  Romance-baiting scams are run by organized crime syndicates that traffic thousands of people into forced labor.<n>Because the scams are inherently text-based, they raise urgent questions about the role of Large Language Models (LLMs) in both current and future automation.
  arXiv  Detail & Related papers  (2025-12-18T07:59:15Z)
• When AI Agents Collude Online: Financial Fraud Risks by Collaborative LLM Agents on Social Platforms [101.2197679948061]
  We study the risks of collective financial fraud in large-scale multi-agent systems powered by large language model (LLM) agents.<n>We present MultiAgentFraudBench, a large-scale benchmark for simulating financial fraud scenarios.
  arXiv  Detail & Related papers  (2025-11-09T16:30:44Z)
• "It Felt Real" Victim Perspectives on Platform Design and Longer-Running Scams [11.449657621942885]
  We show how scammers strategically use platform affordances to stage credibility, orchestrate intimacy, and sustain coercion with victims.<n>By analyzing scams as socio-technical projects, we highlight how platform design can be exploited in longer-running scams.
  arXiv  Detail & Related papers  (2025-10-03T02:34:13Z)
• Send to which account? Evaluation of an LLM-based Scambaiting System [0.0]
  This paper presents the first large-scale, real-world evaluation of a scambaiting system powered by large language models (LLMs)<n>Over a five-month deployment, the system initiated over 2,600 engagements with actual scammers, resulting in a dataset of more than 18,700 messages.<n>It achieved an Information Disclosure Rate (IDR) of approximately 32%, successfully extracting sensitive financial information such as mule accounts.
  arXiv  Detail & Related papers  (2025-09-10T11:08:52Z)
• PsyScam: A Benchmark for Psychological Techniques in Real-World Scams [38.57446009573742]
  PsyScam is a benchmark designed to systematically capture the psychological techniques employed in real-world scam reports.<n>We show that PsyScam presents significant challenges to existing models in both detecting and generating scam content based on the PTs used by real-world scammers.
  arXiv  Detail & Related papers  (2025-05-21T01:55:04Z)
• Interactive Dialogue Agents via Reinforcement Learning on Hindsight Regenerations [58.65755268815283]
  Many real dialogues are interactive, meaning an agent's utterances will influence their conversational partner, elicit information, or change their opinion. We use this fact to rewrite and augment existing suboptimal data, and train via offline reinforcement learning (RL) an agent that outperforms both prompting and learning from unaltered human demonstrations. Our results in a user study with real humans show that our approach greatly outperforms existing state-of-the-art dialogue agents.
  arXiv  Detail & Related papers  (2024-11-07T21:37:51Z)
• 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)
• Evaluating Very Long-Term Conversational Memory of LLM Agents [95.84027826745609]
  We introduce a machine-human pipeline to generate high-quality, very long-term dialogues. We equip each agent with the capability of sharing and reacting to images. The generated conversations are verified and edited by human annotators for long-range consistency.
  arXiv  Detail & Related papers  (2024-02-27T18:42:31Z)
• How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs [66.05593434288625]
  This paper introduces a new perspective to jailbreak large language models (LLMs) as human-like communicators. We apply a persuasion taxonomy derived from decades of social science research to generate persuasive adversarial prompts (PAP) to jailbreak LLMs. PAP consistently achieves an attack success rate of over $92%$ on Llama 2-7b Chat, GPT-3.5, and GPT-4 in $10$ trials. On the defense side, we explore various mechanisms against PAP and, found a significant gap in existing defenses.
  arXiv  Detail & Related papers  (2024-01-12T16:13:24Z)
• LLM-Based Agent Society Investigation: Collaboration and Confrontation in Avalon Gameplay [55.12945794835791]
  Using Avalon as a testbed, we employ system prompts to guide LLM agents in gameplay. We propose a novel framework, tailored for Avalon, features a multi-agent system facilitating efficient communication and interaction. Results affirm the framework's effectiveness in creating adaptive agents and suggest LLM-based agents' potential in navigating dynamic social interactions.
  arXiv  Detail & Related papers  (2023-10-23T14:35:26Z)
• Automatic Scam-Baiting Using ChatGPT [0.46040036610482665]
  We report on the results of a month-long experiment comparing the effectiveness of two ChatGPT-based automatic scam-baiters to a control measure. With engagement from over 250 real email fraudsters, we find that ChatGPT-based scam-baiters show a marked increase in scammer response rate and conversation length. We discuss the implications of these results and practical considerations for wider deployment of automatic scam-baiting.
  arXiv  Detail & Related papers  (2023-09-04T13:13:35Z)

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.