# Love, Lies, and Language Models: Investigating AI's Role in Romance-Baiting Scams

arXiv: https://arxiv.org/abs/2512.16280 · HTML: https://mlsanity.com/paper/2512.16280/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 58 references that are in the map.
- First posted on arXiv 18 Dec 2025.

## Builds on (most cited first, 15 of 15)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [SoulChat: Improving LLMs' Empathy, Listening, and Comfort Abilities through Fine-tuning with Multi-turn Empathy Conversations](https://mlsanity.com/paper/2311.00273/) (2023)
- [Teams of LLM Agents can Exploit Zero-Day Vulnerabilities](https://mlsanity.com/paper/2406.01637/) (2024)
- [People cannot distinguish GPT-4 from a human in a Turing test](https://mlsanity.com/paper/2405.08007/) (2024)
- [Large Language Models Pass the Turing Test](https://mlsanity.com/paper/2503.23674/) (2025)
- [Does GPT-4 pass the Turing test?](https://mlsanity.com/paper/2310.20216/) (2023)
- [Bot or Human? Detecting ChatGPT Imposters with A Single Question](https://mlsanity.com/paper/2305.06424/) (2023)
- [Can AI Relate: Testing Large Language Model Response for Mental Health Support](https://mlsanity.com/paper/2405.12021/) (2024)
- [Human Decision-making is Susceptible to AI-driven Manipulation](https://mlsanity.com/paper/2502.07663/) (2025)
- [Challenges of Large Language Models for Mental Health Counseling](https://mlsanity.com/paper/2311.13857/) (2023)
- [Are You Human? An Adversarial Benchmark to Expose LLMs](https://mlsanity.com/paper/2410.09569/) (2024)
- [An Explorative Study of Pig Butchering Scams](https://mlsanity.com/paper/2412.15423/) (2024)
- [A Survey of Scam Exposure, Victimization, Types, Vectors, and Reporting in 12 Countries](https://mlsanity.com/paper/2407.12896/) (2024)

## Built on by (most cited first, 3 of 3)

- [Agentic Relationship Harm: Benchmarking and Gating Relational Manipulation in AI Agents](https://mlsanity.com/paper/2606.03271/) (2026)
- [FinInvest-GTCN: Explainable Graph-Temporal-Causal Modeling for Risk-Aware Investment Decision Optimization](https://mlsanity.com/paper/2606.28933/) (2026)
- [An Explainable Agentic System for Detection of Conversational Scams with Summary-Based Memory](https://mlsanity.com/paper/2607.11707/) (2026)

## Abstract (arXiv)

Romance-baiting scams have become a major source of financial and emotional harm worldwide. These operations are run by organized crime syndicates that traffic thousands of people into forced labor, requiring them to build emotional intimacy with victims over weeks of text conversations before pressuring them into fraudulent cryptocurrency investments. 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. We investigate this intersection by interviewing 145 insiders and 5 scam victims, performing a blinded long-term conversation study comparing LLM scam agents to human operators, and executing an evaluation of commercial safety filters. Our findings show that LLMs are already widely deployed within scam organizations, with 87% of scam labor consisting of systematized conversational tasks readily susceptible to automation. In a week-long study, an LLM agent not only elicited greater trust from study participants (p=0.007) but also achieved higher compliance with requests than human operators (46% vs. 18% for humans). Meanwhile, popular safety filters detected 0.0% of romance baiting dialogues. Together, these results suggest that romance-baiting scams may be amenable to full-scale LLM automation, while existing defenses remain inadequate to prevent their expansion.