ScamAgents: How AI Agents Can Simulate Human-Level Scam Calls

TL;DR

This paper introduces ScamAgent, an AI-powered multi-turn conversational agent capable of generating realistic scam calls, exposing vulnerabilities in current safety measures, and emphasizing the need for advanced detection and control strategies.

Contribution

We develop ScamAgent, a novel multi-turn LLM-based system that creates convincing scam call scripts and demonstrates the limitations of existing safety guardrails against such threats.

Findings

Current safety guardrails are ineffective against multi-turn AI scam agents.

ScamAgent can generate lifelike scam scripts that bypass safeguards.

A complete automated scam pipeline from script to voice is demonstrated.

Abstract

Large Language Models (LLMs) have demonstrated impressive fluency and reasoning capabilities, but their potential for misuse has raised growing concern. In this paper, we present ScamAgent, an autonomous multi-turn agent built on top of LLMs, capable of generating highly realistic scam call scripts that simulate real-world fraud scenarios. Unlike prior work focused on single-shot prompt misuse, ScamAgent maintains dialogue memory, adapts dynamically to simulated user responses, and employs deceptive persuasion strategies across conversational turns. We show that current LLM safety guardrails, including refusal mechanisms and content filters, are ineffective against such agent-based threats. Even models with strong prompt-level safeguards can be bypassed when prompts are decomposed, disguised, or delivered incrementally within an agent framework. We further demonstrate the transformation of scam scripts into lifelike voice calls using modern text-to-speech systems, completing a fully automated scam pipeline. Our findings highlight an urgent need for multi-turn safety auditing, agent-level control frameworks, and new methods to detect and disrupt conversational deception powered by generative AI.