On the Feasibility of Fully AI-automated Vishing Attacks
Not on the map. Why?
Abstract (arXiv)
A vishing attack is a form of social engineering where attackers use phone calls to deceive individuals into disclosing sensitive information, such as personal data, financial information, or security credentials. Attackers exploit the perceived urgency and authenticity of voice communication to manipulate victims, often posing as legitimate entities like banks or tech support. Vishing is a particularly serious threat as it bypasses security controls designed to protect information. In this work, we study the potential for vishing attacks to escalate with the advent of AI. In theory, AI-powered software bots may have the ability to automate these attacks by initiating conversations with potential victims via phone calls and deceiving them into disclosing sensitive information. To validate this thesis, we introduce ViKing, an AI-powered vishing system developed using publicly available AI technology. It relies on a Large Language Model (LLM) as its core cognitive processor to steer conversations with victims, complemented by a pipeline of speech-to-text and text-to-speech modules that facilitate audio-text conversion in phone calls. Through a controlled social experiment involving 240 participants, we discovered that ViKing has successfully persuaded many participants to reveal sensitive information, even those who had been explicitly warned about the risk of vishing campaigns. Interactions with ViKing's bots were generally considered realistic. From these findings, we conclude that tools like ViKing may already be accessible to potential malicious actors, while also serving as an invaluable resource for cyber awareness programs.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 13 of its 74 references that are in the map. First posted on arXiv 20 Sep 2024.
Builds on
13 of 74 references.
References · 13
- 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
- 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
- 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
- 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,339
- 2023LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Modelscited by 317
- 2017Generalized End-to-End Loss for Speaker Verificationcited by 290
- 2021YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyonecited by 277
- 2019AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Losscited by 189
- 2023Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attackscited by 189
- 2023FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learningcited by 146
- 2023From Prompt Injections to SQL Injection Attacks: How Protected is Your LLM-Integrated Web Application?cited by 53
- 2023An Improved Transformer-based Model for Detecting Phishing, Spam, and Ham: A Large Language Model Approachcited by 12
1 more
Built on by
Citing papers per year: 2025: 3.
3 papers.