arXiv subjects

Andrew Kao

Publications and source records attributed to Andrew Kao.

2 recordsLinked to original sources

arXiv2026

Evaluating AI Models' Capability to Automate Voice Phishing Attacks

Voice phishing (vishing) attacks have traditionally been limited by the need for human operators. The rapid emergence of high-quality AI voice synthesis and large language models (LLMs) reduces this bottleneck and enables scalable, automated scams. In this paper, we conduct a large-scale survey experiment (N=4100) and qualitative interviews (N=12) to assess U.S. adults' susceptibility to AI-powered voice phishing attacks. Participants were exposed to audio recordings or transcripts of scam scenarios generated using leading voice models such as Llama Full Duplex (Llama FD), Sesame, Gemini, OAI AVM, Play$.$AI, and ElevenLabs and the corresponding human baselines. The results show high compliance rates. Up to 36% of participants would or might comply with phishing requests in the "relative-in-distress" category. Overall compliance rate across all five scam categories was 16.5%, a striking figure given the low cost and high scalability of AI-automated voice phishing. Caller persuasiveness was the strongest predictor of compliance and certain models (most notably Sesame) achieved ratings comparable to human voices, or sometimes even slightly surpassing them. Our economic analysis suggests that while human-operated vishing is unprofitable at US wages, AI-powered vishing appears to be economically viable for several models. The primary risk of present-day AI-enabled vishing thus lies in the economics of automation rather than novel or "superhuman" persuasive techniques, though these cannot be ruled out for future systems. This raises significant concerns for the design of AI systems, consumer protection, and model release policies.

cs.CR↗

arXiv2024

Evaluating Large Language Models' Capability to Launch Fully Automated Spear Phishing Campaigns: Validated on Human Subjects

In this paper, we evaluate the capability of large language models to conduct personalized phishing attacks and compare their performance with human experts and AI models from last year. We include four email groups with a combined total of 101 participants: A control group of arbitrary phishing emails, which received a click-through rate (recipient pressed a link in the email) of 12%, emails generated by human experts (54% click-through), fully AI-automated emails 54% (click-through), and AI emails utilizing a human-in-the-loop (56% click-through). Thus, the AI-automated attacks performed on par with human experts and 350% better than the control group. The results are a significant improvement from similar studies conducted last year, highlighting the increased deceptive capabilities of AI models. Our AI-automated emails were sent using a custom-built tool that automates the entire spear phishing process, including information gathering and creating personalized vulnerability profiles for each target. The AI-gathered information was accurate and useful in 88% of cases and only produced inaccurate profiles for 4% of the participants. We also use language models to detect the intention of emails. Claude 3.5 Sonnet scored well above 90% with low false-positive rates and detected several seemingly benign emails that passed human detection. Lastly, we analyze the economics of phishing, highlighting how AI enables attackers to target more individuals at lower cost and increase profitability by up to 50 times for larger audiences.

cs.CR↗