14 citations · 23 across the 6 of their papers we have counts for
6 papers
Evaluating AI Models' Capability to Automate Voice Phishing Attacks
Fred Heiding, Claudio Mayrink Verdun, Simon Lermen +5
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 (…
Manipulation Is Task-Dependent: A Multi-Axis, Multi-Environment Evaluation of Frontier LLMs
Adeeb Zaman, Erik Nordby, Fred Heiding
We evaluate manipulative behavior in six frontier language models across six environments, ranging from negotiation tasks to agentic workflows, resulting in 13{,}590 individual sce…
Toward Quantitative Modeling of Cybersecurity Risks Due to AI Misuse
Steve Barrett, Malcolm Murray, Otter Quarks +17
Advanced AI systems offer substantial benefits but also introduce risks. In 2025, AI-enabled cyber offense has emerged as a concrete example. This technical report applies a quanti…
Can AI Models be Jailbroken to Phish Elderly Victims? An End-to-End Evaluation
Fred Heiding, Simon Lermen
We present an end-to-end demonstration of how attackers can exploit AI safety failures to harm vulnerable populations: from jailbreaking LLMs to generate phishing content, to deplo…
Evaluating Large Language Models' Capability to Launch Fully Automated Spear Phishing Campaigns: Validated on Human Subjects
Fred Heiding, Simon Lermen, Andrew Kao +2
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…
Devising and Detecting Phishing: Large Language Models vs. Smaller Human Models
Fredrik Heiding, Bruce Schneier, Arun Vishwanath +2
AI programs, built using large language models, make it possible to automatically create phishing emails based on a few data points about a user. They stand in contrast to traditio…