5 citations · 6 across the 3 of their papers we have counts for
3 papers
cs.CR2025★ 1 cited
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…
cs.AI2025
Deceptive Automated Interpretability: Language Models Coordinating to Fool Oversight Systems
Simon Lermen, Mateusz Dziemian, Natalia Pérez-Campanero Antolín
We demonstrate how AI agents can coordinate to deceive oversight systems using automated interpretability of neural networks. Using sparse autoencoders (SAEs) as our experimental f…
cs.CR2024★ 5 cited
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…