4 citations · 11 across the 6 of their papers we have counts for
6 papers
Mind the Gap: Comparing Model- vs Agentic-Level Red Teaming with Action-Graph Observability on GPT-OSS-20B
Ilham Wicaksono, Zekun Wu, Rahul Patel +3
As the industry increasingly adopts agentic AI systems, understanding their unique vulnerabilities becomes critical. Prior research suggests that security flaws at the model level…
LibVulnWatch: A Deep Assessment Agent System and Leaderboard for Uncovering Hidden Vulnerabilities in Open-Source AI Libraries
Zekun Wu, Seonglae Cho, Umar Mohammed +7
Open-source AI libraries are foundational to modern AI systems, yet they present significant, underexamined risks spanning security, licensing, maintenance, supply chain integrity,…
Eliciting Personality Traits in Large Language Models
Airlie Hilliard, Cristian Munoz, Zekun Wu +1
Large Language Models (LLMs) are increasingly being utilized by both candidates and employers in the recruitment context. However, with this comes numerous ethical concerns, partic…
Intersectional Fairness: A Fractal Approach
Giulio Filippi, Sara Zannone, Adriano Koshiyama
The issue of fairness in AI has received an increasing amount of attention in recent years. The problem can be approached by looking at different protected attributes (e.g., ethnic…
Uncovering Bias in Face Generation Models
Cristian Muñoz, Sara Zannone, Umar Mohammed +1
Recent advancements in GANs and diffusion models have enabled the creation of high-resolution, hyper-realistic images. However, these models may misrepresent certain social groups…
Local Law 144: A Critical Analysis of Regression Metrics
Giulio Filippi, Sara Zannone, Airlie Hilliard +1
The use of automated decision tools in recruitment has received an increasing amount of attention. In November 2021, the New York City Council passed a legislation (Local Law 144)…