5 citations · 8 across the 5 of their papers we have counts for
6 papers
Using Codebooks to Detect Cybercrime Topics in Text Narratives
Shufan Chai, Liangliang Sun, Jessica Staddon
In the United States, management of cybercrime-related consumer complaints increasingly falls on state and city governments given de-staffing of federal agencies. AI, and in partic…
Semi-Automated Detection of Gaps in LLM Security Knowledge
Shufan Chai, Liangliang Sun, Jessica Staddon
Large language models (LLMs) are increasingly used for a range of software, hardware and human-centered security tasks. Consequently, LLM performance on security tasks is an active…
Distinguishing Scams and Fraud with Ensemble Learning
Isha Chadalavada, Tianhui Huang, Jessica Staddon
Users increasingly query LLM-enabled web chatbots for help with scam defense. The Consumer Financial Protection Bureau's complaints database is a rich data source for evaluating LL…
Learned, Lagged, LLM-splained: LLM Responses to End User Security Questions
Vijay Prakash, Kevin Lee, Arkaprabha Bhattacharya +2
Answering end user security questions is challenging. While large language models (LLMs) like GPT, LLAMA, and Gemini are far from error-free, they have shown promise in answering a…
Can LLMs be Scammed? A Baseline Measurement Study
Udari Madhushani Sehwag, Kelly Patel, Francesca Mosca +2
Despite the importance of developing generative AI models that can effectively resist scams, current literature lacks a structured framework for evaluating their vulnerability to s…
Shortchanged: Uncovering and Analyzing Intimate Partner Financial Abuse in Consumer Complaints
Arkaprabha Bhattacharya, Kevin Lee, Vineeth Ravi +2
Digital financial services can introduce new digital-safety risks for users, particularly survivors of intimate partner financial abuse (IPFA). To offer improved support for such u…