Anuj Gautam

Ran Elgedawy, Porter Dosch, John Sadik et al.

$ $Large Language Models (LLMs) are being increasingly utilized in various applications, with code generations being a notable example. While previous research has shown that LLMs have the capability to generate both secure and insecure code, the literature does not take into account what factors help generate secure and effective code. Therefore in this paper we focus on identifying and understanding the conditions and contexts in which LLMs can be effectively and safely deployed in real-world scenarios to generate quality code. We conducted a comparative analysis of four advanced LLMs--GPT-3.5 and GPT-4 using ChatGPT and Bard and Gemini from Google--using 9 separate tasks to assess each model's code generation capabilities. We contextualized our study to represent the typical use cases of a real-life developer employing LLMs for everyday tasks as work. Additionally, we place an emphasis on security awareness which is represented through the use of two distinct versions of our developer persona. In total, we collected 61 code outputs and analyzed them across several aspects: functionality, security, performance, complexity, and reliability. These insights are crucial for understanding the models' capabilities and limitations, guiding future development and practical applications in the field of automated code generation.

Sean Oesch, Anuj Gautam, Scott Ruoti

Password managers help users more effectively manage their passwords, encouraging them to adopt stronger passwords across their many accounts. In contrast to desktop systems where password managers receive no system-level support, mobile operating systems provide autofill frameworks designed to integrate with password managers to provide secure and usable autofill for browsers and other apps installed on mobile devices. In this paper, we evaluate mobile autofill frameworks on iOS and Android, examining whether they achieve substantive benefits over the ad-hoc desktop environment or become a problematic single point of failure. Our results find that while the frameworks address several common issues, they also enforce insecure behavior and fail to provide password managers sufficient information to override the frameworks' insecure behavior, resulting in mobile managers being less secure than their desktop counterparts overall. We also demonstrate how these frameworks act as a confused deputy in manager-assisted credential phishing attacks. Our results demonstrate the need for significant improvements to mobile autofill frameworks. We conclude the paper with recommendations for the design and implementation of secure autofill frameworks.