Srivathsan G. Morkonda

Srivathsan G Morkonda, Mahmoud Selim, Hala Assal

The majority of software developers use or are planning to use Artificial Intelligence (AI) tools in their development processes. Their top reasons include improving productivity and faster learning. In fact, Large Language Model (LLM)-generated code is currently in production, including in major tech companies. However, concerns were raised about the risks associated with the use of AI tools to generate code. In this paper, we focus our attention on the risks to software security. We empirically evaluate the security of code generated by seven popular LLMs. We build upon previous work to mimic the behaviours of developers when using LLMs to generate code. Our results show that all seven LLMs that we have evaluated generate code that contains vulnerabilities, the majority of which are of critical or high severity.

Srivathsan G. Morkonda, AbdelRahman Abdou

Two-factor authentication (2FA) offers several security benefits that security-conscious users might expect from high-value services such as online banks. In this work, we present our preliminary study to develop a scoring scheme to automatically recognize when bank sites mention support for two-factor authentication. We extract information related to security features (primarily 2FA) offered by 379 bank domains from 93 countries. We use a subset of these sites to refine our scoring scheme to include several heuristics for identifying whether sites offer 2FA. For each bank domain in our dataset, we use our algorithm based on text-analysis to calculate whether the domain offers 2FA to the users of the domain's online banking platform. Our preliminary findings suggest that 2FA is yet to be widely adopted by banking domains.

Srivathsan G. Morkonda, Paul C. van Oorschot, Sonia Chiasson

Single sign-on authentication systems such as OAuth 2.0 are widely used in web services. They allow users to use accounts registered with major identity providers such as Google and Facebook to login on multiple services (relying parties). These services can both identify users and access a subset of the user's data stored with the provider. We empirically investigate the end-user privacy implications of OAuth 2.0 implementations in relying parties most visited around the world. We collect data on the use of OAuth-based logins in the Alexa Top 500 sites per country for five countries. We categorize user data made available by four identity providers (Google, Facebook, Apple and LinkedIn) and evaluate popular services accessing user data from the SSO platforms of these providers. Many services allow users to choose from multiple login options (with different identity providers). Our results reveal that services request different categories and amounts of personal data from different providers, with at least one choice undeniably more privacy-intrusive. These privacy choices (and their privacy implications) are highly invisible to users. Based on our analysis, we also identify areas which could improve user privacy and help users make informed decisions.