CoZure: Context Free Grammar Co-Pilot Tool for Finding New Lateral Movements in Azure Active Directory

Abdullahi Chowdhury, Hung X. Nguyen

Abstract: Securing cloud environments such as Microsoft Azure cloud is challenging and vulnerabilities due to misconfigurations, especially with user roles assignment, are common. There have been significant efforts to find vulnerabilities that enable lateral movements in Azure AD systems. All of the existing works, however, either follow a manual process to find new vulnerabilities or are only able to discover whether known vulnerabilities exist in a deployed Azure environment. We develop an Azure Active Directory (AAD) lateral movement-discovery tool, CoZure, that can help researchers find new lateral movements in an Azure AD environment. CoZure deploys algorithms from Context-Free Grammar (CFG) to first learn the ways (grammar rules) that security researchers find vulnerabilities and then extend these rules to discover new lateral movement paths. CoZure first collects a large set of existing AAD environment commands using a specialized scraping tool, it then uses CFG to build a knowledge base dataset from these commands and previous attacks. Cozure then applies the knowledge learned to find new combinations of commands that could open up new candidate lateral movements, which are then tested in a real AD environment for validation and manually checked by the user. CoZure helped discover lateral movements that current fuzzing tools (e.g., OneFuzz, RESTler) cannot identify and also shows better performance in finding existing misconfiguration issues in Azure AD. Using CoZure, we have discovered two new (not previously known) lateral movement methods that could lead to numerous new attacking paths in Azure AD.