Given a large component-based program, it may be very complex to identify an optimal access-control policy, allowing the program to execute with no authorization failures and no violations of the principle of least privilege. This paper presents a novel combination of static and dynamic analysis for automatic determination of precise access-control policies for programs that will be executed on stack-based access control systems, such as Java and the Common Language Runtime (CLR). The static analysis soundly models the execution of the program taking into account native methods, reflection, and multi-threaded code. The dynamic analysis interactively refines the potentially conservative results of the static analysis, with no need for writing or generating test cases or for restarting the system if an authorization failure occurs during testing, and no risk of corrupting the underlying system on which the analysis is performed. We implemented the analysis framework presented by this paper in an analysis tool for Java programs, called Access-Control Explorer (ACE). ACE allows for automatic, safe, and precise identification of access-right requirements and library-code locations that should be made privilege-asserting to prevent client code from requiring unnecessary access rights. This paper presents experimental results obtained on large production-level applications.

Modern multiuser software systems have adopted role-based access control (RBAC) for authorization management. This paper presents a formal model for RBAC policy validation and a static-analysis model for RBAC systems that can be used to (i) identify the roles required by users to execute an enterprise application, (ii) detect potential inconsistencies caused by principal-delegation policies, which are used to override a user's role assignment, (Hi) report if the roles assigned to a user by a given policy are redundant or insufficient, and (iv) report vulnerabilities that can result from unchecked intra-component accesses. The algorithms described in this paper have been implemented as part of IBM's enterprise security policy evaluator (ESPE) tool. Experimental results show that the tool found numerous policy flaws, including ten previously unknown flaws from two production-level applications, with no false-positive reports.