Amazon Technologies, Inc.

Diffblue Ltd

Daniel Kroening

Neha Rungta

John Byron Cook

Carsten Varming

Daniel George Peebles

Alejandro Naser Pastoriza

Peter Schrammel

Methods, systems, and computer-readable media for analysis of role reachability with transitive tags are disclosed. An access control analyzer determines a graph including nodes and edges. The nodes represent roles in a provider network hosting resources. The roles are associated with access control policies granting or denying access to individual resources. One or more of the access control policies grant or deny access based (at least in part) on key-value attributes. The access control analyzer determines, based (at least in part) on a role reachability analysis of the graph, whether a first role can assume a second role using role assumption steps for a particular state of the attributes. The attributes may include transitive attributes that persist during the role assumption steps.

Analysis of role reachability with transitive tags

Methods, systems, and computer-readable media for analysis of role reachability using policy complements are disclosed. An access control analyzer determines two nodes in a graph that potentially have a common edge. The nodes correspond to roles in a provider network, and the roles are associated with first and second access control policies that grant or deny access to resources. The access control analyzer performs a role reachability analysis that determines whether the first role can assume the second role for a particular state of one or more key-value tags. The role reachability analysis determines a third access control policy authorizing a negation of a role assumption request for the second role. The role reachability analysis performs analysis of the third access control policy with respect to a role assumption policy for the second role for the particular state of the one or more key-value tags.

Analysis of role reachability using policy complements

A computer-implemented method comprising obtaining a first candidate test associated with a testable component, wherein the first candidate test comprises an input having an input value; generating a second candidate test associated with the testable component by performing a dynamic mutation-based fuzzing of the first candidate test, wherein the second candidate test is based on the first candidate test and comprises a modified input value for the input based on data generated during an execution of the first candidate test or domain knowledge associated with the testable component; and creating a test for the testable component based on the first candidate test or the second candidate test.

Systems and methods for automatic test generation

Growing community of inventors

Oxford, United Kingdom

Daniel Kroening