Abstract Information-flow control mechanisms are difficult both to design and to prove correct. To reduce the time wasted on doomed proof attempts due to broken definitions, we advocate modern random-testing techniques for finding counterexamples during the design process. We show how to use QuickCheck, a property-based random-testing tool, to guide the design of increasingly complex information-flow abstract machines, leading up to a sophisticated register machine with a novel and highly permissive flow-sensitive dynamic enforcement mechanism that is sound in the presence of first-class public labels. We find that both sophisticated strategies for generating well-distributed random programs and readily falsifiable formulations of noninterference properties are critically important for efficient testing. We propose several approaches and evaluate their effectiveness on a collection of injected bugs of varying subtlety. We also present an effective technique for shrinking large counterexamples to minimal, easily comprehensible ones. Taken together, our best methods enable us to quickly and automatically generate simple counterexamples for more than 45 bugs. Moreover, we show how testing guides the discovery of the sophisticated invariants needed for the noninterference proof of our most complex machine.

Recent advances in hardware design have demonstrated mechanisms allowing a wide range of low-level security policies (or micro-policies) to be expressed using rules on metadata tags. We propose a methodology for defining and reasoning about such tag-based reference monitors in terms of a high-level "symbolic machine" and we use this methodology to define and formally verify micro-policies for dynamic sealing, compartmentalization, control-flow integrity, and memory safety, in addition, we show how to use the tagging mechanism to protect its own integrity. For each micro-policy, we prove by refinement that the symbolic machine instantiated with the policy's rules embodies a high-level specification characterizing a useful security property. Last, we show how the symbolic machine itself can be implemented in terms of a hardware rule cache and a software controller.