🤖 AI Summary
This work addresses the bounded model checking problem for hyperproperties. We present the first off-the-shelf QBF-based verifier supporting both HyperLTL and A-HLTL specifications against NuSMV and Verilog models. Our approach uniquely integrates QBF encoding depth into the semantic unfolding of hyperproperties and the handling of quantifier alternation, enabling support for arbitrary-order quantifier nesting. We introduce a dual-mode architecture—bug-hunting and synthesis—that guarantees semantic consistency between counterexamples and existential witnesses. Key optimizations include trace-bounded encoding, automated model translation, and instantiation refinement. Evaluated across seven benchmark categories—including information-flow security and concurrent data structures—the tool achieves efficient verification. Experimental results demonstrate significant performance improvements over state-of-the-art tools on complex hyperproperties such as non-interference, non-transitivity, and confidentiality refinement.
📝 Abstract
We present HyperQB, a push-button QBF-based bounded model checker for hyperproperties. HyperQB takes as input a NuSMV model and a formula expressed in the temporal logic HyperLTL. Our QBF-based technique allows HyperQB to seamlessly deal with quantifier alternations. Based on the selection of either bug hunting or synthesis, the instances of counterexamples (for negated formula) or witnesses (for synthesis of positive formulas) are returned. We report on successful and effective verification for a rich set of experiments on a variety of case studies, including information-flow security, concurrent data structures, path planning for robots, co-termination, deniability, intransitivity of non-interference, and secrecy-preserving refinement. We also rigorously compare and contrast HyperQB with existing tools for model checking hyperporperties.