The SAT solver CaDiCaL provides a rich feature set with a clean library interface. It has
been adopted by many users, is well documented and easy to extend due to its effective …

Modern software and networks underpin our digital society, yet the rapid growth of
vulnerabilities that are uncovered within these threaten our cyber security posture …

Evaluation of deep reinforcement learning (RL) is inherently challenging. Especially the
opaqueness of learned policies and the stochastic nature of both agents and environments …

In this paper, we present a technique for learning seed-adaptive mutation strategies for
fuzzers. The performance of mutation-based fuzzers highly depends on the mutation …

SMT solvers are highly complex pieces of software with performance, robustness, and
correctness as key requirements. Complementing traditional testing techniques for these …

Treating a saturation-based automatic theorem prover (ATP) as a Las Vegas randomized
algorithm is a way to illuminate the chaotic nature of proof search and make it amenable to …

We present a novel tool BertRLFuzzer, a BERT and Reinforcement Learning (RL) based
fuzzer aimed at finding security vulnerabilities for Web applications. BertRLFuzzer works as …

Ensuring correctness of verification tools is equally important as the correctness of the actual
problems they try to establish. In this paper we evaluate automated fuzzing and debugging …

Over the past few years, SMT string solvers have found their applications in an increasing
number of domains, such as program analyses in mobile and Web applications, which …

Coverage-guided fuzzing (CGF) is a widely used technique for exposing vulnerabilities in
software. Despite its success, selecting the ideal CGF fuzzer or fuzzer combination for a …