Biblio

Modern verifiers for cryptographic protocols can analyze sophisticated designs automatically, but require the entire code of the protocol to operate. Compositional techniques, by contrast, allow us to verify each system component separately, against its own guarantees and assumptions about other components and the environment. Compositionality helps protocol design because it explains how the design can evolve and when it can run safely along other protocols and programs. For example, it might say that it is safe to add some functionality to a server without having to patch the client. Unfortunately, while compositional frameworks for protocol verification do exist, they require non-trivial human effort to identify specifications for the components of the system, thus hindering their adoption. To address these shortcomings, we investigate techniques for automated, compositional analysis of authentication protocols, using automata-learning techniques to synthesize assumptions for protocol components. We report preliminary results on the Needham-Schroeder-Lowe protocol, where our synthesized assumption was capable of lowering verification time while also allowing us to verify protocol variants compositionally.

Side-channel attacks allow an adversary to uncover secret program data by observing the behavior of a program with respect to a resource, such as execution time, consumed memory or response size. Side-channel vulnerabilities are difficult to reason about as they involve analyzing the correlations between resource usage over multiple program paths. We present DifFuzz, a fuzzing-based approach for detecting side-channel vulnerabilities related to time and space. DifFuzz automatically detects these vulnerabilities by analyzing two versions of the program and using resource-guided heuristics to find inputs that maximize the difference in resource consumption between secret-dependent paths. The methodology of DifFuzz is general and can be applied to programs written in any language. For this paper, we present an implementation that targets analysis of Java programs, and uses and extends the Kelinci and AFL fuzzers. We evaluate DifFuzz on a large number of Java programs and demonstrate that it can reveal unknown side-channel vulnerabilities in popular applications. We also show that DifFuzz compares favorably against Blazer and Themis, two state-of-the-art analysis tools for finding side-channels in Java programs.