Symbolic execution is a well-known software testing technique that evaluates how a program runs when considering a symbolic input, i.e., an input that can initially assume any concrete value ...admissible for its data type. The dynamic twist of this technique is dubbed concolic execution and has been demonstrated to be a practical technique for testing even complex real-world programs. Unfortunately, developing concolic engines is hard. Indeed, an engine has to correctly instrument the program to build accurate symbolic expressions, which represent the program computation. Furthermore, to reason over such expressions, it has to interact with an SMT solver. Hence, several implementation bugs may emerge within the different layers of an engine.
In this article, we consider the problem of testing concolic engines. In particular, we propose several testing strategies whose main intuition is to exploit the concrete state kept by the executor to identify inconsistencies within the symbolic state. We integrated our strategies into three state-of-the-art concolic executors (SymCC, SymQEMU, and Fuzzolic, respectively) and then performed several experiments to show that our ideas can find bugs in these frameworks. Overall, our approach was able to discover more than 12 bugs across these engines.
•We identify the main steps carried out by recent concolic frameworks during their analysis, pinpointing where implementation bugs may emerge.•We propose a set of novel and practical ideas on how to identify implementation gaps in modern concolic executors.•We integrate our strategies into state-of-the-art concolic executors and perform experiments to show that our ideas can find bugs in these frameworks.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Embedded devices are pivotal in many aspects to our everyday life, acting as key elements within our critical infrastructures, e-health sector, and the IoT ecosystem. These devices ship with custom ...software, dubbed firmware, whose development may not have followed strict security-by-design guidelines and for which no detailed documentation may be available. Given their critical role, testing their software before deploying them is crucial. Software fuzzing is a popular software testing technique that has shown to be quite effective in the last decade. However, the firmware may contain thousands of subcomponents with unexpected interplays. Moreover, operators may have a tight time budget to perform a security evaluation, requiring focused fuzzing on the most critical subcomponents. Also, considering the lack of accurate documentation for a device, it is quite hard for a security operator to understand what to fuzz and how to fuzz a specific device firmware. In this paper, we present Fuzzplanner, a visual analytics solution that enables security operators during the design of a fuzzing campaign over a device firmware. Fuzzplanner helps the operator identify the best candidates for fuzzing using several innovative visual aids. Our contributions include introducing Fuzzplanner, exploring diverse analytical tools to pinpoint critical binaries, and showing its efficacy with two real-world firmware image scenarios.
