Automated software testing based on fuzzing has experienced a revival in recent years. Especially feedback-driven fuzzing has become well-known for its ability to efficiently perform randomized testing with limited input corpora. Despite a lot of progress, two common problems are magic numbers and (nested) checksums. Computationally expensive methods such as taint tracking and symbolic execution are typically used to overcome such roadblocks. Unfortunately, such methods often require access to source code, a rather precise description of the environment (e.g., behavior of library calls or the underlying OS), or the exact semantics of the platform's instruction set. In this paper, we introduce a lightweight, yet very effective alternative to taint tracking and symbolic execution to facilitate and optimize state-of-the-art feedback fuzzing that easily scales to large binary applications and unknown environments. We observe that during the execution of a given program, parts of the input often end up directly (i.e., nearly unmodified) in the program state. This input-to-state correspondence can be exploited to create a robust method to overcome common fuzzing roadblocks in a highly effective and efficient manner. Our prototype implementation, called REDQUEEN, is able to solve magic bytes and (nested) checksum tests automatically for a given binary executable. Additionally, we show that our techniques outperform various state-of-the-art tools on a wide variety of targets across different privilege levels (kernel-space and userland) with no platform-specific code. REDQUEEN is the first method to find more than 100% of the bugs planted in LAVA-M across all targets. Furthermore, we were able to discover 65 new bugs and obtained 16 CVEs in multiple programs and OS kernel drivers. Finally, our evaluation demonstrates that REDQUEEN is fast, widely applicable and outperforms concurrent approaches by up to three orders of magnitude.

Fig. 1: AFL and AFL + IJON trying to defeat Bowser in Super Mario Bros. (Level 3-4). The lines are the traces of all runs found by the fuzzer.

Virtual machine monitors (VMMs, also called hypervisors) represent a very critical part of a modern software stack: compromising them could allow an attacker to take full control of the whole cloud infrastructure of any cloud provider. Hence their security is critical for many applications, especially in the context of Infrastructure-as-a-Service. In this paper, we present the design and implementation of HYPER-CUBE, a novel fuzzer that aims explicitly at testing hypervisors in an efficient, effective, and precise way. Our approach is based on a custom operating system that implements a custom bytecode interpreter. This high-throughput design for long-running, interactive targets allows us to fuzz a large number of both open source and proprietary hypervisors. In contrast to one-dimensional fuzzers such as AFL, HYPER-CUBE can interact with any number of interfaces in any order. Our evaluation results show that we can find more bugs (over 2×) and coverage (as much as 2×) than state-of-the-art hypervisor fuzzers. In most cases, we were even able to do so using multiple orders of magnitude less time than comparable fuzzers. HYPER-CUBE was also able to rediscover a set of well-known hypervisor vulnerabilities, such as VENOM, in less than five minutes. In total, we found 54 novel bugs, and so far obtained 43 CVEs. Our evaluation results demonstrate that nextgeneration coverage-guided fuzzers should incorporate a higherthroughput design for long-running targets such as hypervisors.

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