Smart contracts are increasingly being used to manage large numbers of high-value cryptocurrency accounts. There is a strong demand for automated, efficient, and comprehensive methods to detect security vulnerabilities in a given contract. While the literature features a plethora of analysis methods for smart contracts, the existing proposals do not address the increasing complexity of contracts. Existing analysis tools suffer from false alarms and missed bugs in today’s smart contracts that are increasingly defined by complexity and interdependencies. To scale accurate analysis to modern smart contracts, we introduce EF↯CF, a high-performance fuzzer for Ethereum smart contracts. In contrast to previous work, EF↯CF efficiently and accurately models complex smart contract interactions, such as reentrancy and cross-contract interactions, at a very high fuzzing throughput rate. To achieve this, EF↯CF transpiles smart contract bytecode into native C++ code, thereby enabling the reuse of existing, optimized fuzzing toolchains. Furthermore, EF↯CF increases fuzzing efficiency by employing a structure-aware mutation engine for smart contract transaction sequences and using a contract’s ABI to generate valid transaction inputs. In a comprehensive evaluation, we show that EF↯CF scales better—without compromising accuracy—to complex contracts compared to state-of-the-art approaches, including other fuzzers, symbolic/concolic execution, and hybrid approaches. Moreover, we show that EF↯CF can automatically generate transaction sequences that exploit reentrancy bugs to steal Ether.

Memory safety in complex applications implemented in unsafe programming languages such as C/C++ is still an unresolved problem in practice. Such applications were often developed in an ad-hoc, security-ignorant fashion, and thus they contain many types of security issues. Many different types of defenses have been proposed in the past to mitigate these problems, some of which are even widely used in practice. However, advanced attacks are still able to circumvent these defenses, and the arms race is not (yet) over. On the defensive side, the most promising next step is a tighter integration of the hardware and software level: modern mitigation techniques are either accelerated using hardware extensions or implemented in the hardware by extensions of the instruction set architecture (ISA). In particular, memory tagging, as proposed by ARM or SPARC, promises to solve many issues for practical memory safety. Unfortunately, Intel x86-64, which represents the most important ISA for both the desktop and server domain, lacks support for hardware-accelerated memory tagging, so memory tagging is not considered practical for this platform. In this paper, we present the design and implementation of an efficient, software-only pointer tagging scheme for Intel x86-64 based on a novel metadata embedding scheme. The basic idea is to alias multiple virtual pages to one physical page so that we can efficiently embed tag bits into a pointer. Furthermore, we introduce several optimizations that significantly reduce the performance impact of this approach to memory tagging. Based on this scheme, we propose a novel use-after-free mitigation scheme, called xTag, that offers better performance and strong security properties compared to state-of-the-art methods. We also show how double-free vulnerabilities can be mitigated. Our approach is highly compatible, allowing pointers to be passed back and forth between instrumented and non-instrumented code without losing metadata, and it is even compatible with inline assembly. We conclude that building exploit mitigation mechanisms on top of our memory tagging scheme is feasible on Intel x86-64, as demonstrated by the effective prevention of use-after-free bugs in the Firefox web browser.

Android is an open software platform for mobile devices with a large market share in the smart phone sector. The openness of the system as well as its wide adoption lead to an increasing amount of malware developed for this platform. ANANAS is an expandable and modular framework for analyzing Android applications. It takes care of common needs for dynamic malware analysis and provides an interface for the development of plugins. Adaptability and expandability have been main design goals during the development process. An abstraction layer for simple user interaction and phone event simulation is also part of the framework. It allows an analyst to script the required user simulation or phone events on demand or adjust the simulation to his needs. Six plugins have been developed for ANANAS. They represent well known techniques for malware analysis, such as system call hooking and network traffic analysis. The focus clearly lies on dynamic analysis, as five of the six plugins are dynamic analysis methods.