How Effective are Smart Contract Analysis Tools? Evaluating Smart Contract Static Analysis Tools Using Bug Injection

Security attacks targeting smart contracts have been on the rise, which have led to financial loss and erosion of trust. Therefore, it is important to enable developers to discover security vulnerabilities in smart contracts before deployment. A number of static analysis tools have been developed for finding security bugs in smart contracts. However, despite the numerous bug-finding tools, there is no systematic approach to evaluate the proposed tools and gauge their effectiveness. This paper proposes SolidiFI, an automated and systematic approach for evaluating smart contract static analysis tools. SolidiFI is based on injecting bugs (i.e., code defects) into all potential locations in a smart contract to introduce targeted security vulnerabilities. SolidiFI then checks the generated buggy contract using the static analysis tools, and identifies the bugs that the tools are unable to detect (false-negatives) along with identifying the bugs reported as false-positives. SolidiFI is used to evaluate six widely-used static analysis tools, namely, Oyente, Securify, Mythril, SmartCheck, Manticore and Slither, using a set of 50 contracts injected by 9369 distinct bugs. It finds several instances of bugs that are not detected by the evaluated tools despite their claims of being able to detect such bugs, and all the tools report many false positivesComment: ISSTA 202

EASYFLOW: Keep Ethereum Away From Overflow

While Ethereum smart contracts enabled a wide range of blockchain applications, they are extremely vulnerable to different forms of security attacks. Due to the fact that transactions to smart contracts commonly involve cryptocurrency transfer, any successful attacks can lead to money loss or even financial disorder. In this paper, we focus on the overflow attacks in Ethereum , mainly because they widely rooted in many smart contracts and comparatively easy to exploit. We have developed EASYFLOW , an overflow detector at Ethereum Virtual Machine level. The key insight behind EASYFLOW is a taint analysis based tracking technique to analyze the propagation of involved taints. Specifically, EASYFLOW can not only divide smart contracts into safe contracts, manifested overflows, well-protected overflows and potential overflows, but also automatically generate transactions to trigger potential overflows. In our preliminary evaluation, EASYFLOW managed to find potentially vulnerable Ethereum contracts with little runtime overhead.Comment: Proceedings of the 41st International Conference on Software Engineering: Companion Proceedings. IEEE Press, 201

The Art of The Scam: Demystifying Honeypots in Ethereum Smart Contracts

Modern blockchains, such as Ethereum, enable the execution of so-called smart contracts - programs that are executed across a decentralised network of nodes. As smart contracts become more popular and carry more value, they become more of an interesting target for attackers. In the past few years, several smart contracts have been exploited by attackers. However, a new trend towards a more proactive approach seems to be on the rise, where attackers do not search for vulnerable contracts anymore. Instead, they try to lure their victims into traps by deploying seemingly vulnerable contracts that contain hidden traps. This new type of contracts is commonly referred to as honeypots. In this paper, we present the first systematic analysis of honeypot smart contracts, by investigating their prevalence, behaviour and impact on the Ethereum blockchain. We develop a taxonomy of honeypot techniques and use this to build HoneyBadger - a tool that employs symbolic execution and well defined heuristics to expose honeypots. We perform a large-scale analysis on more than 2 million smart contracts and show that our tool not only achieves high precision, but is also highly efficient. We identify 690 honeypot smart contracts as well as 240 victims in the wild, with an accumulated profit of more than $90,000 for the honeypot creators. Our manual validation shows that 87% of the reported contracts are indeed honeypots

A New View on Classification of Software Vulnerability Mitigation Methods

Software vulnerability mitigation is a well-known research area and many methods have been proposed for it Some papers try to classify these methods from different specific points of views In this paper we aggregate all proposed classifications and present a comprehensive classification of vulnerability mitigation methods We define software vulnerability as a kind of software fault and correspond the classes of software vulnerability mitigation methods accordingly In this paper the software vulnerability mitigation methods are classified into vulnerability prevention vulnerability tolerance vulnerability removal and vulnerability forecasting We define each vulnerability mitigation method in our new point of view and indicate some methods for each class Our general point of view helps to consider all of the proposed methods in this review We also identify the fault mitigation methods that might be effective in mitigating the software vulnerabilities but are not yet applied in this area Based on that new directions are suggested for the future researc

Automatic Software Repair: a Bibliography

This article presents a survey on automatic software repair. Automatic software repair consists of automatically finding a solution to software bugs without human intervention. This article considers all kinds of repairs. First, it discusses behavioral repair where test suites, contracts, models, and crashing inputs are taken as oracle. Second, it discusses state repair, also known as runtime repair or runtime recovery, with techniques such as checkpoint and restart, reconfiguration, and invariant restoration. The uniqueness of this article is that it spans the research communities that contribute to this body of knowledge: software engineering, dependability, operating systems, programming languages, and security. It provides a novel and structured overview of the diversity of bug oracles and repair operators used in the literature