Integer Overflow Vulnerabilities Research Articles

A Smart Contract Vulnerability Detection Method Based on Multimodal Feature Fusion and Deep Learning

With the proliferation of blockchain technology in decentralized applications like decentralized finance and supply chain and identity management, smart contracts operating on a blockchain frequently encounter security issues such as reentrancy vulnerabilities, timestamp dependency vulnerabilities, tx.origin vulnerabilities, and integer overflow vulnerabilities. These security concerns pose a significant risk of causing substantial losses to user accounts. Consequently, the detection of vulnerabilities in smart contracts has become a prominent area of research. Existing research exhibits limitations, including low detection accuracy in traditional smart contract vulnerability detection approaches and the tendency of deep learning-based solutions to focus on a single type of vulnerability. To address these constraints, this paper introduces a smart contract vulnerability detection method founded on multimodal feature fusion. This method adopts a multimodal perspective to extract three modal features from the lifecycle of smart contracts, leveraging both static and dynamic features comprehensively. Through deep learning models like Graph Convolutional Networks (GCNs) and bidirectional Long Short-Term Memory networks (bi-LSTMs), effective detection of vulnerabilities in smart contracts is achieved. Experimental results demonstrate that the proposed method attains detection accuracies of 85.73% for reentrancy vulnerabilities, 85.41% for timestamp dependency vulnerabilities, 83.58% for tx.origin vulnerabilities, and 90.96% for integer Overflow vulnerabilities. Furthermore, ablation experiments confirm the efficacy of the newly introduced modal features, highlighting the significance of fusing dynamic and static features in enhancing detection accuracy.

MSmart: Smart Contract Vulnerability Analysis and Improved Strategies Based on Smartcheck

As is well known, smart contracts on the blockchain store plenty of digital assets, and these contracts deployed on the blockchain are difficult to be modified. For this reason, the analysis and detection of smart contract vulnerabilities have received extensive attention. Smartcheck, a typical Java-implemented static analysis tool of smart contracts, is capable of converting Solidity source code into path diagrams based on the lexical and syntactic analysis, and finds smart contract vulnerabilities by path matching. Although Smartcheck can analyze most of the real-world vulnerabilities, some imperceptible vulnerabilities may be ignored, causing huge economic losses. In order to address these issues, we develop a new tool named MSmart to analyze the vulnerabilities of high risk such as timestamp dependence vulnerabilities, integer overflow vulnerabilities, self-destruct vulnerabilities, etc. MSmart converts the smart contract source code into an intermediate representation, and looks for smart contract vulnerabilities based on intermediate representation and XPath rules. We add new intermediate representation rules of Smartcheck to detect more kinds of vulnerabilities and optimize existing rules to suit the complexity of smart contract. We also implemented smart contract batch detection to shorten the time it takes to find vulnerabilities. To analysis the performance of MSmart, we collect 6000 real-world contracts from Etherscan and design some comparative experiments with other tools. The results of experiment show that MSmart is able to analyze related vulnerabilities better, and false positives and false negatives have been reduced due to our improvements.

Mutation testing for integer overflow in ethereum smart contracts

Integer overflow is a common vulnerability in Ethereum Smart Contracts (ESCs) and often causes huge economic losses. Smart contracts cannot be changed once it is deployed on the blockchain and thus demand further testing. Mutation testing is a fault-based testing method that can effectively improve the sufficiency of a test for smart contracts. However, existing methods cannot efficiently perform mutation testing specifically for integer overflow in ESCs. Therefore, by analyzing integer overflow in ESCs, we propose five special mutation operators to address such vulnerability in terms of detecting sufficiency in ESC testing. An empirical study on 40 open-source ESCs is conducted to evaluate the effectiveness of the proposed mutation operators. Results show that: (1) our proposed mutation operators can reproduce all 179 integer overflow vulnerabilities in 40 smart contracts, and the generated mutants have high compilation pass rate and integer overflow vulnerability generation rate; moreover, (2) the generated mutants can find the shortcomings of existing testing methods for integer overflow vulnerability, thereby providing effective support to improve the sufficiency of the test.

A Design and Verification Methodology for a TrustZone Trusted Execution Environment

Hardware support for isolated execution (e.g., ARM TrustZone) enables the development of a trusted execution environment (TEE) that ensures the security of the code and data while communicating with a compromised rich execution environment (REE). The ability to satisfy various security services is complicated and usually consists of trusted applications, a trusted kernel and a secure monitor. However, formally verifying the security of an entire TEE security remains challenging. We present a methodology for designing a TEE in a way that enables verification of its security properties. Our methodology consists of forcing a trusted application and kernel to communicate with an REE via a narrow interface and compile and link them with a small secure monitor that implements the interface and runs at the highest privilege level. We provide functional verification of the secure monitor to ensure that it correctly switches the TEE/REE, communicates with the REE at a pre-defined memory space and has no integer overflow vulnerability. We also perform a verification of the secure monitor’s scheduler to ensure that it satisfies information flow noninterference. We present a modular verification framework that can prove an end-to-end security property for cross-language programmes (e.g., C and assembly languages). Our evaluation suggests that the methodology scales to real-world TEE applications.

An Approach to Vulnerability Searching of Integer Overflows in the Executable Program Code

This article proposes an approach to identifying integer overflow vulnerabilities in software represented by the executable code of x86 architecture. The approach is based on symbolic code execution and initially twofold representation of memory cells. A truncated control transfer graph is constructed from the machine code of the program, the paths in which are layer-by-layer checked for the feasibility of the vulnerability conditions. The proposed methods were implemented in practice and experimentally tested on the various code samples.

Sound input filter generation for integer overflow errors

We present a system, SIFT, for generating input filters that nullify integer overflow errors associated with critical program sites such as memory allocation or block copy sites. SIFT uses a static pro- gram analysis to generate filters that discard inputs that may trigger integer overflow errors in the computations of the sizes of allocated memory blocks or the number of copied bytes in block copy operations. Unlike all previous techniques of which we are aware, SIFT is sound -- if an input passes the filter, it will not trigger an integer overflow error at any analyzed site. Our results show that SIFT successfully analyzes (and therefore generates sound input filters for) 56 out of 58 memory allocation and block memory copy sites in analyzed input processing modules from five applications (VLC, Dillo, Swfdec, Swftools, and GIMP). These nullified errors include six known integer overflow vulnerabilities. Our results also show that applying these filters to 62895 real-world inputs produces no false positives. The analysis and filter generation times are all less than a second.

Using type analysis in compiler to mitigate integer-overflow-to-buffer-overflow threat

One of the top two causes of software vulnerabilities in operating systems is the integer overflow. A typical integer overflow vulnerability is the Integer Overflow to Buffer Overflow IO2BO for short vulnerability. IO2BO is an underestimated threat. Many programmers have not realized the existence of IO2BO and its harm. Even for those who are aware of IO2BO, locating and fixing IO2BO vulnerabilities are still tedious and error-prone. Automatically identifying and fixing this kind of vulnerability are critical for software security. In this article, we present the design and implementation of IntPatch, a compiler extension for automatically fixing IO2BO vulnerabilities in C/C++ programs at compile time. IntPatch utilizes classic type theory and a dataflow analysis framework to identify potential IO2BO vulnerabilities, and then uses backward slicing to find out related vulnerable arithmetic operations, and finally instruments programs with runtime checks. Moreover, IntPatch provides an interface for programmers who want to check integer overflows manually. We evaluated IntPatch on a few real-world applications. It caught all 46 previously known IO2BO vulnerabilities in our test suite and found 21 new bugs. Applications patched by IntPatch have negligible runtime performance losses which are on average 1%.

Using type analysis in compiler to mitigate integer-overflow-to-buffer-overflow threat

One of the top two causes of software vulnerabilities in operating systems is the integer overflow. A typical integer overflow vulnerability is the Integer Overflow to Buffer Overflow IO2BO for sho...