2,582 research outputs found
Empirical Review of Smart Contract and DeFi Security: Vulnerability Detection and Automated Repair
Decentralized Finance (DeFi) is emerging as a peer-to-peer financial
ecosystem, enabling participants to trade products on a permissionless
blockchain. Built on blockchain and smart contracts, the DeFi ecosystem has
experienced explosive growth in recent years. Unfortunately, smart contracts
hold a massive amount of value, making them an attractive target for attacks.
So far, attacks against smart contracts and DeFi protocols have resulted in
billions of dollars in financial losses, severely threatening the security of
the entire DeFi ecosystem. Researchers have proposed various security tools for
smart contracts and DeFi protocols as countermeasures. However, a comprehensive
investigation of these efforts is still lacking, leaving a crucial gap in our
understanding of how to enhance the security posture of the smart contract and
DeFi landscape.
To fill the gap, this paper reviews the progress made in the field of smart
contract and DeFi security from the perspective of both vulnerability detection
and automated repair. First, we analyze the DeFi smart contract security issues
and challenges. Specifically, we lucubrate various DeFi attack incidents and
summarize the attacks into six categories. Then, we present an empirical study
of 42 state-of-the-art techniques that can detect smart contract and DeFi
vulnerabilities. In particular, we evaluate the effectiveness of traditional
smart contract bug detection tools in analyzing complex DeFi protocols.
Additionally, we investigate 8 existing automated repair tools for smart
contracts and DeFi protocols, providing insight into their advantages and
disadvantages. To make this work useful for as wide of an audience as possible,
we also identify several open issues and challenges in the DeFi ecosystem that
should be addressed in the future.Comment: This paper is submitted to the journal of Expert Systems with
Applications (ESWA) for revie
Implementation Ids for Web Security Mechanism against Injection and Multiple Attacks
In this paper we propose a philosophy and a model apparatus to assess web application security instruments. The approach is in view of the thought that infusing sensible Vulnerabilities in a web application and assaulting them naturally can be utilized to bolster the evaluation of existing security systems and apparatuses in custom setup situations. The investigations incorporate the assessment of scope and bogus positives of an interruption recognition framework for SQL Injection assaults and the viability's evaluation of two top business web application defenselessness scanners. Results demonstrate that the infusion of vulnerabilities and assaults is to be sure a viable approach to assess security components and to bring up their shortcomings as well as courses for their change
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Systems and methods for automated detection of application vulnerabilities
*/Board of Regents, University of Texas Syste
Non-invasive Techniques Towards Recovering Highly Secure Unclonable Cryptographic Keys and Detecting Counterfeit Memory Chips
Due to the ubiquitous presence of memory components in all electronic computing systems, memory-based signatures are considered low-cost alternatives to generate unique device identifiers (IDs) and cryptographic keys. On the one hand, this unique device ID can potentially be used to identify major types of device counterfeitings such as remarked, overproduced, and cloned. On the other hand, memory-based cryptographic keys are commercially used in many cryptographic applications such as securing software IP, encrypting key vault, anchoring device root of trust, and device authentication for could services. As memory components generate this signature in runtime rather than storing them in memory, an attacker cannot clone/copy the signature and reuse them in malicious activity. However, to ensure the desired level of security, signatures generated from two different memory chips should be completely random and uncorrelated from each other. Traditionally, memory-based signatures are considered unique and uncorrelated due to the random variation in the manufacturing process. Unfortunately, in previous studies, many deterministic components of the manufacturing process, such as memory architecture, layout, systematic process variation, device package, are ignored. This dissertation shows that these deterministic factors can significantly correlate two memory signatures if those two memory chips share the same manufacturing resources (i.e., manufacturing facility, specification set, design file, etc.). We demonstrate that this signature correlation can be used to detect major counterfeit types in a non-invasive and low-cost manner. Furthermore, we use this signature correlation as side-channel information to attack memory-based cryptographic keys. We validate our contribution by collecting data from several commercially available off-the-shelf (COTS) memory chips/modules and considering different usage-case scenarios
Do you still need a manual smart contract audit?
We investigate the feasibility of employing large language models (LLMs) for
conducting the security audit of smart contracts, a traditionally
time-consuming and costly process. Our research focuses on the optimization of
prompt engineering for enhanced security analysis, and we evaluate the
performance and accuracy of LLMs using a benchmark dataset comprising 52
Decentralized Finance (DeFi) smart contracts that have previously been
compromised.
Our findings reveal that, when applied to vulnerable contracts, both GPT-4
and Claude models correctly identify the vulnerability type in 40% of the
cases. However, these models also demonstrate a high false positive rate,
necessitating continued involvement from manual auditors. The LLMs tested
outperform a random model by 20% in terms of F1-score.
To ensure the integrity of our study, we conduct mutation testing on five
newly developed and ostensibly secure smart contracts, into which we manually
insert two and 15 vulnerabilities each. This testing yielded a remarkable
best-case 78.7% true positive rate for the GPT-4-32k model. We tested both,
asking the models to perform a binary classification on whether a contract is
vulnerable, and a non-binary prompt. We also examined the influence of model
temperature variations and context length on the LLM's performance.
Despite the potential for many further enhancements, this work lays the
groundwork for a more efficient and economical approach to smart contract
security audits
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