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We show that for unconstrained Deep Linear Discriminant Analysis (LDA) classifiers, maximum-likelihood training admits pathological solutions in which class means drift together, covariances collapse, and the learned representation becomes…
As Android malware is growing and evolving, deep learning has been introduced into malware detection, resulting in great effectiveness. Recent work is considering hybrid models and multi-view learning. However, they use only simple…
Vulnerability detection methods based on deep learning (DL) have shown strong performance on benchmark datasets, yet their real-world effectiveness remains underexplored. Recent work suggests that both graph neural network (GNN)-based and…
A widespread belief in the blockchain security community is that automated techniques are only good for detecting shallow bugs, typically of small value. In this paper, we present the techniques and insights that have led us to repeatable…
Smart contracts are nowadays at the core of most blockchain systems, as they specify and allow an agreement between entities that wish to perform a transaction. As any computer program, smart contracts are subject to the presence of…
We present VeriSmart, a highly precise verifier for ensuring arithmetic safety of Ethereum smart contracts. Writing safe smart contracts without unintended behavior is critically important because smart contracts are immutable and even a…
LoRa provides long-range, energy-efficient communications in Internet of Things (IoT) applications that rely on Low-Power Wide-Area Network (LPWAN) capabilities. Despite these merits, concerns persist regarding the security of LoRa…
Large language models (LLMs) demonstrate considerable proficiency in numerous coding-related tasks; however, their capabilities in detecting software vulnerabilities remain limited. This limitation primarily stems from two factors: (1) the…
This paper presents SAILFISH, a scalable system for automatically finding state-inconsistency bugs in smart contracts. To make the analysis tractable, we introduce a hybrid approach that includes (i) a light-weight exploration phase that…
With the rapid growth of the NFT market, the security of smart contracts has become crucial. However, existing AI-based detection models for NFT contract vulnerabilities remain limited due to their complexity, while traditional manual…
In this work, we provide a comprehensive survey of smart contract upgradability patterns using proxies. A primary characteristic of smart contracts on the Ethereum blockchain is that they are immutable once implemented, no changes can be…
Developers of smart contracts pervasively reuse subcontracts to improve development efficiency. Like any program language, such subcontract reuse may unexpectedly include, or introduce vulnerabilities to the end-point smart contract.…
Smart contracts have transformed decentralized finance by enabling programmable, trustless transactions. However, their widespread adoption and growing financial significance have attracted persistent and sophisticated threats, such as…
Smart contracts extended blockchain functionality beyond simple transactions, powering complex applications like decentralized finance (DeFi). However, this complexity introduces serious security challenges, including price manipulation and…
As Deep Learning (DL) systems are widely deployed for mission-critical applications, debugging such systems becomes essential. Most existing works identify and repair suspicious neurons on the trained Deep Neural Network (DNN), which,…
Smart contracts are small programs on the blockchain that often handle valuable assets. Vulnerabilities in smart contracts can be costly, as time has shown over and over again. Countermeasures are high in demand and include best practice…
Fixing bugs is easiest by patching source code. However, source code is not always available: only 0.3% of the ~49M smart contracts that are currently deployed on Ethereum have their source code publicly available. Moreover, since contracts…
Code vulnerability detection is crucial for ensuring the security and reliability of modern software systems. Recently, Large Language Models (LLMs) have shown promising capabilities in this domain. However, notable discrepancies in…
Software vulnerability detection is critical in software security because it identifies potential bugs in software systems, enabling immediate remediation and mitigation measures to be implemented before they may be exploited. Automatic…
The rapid growth of the blockchain ecosystem and the increasing value locked in smart contracts necessitate robust security measures. While languages like Solidity and Move aim to improve smart contract security, vulnerabilities persist.…