Blockchain adoption has surged with the rise of Decentralized Finance (DeFi) applications. However, the significant value of digital assets managed by DeFi protocols makes them prime targets for attacks. Current smart contract vulnerability detection tools struggle with DeFi protocols due to deep logical bugs arising from complex financial interactions between multiple smart contracts. These tools primarily analyze individual contracts and resort to brute-force methods for DeFi protocols crossing numerous smart contracts, leading to inefficiency. We introduce Foray, a highly effective attack synthesis framework against deep logical bugs in DeFi protocols. Foray proposes a novel attack sketch generation and completion framework. Specifically, instead of treating DeFis as regular programs, we design a domain-specific language (DSL) to lift the low-level smart contracts into their high-level financial operations. Based on our DSL, we first compile a given DeFi protocol into a token flow graph, our graphical representation of DeFi protocols. Then, we design an efficient sketch generation method to synthesize attack sketches for a certain attack goal (e.g., price manipulation, arbitrage, etc.). This algorithm strategically identifies candidate sketches by finding reachable paths in TFG, which is much more efficient than random enumeration. For each candidate sketch written in our DSL, Foray designs a domain-specific symbolic compilation to compile it into SMT constraints. Our compilation simplifies the constraints by removing redundant smart contract semantics. It maintains the usability of symbolic compilation, yet scales to problems orders of magnitude larger. Finally, the candidates are completed via existing solvers and are transformed into concrete attacks via direct syntax transformation.
Knowdit: Agentic Smart Contract Vulnerability Detection with Auditing Knowledge Summarization
Nanyang Technological University（南洋理工大学）；National Superior College for Engineers, Beihang University（北京航空航天大学高等工程学院）；Bitslab；The Chinese University of Hong Kong（香港中文大学）
SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities
Huazhong University of Science and Technology（华中科技大学）；Hebei University（河北大学）；University of Colorado Colorado Springs（科罗拉多大学科罗拉多斯普林斯分校）；University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）
Mitigating Deep Learning Vulnerabilities from Adversarial Examples Attack in the Cybersecurity Domain
Institute of Electrical and Electronic Engineers（电气与电子工程师协会）
已知深度学习模型通过在具有最佳准确度的大型数据集上采用多个历元和训练样本来解决分类和回归问题。然而，这并不意味着它们是防攻击的或不暴露于漏洞。新部署的系统，特别是在公共环境（即公共网络）上的系统，容易受到来自各种实体的攻击。此外，已发表的关于深度学习系统的研究（Goodfellow et al.，2014）已经确定了大量的攻击点和广泛的攻击面，这些攻击面有证据表明存在来自对抗性示例的利用。成功利用这些系统可能会导致严重的现实世界的影响。例如，（1）对运行深度强化学习系统的自动驾驶汽车的对抗攻击会对人类产生直接的错误分类，从而导致不幸的事故。(2)自动驾驶车辆误读红灯信号可能导致汽车撞上另一辆汽车（3）将行人车道误分类为交叉路口车道，可能导致车祸。这只是冰山一角，计算机视觉的部署并不完全集中在自动驾驶汽车上，还包括许多其他领域，这将对现实世界产生决定性的影响。必须在发展的早期阶段减轻这些脆弱性。在实际部署之前，必须在全球范围内制定和实施基线安全标准。
Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detection
Zhejiang Gongshang University（浙江工商大学）；Zhejiang University（浙江大学）；National University of Singapore（新加坡国立大学）；Southern University of Science and Technology（南方科技大学）；Zhejiang Lab（之江实验室）
VulDeePecker: A Deep Learning-Based System for Vulnerability Detection
Huazhong University of Science and Technology（华中科技大学）；Hebei University（河北大学）；Shenzhen Huazhong University of Science and Technology Research Institute（深圳华中科技大学研究院）；University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）
软件漏洞的自动检测是一个重要的研究课题。然而，该问题的现有解决方案依赖于人类专家来定义特征，并且经常错过许多漏洞（即，导致高假阴性率）。在本文中，我们开始研究使用基于深度学习的漏洞检测，以减轻人类专家手动定义特征的繁琐和主观任务。由于深度学习的动机是处理与漏洞检测问题非常不同的问题，因此我们需要一些将深度学习应用于漏洞检测的指导原则。特别是，我们需要找到适合深度学习的软件程序的表示。为此，我们建议使用代码小工具来表示程序，然后将它们转换为向量，其中代码小工具是一些（不一定是连续的）代码行，这些代码行在语义上彼此相关。这导致了一个基于深度学习的漏洞检测系统的设计和实现，称为Vulnerability Deep Pecker（VulDeepPecker）。为了评估VulDeePecker，我们为深度学习方法提供了第一个漏洞数据集。实验结果表明，VulDeePecker可以实现比其他方法少得多的假阴性（具有合理的假阳性）。我们进一步将VulDeePecker应用于3个软件产品（即Xen，Pionkey和Libav），并检测到4个漏洞，这些漏洞在国家漏洞数据库中没有报告，但在发布这些产品的后续版本时被供应商“默默”修补;相比之下，这些漏洞几乎完全被我们实验的其他漏洞检测系统所遗漏。
Obfuscating Code Vulnerabilities against Static Analysis in JavaScript Code
University of Verona（维罗纳大学）；University of Cagliari（卡利亚里大学）；School of Advanced Defense Studies (CASD)（高级防务研究学院（CASD））；National Interuniversity Consortium for Informatics（国家大学间信息学联盟）
LLM-Powered Detection of Price Manipulation in DeFi
The Hong Kong University of Science and Technology（香港科技大学）；MegaETH；McGill University（麦吉尔大学）；Nankai University（南开大学）；Monash University（莫纳什大学）；Southern University of Science and Technology（南方科技大学）
AiRacleX: Automated Detection of Price Oracle Manipulations via LLM-Driven Knowledge Mining and Prompt Generation
Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（高性能计算研究所（科技研究局））；Singapore Management University（新加坡管理大学）
