相关论文: Yama: Precise Opcode-based Data Flow Analysis for …

While Large Language Models (LLMs) demonstrate exceptional natural language capabilities, general-purpose models lack specialized domain knowledge for effective cybersecurity analysis. In this work, we investigate Domain-Adaptive Continuous…

Code comprehension and analysis of open-source project codebases is a task frequently performed by developers and researchers. However, existing tools that practitioners use for assistance with such tasks often require prior project setup,…

Mobile apps provide various critical services, such as banking, communication, and healthcare. To this end, they have access to our personal information and have the ability to perform actions on our behalf. Hence, securing mobile apps is…

Providing timely and personalized guidance for students' programming assignments, offers significant practical value for helping students complete assignments and enhance their learning. In recent years, various automated Fault Localization…

We present a novel malware detection approach based on metrics over quantitative data flow graphs. Quantitative data flow graphs (QDFGs) model process behavior by interpreting issued system calls as aggregations of quantifiable data…

The NPM ecosystem has become a primary target for software supply chain attacks, yet existing detection tools are evaluated in isolation on incompatible datasets, making cross-tool comparison unreliable. We conduct a benchmark-driven…

The proliferation of camera-enabled devices and large video repositories has led to a diverse set of video analytics applications. These applications rely on video pipelines, represented as DAGs of operations, to transform videos, process…

WebShell attacks - where adversaries implant malicious scripts on web servers - remain a persistent threat. Prior machine-learning and deep-learning detectors typically depend on task-specific supervision and can be brittle under data…

One of the most significant challenges in the field of software code auditing is the presence of vulnerabilities in software source code. Every year, more and more software flaws are discovered, either internally in proprietary code or…

With an increasing number of value-flow properties to check, existing static program analysis still tends to have scalability issues when high precision is required. We observe that the key design flaw behind the scalability problem is that…

Software vulnerabilities, caused by unintentional flaws in source code, are a primary root cause of cyberattacks. Static analysis of source code has been widely used to detect these unintentional defects introduced by software developers.…

Nowadays, cyberattacks are growing exponentially, causing havoc to Internet users. In particular, authentication attacks constitute the major attack vector where intruders impersonate legitimate users to maliciously access systems or…

Large language models (LLMs) can detect software vulnerabilities, but how do they actually identify vulnerable code? We address this question using mechanistic interpretability; analyzing the internal computations of a neural network to…

This paper presents DeepTective, a deep learning approach to detect vulnerabilities in PHP source code. Our approach implements a novel hybrid technique that combines Gated Recurrent Units and Graph Convolutional Networks to detect SQLi,…

The growing interconnection between software systems increases the need for security already at design time. Security-related properties like confidentiality are often analyzed based on data flow diagrams (DFDs). However, manually analyzing…

We present a comprehensive dataset of Java vulnerability-fixing commits (VFCs) to advance research in Java vulnerability analysis. Our dataset, derived from thousands of open-source Java projects on GitHub, comprises two variants: JavaVFC…

The npm (Node Package Manager) ecosystem is the most important package manager for JavaScript development with millions of users. Consequently, a plethora of earlier work investigated how vulnerability reporting, patch propagation, and in…

We report the findings of a reimplementation of 18 foundational studies in feature-based machine learning for Android malware detection, published during the period 2013-2023. These studies are reevaluated on a level playing field using a…

Code reuse is common in modern software development, but it can also spread vulnerabilities when developers unknowingly copy risky code. The code fragments that preserve the logic of known vulnerabilities are known as vulnerable code clones…

Android is an open software platform for mobile devices with a large market share in the smartphone sector. The openness of the system as well as its wide adoption lead to an increasing amount of malware developed for this platform. ANANAS…