相关论文: Improving Program Debloating with 1-DU Chain Minim…

As software grows in complexity to accommodate diverse features and platforms, software bloating has emerged as a significant challenge, adversely affecting performance and security. However, existing approaches inadequately address the…

Software debloating tools seek to improve program security and performance by removing unnecessary code, called bloat. While many techniques have been proposed, several barriers to their adoption have emerged. Namely, debloating tools are…

Software debloating is an emerging field of study aimed at improving the security and performance of software by removing excess library code and features that are not needed by the end user (called bloat). Software bloat is pervasive, and…

Program debloating aims to remove unused code to reduce performance overhead, attack surfaces, and maintenance costs. Over time, debloating has evolved across multiple layers (container, library, and application), each building on the…

Nearly all modern software suffers from bloat that negatively impacts its performance and security. To combat this problem, several automated techniques have been proposed to debloat software. A key metric used in many of these works to…

Program debloating aims to enhance the performance and reduce the attack surface of bloated applications. Several techniques have been recently proposed to specialize programs. These approaches are either based on unsound strategies or…

Recent advancements in Large Language Models (LLMs) have led to their widespread adoption in daily applications. Despite their impressive capabilities, they remain vulnerable to adversarial attacks, as even minor meaning-preserving changes…

Today's Android developers tend to include numerous features to accommodate diverse user requirements, which inevitably leads to bloated apps. Yet more often than not, only a fraction of these features are frequently utilized by users, thus…

Software bloat is code that is packaged in an application but is actually not necessary to run the application. The presence of software bloat is an issue for security, for performance, and for maintenance. In this paper, we introduce a…

Defense techniques such as Data Execution Prevention (DEP) and Address Space Layout Randomization (ASLR) were the early role models preventing primitive code injection and return-oriented programming (ROP) attacks. Notably, these techniques…

Software debloating techniques are applied to craft a specialized version of the program based on the user's requirements and remove irrelevant code accordingly. The debloated programs presumably maintain better performance and reduce the…

Machine Learning (ML) models have been utilized for malware detection for over two decades. Consequently, this ignited an ongoing arms race between malware authors and antivirus systems, compelling researchers to propose defenses for…

Machine unlearning offers a promising solution to privacy and safety concerns in large language models (LLMs) by selectively removing targeted knowledge while preserving utility. However, current methods are highly sensitive to downstream…

Code-reuse attacks have become a kind of common attack method, in which attackers use the existing code in the program to hijack the control flow. Most existing defenses focus on control flow integrity (CFI), code randomization, and…

Decentralized storage systems face a fundamental trade-off between replication overhead, recovery efficiency, and security guarantees. Current approaches either rely on full replication, incurring substantial storage costs, or employ…

Software reuse may result in software bloat when significant portions of application dependencies are effectively unused. Several tools exist to remove unused (byte)code from an application or its dependencies, thus producing smaller…

Recognizing vulnerabilities in stripped binary files presents a significant challenge in software security. Although some progress has been made in generating human-readable information from decompiled binary files with Large Language…

Modern software systems heavily use C/C++ based libraries. Because of the weak memory model of C/C++, libraries may suffer from vulnerabilities which can expose the applications to potential attacks. For example, a very large number of…

Large language models (LLMs) have shown promising performance in software vulnerability detection, yet their reasoning capabilities remain unreliable. We propose R2Vul, a method that combines reinforcement learning from AI feedback (RLAIF)…

Dimensionality reduction (DR) techniques inherently distort the original structure of input high-dimensional data, producing imperfect low-dimensional embeddings. Diverse distortion measures have thus been proposed to evaluate the…