Related papers: Path-wise Vulnerability Mitigation
Vulnerability Discovery with attack Injection security threats are increasing for the server software, when software is developed, the software tested for the functionality. Due to unawareness of software vulnerabilities most of the…
Identifying which software versions are affected by a vulnerability is critical for patching, risk mitigation. Despite a growing body of tools, their real-world effectiveness remains unclear due to narrow evaluation scopes often limited to…
The ever-evolving capabilities of cyber attackers force security administrators to focus on the early identification of emerging threats. Targeted cyber attacks usually consist of several phases, from initial reconnaissance of the network…
Automated program repair (APR) has achieved promising results, especially using neural networks. Yet, the overwhelming majority of patches produced by APR tools are confined to one single location. When looking at the patches produced with…
Automated Program Repair (APR) is a task to automatically generate patches for the buggy code. However, most research focuses on generating correct patches while ignoring the consistency between the fixed code and the original buggy code.…
In the context of test case based automated program repair (APR), the research community call the patches that pass all the test cases but fail to actually fix the bug test case overfitted patches. Currently, overfitted patches has to be…
The number of vulnerabilities reported in open source software has increased substantially in recent years. Security patches provide the necessary measures to protect software from attacks and vulnerabilities. In practice, it is difficult…
We propose FPGA-Patch, the first-of-its-kind defense that leverages automated program repair concepts to thwart power side-channel attacks on cloud FPGAs. FPGA-Patch generates isofunctional variants of the target hardware by injecting…
Relying on dependency packages accelerates software development, but it also increases the exposure to security vulnerabilities that may be present in dependencies. While developers have full control over which dependency packages (and…
Toward robust malware detection, we explore the attack surface of existing malware detection systems. We conduct root-cause analyses of the practical binary-level black-box adversarial malware examples. Additionally, we uncover the…
A timely software update is vital to combat the increasing security vulnerabilities. However, some software vendors may secretly patch their vulnerabilities without creating CVE entries or even describing the security issue in their change…
Patch-based adversarial attacks were proven to compromise the robustness and reliability of computer vision systems. However, their conspicuous and easily detectable nature challenge their practicality in real-world setting. To address…
An adversarial patch can arbitrarily manipulate image pixels within a restricted region to induce model misclassification. The threat of this localized attack has gained significant attention because the adversary can mount a…
Security vulnerabilities are among the most critical software defects in existence. As such, they require patches that are correct and quickly deployed. This motivates an automatic patch generation method that emphasizes both soundness and…
Software vulnerabilities pose serious risks to modern software ecosystems. While the National Vulnerability Database (NVD) is the authoritative source for cataloging these vulnerabilities, it often lacks explicit links to the corresponding…
Many applications have security vulnerabilities that can be exploited. It is practically impossible to find all of them due to the NP-complete nature of the testing problem. Security solutions provide defenses against these attacks through…
Vision Transformers (ViTs) have a radically different architecture with significantly less inductive bias than Convolutional Neural Networks. Along with the improvement in performance, security and robustness of ViTs are also of great…
Localized adversarial patches aim to induce misclassification in machine learning models by arbitrarily modifying pixels within a restricted region of an image. Such attacks can be realized in the physical world by attaching the adversarial…
Adversarial patch attacks pose a severe threat to deep neural networks, yet most existing approaches rely on unrealistic white-box assumptions, untargeted objectives, or produce visually conspicuous patches that limit real-world…
Vulnerabilities severely threaten software systems, making the timely application of security patches crucial for mitigating attacks. However, software vendors often silently patch vulnerabilities with limited disclosure, where Security…