Related papers: Exploring Security Commits in Python
Large Language Models (LLMs) have shown impressive proficiency in code generation. Unfortunately, these models share a weakness with their human counterparts: producing code that inadvertently has security vulnerabilities. These…
While attackers often distribute malware to victims via open-source, community-driven package repositories, these repositories do not currently run automated malware detection systems. In this work, we explore the security goals of the…
Tabular Generative Models are often argued to preserve privacy by creating synthetic datasets that resemble training data. However, auditing their empirical privacy remains challenging, as commonly used similarity metrics fail to…
We propose a new formal criterion for secure compilation, providing strong security guarantees for components written in unsafe, low-level languages with C-style undefined behavior. Our criterion goes beyond recent proposals, which protect…
In an era shaped by Generative Artificial Intelligence for code generation and the rising adoption of Python-based Machine Learning systems (MLS), software quality has emerged as a major concern. As these systems grow in complexity and…
The field of DevOps security education necessitates innovative approaches to effectively address the ever-evolving challenges of cybersecurity. In adopting a student-centered ap-proach, there is the need for the design and development of a…
Despite being the most popular programming language, Python has not yet received enough attention from the community. To the best of our knowledge, there is no general static analysis framework proposed to facilitate the implementation of…
Attacks against computer systems exploiting software vulnerabilities can cause substantial damage to the cyber-infrastructure of our modern society and economy. To minimize the consequences, it is vital to detect and fix vulnerabilities as…
Coverage analysis is widely used but can suffer from high overhead. This overhead is especially acute in the context of Python, which is already notoriously slow (a recent study observes a roughly 30x slowdown vs. native code). We find that…
Compared to other programming languages (e.g., Java), Python has more idioms to make Python code concise and efficient. Although pythonic idioms are well accepted in the Python community, Python programmers are often faced with many…
Large language models (LLMs) for code generation are becoming integral to modern software development, but their real-world prevalence and security impact remain poorly understood. We present the first large-scale empirical study of…
We present HaPy-Bug, a curated dataset of 793 Python source code commits associated with bug fixes, with each line of code annotated by three domain experts. The annotations offer insights into the purpose of modified files, changes at the…
The increasing complexity and volume of software systems have heightened the importance of identifying and mitigating security vulnerabilities. The existing software vulnerability datasets frequently fall short in providing comprehensive,…
The objectives of cyberattacks are becoming sophisticated, and attackers are concealing their identity by masquerading as other attackers. Cyber threat intelligence (CTI) is gaining attention as a way to collect meaningful knowledge to…
Context: Identifying potential vulnerable code is important to improve the security of our software systems. However, the manual detection of software vulnerabilities requires expert knowledge and is time-consuming, and must be supported by…
The integration of large language models (LLMs) into cyber security applications presents both opportunities and critical safety risks. We introduce CyberLLMInstruct, a dataset of 54,928 pseudo-malicious instruction-response pairs spanning…
The capability of generating high-quality source code using large language models (LLMs) reduces software development time and costs. However, they often introduce security vulnerabilities due to training on insecure open-source data. This…
In addressing the pivotal role of translating natural language queries into SQL commands, we propose a suite of compact, fine-tuned models and self-refine mechanisms to democratize data access and analysis for non-expert users, mitigating…
Code sharing and reuse is a widespread use practice in software engineering. Although a vast amount of open-source Python code is accessible on many online platforms, programmers often find it difficult to restore a successful runtime…
In the field of data science, and for academics in general, the Python programming language is a popular choice, mainly because of its libraries for storing, manipulating, and gaining insight from data. Evidence includes the versatile set…