Zobrazeno 1 - 10
of 994
pro vyhledávání: '"Cotroneo P."'
This practical experience report explores Neural Machine Translation (NMT) models' capability to generate offensive security code from natural language (NL) descriptions, highlighting the significance of contextual understanding and its impact on mod
Externí odkaz:
http://arxiv.org/abs/2408.02402
Context: AI code generators are revolutionizing code writing and software development, but their training on large datasets, including potentially untrusted source code, raises security concerns. Furthermore, these generators can produce incomplete c
Externí odkaz:
http://arxiv.org/abs/2404.07548
Autor:
Cotroneo, Domenico, Liguori, Pietro
Traditional software fault injection methods, while foundational, face limitations in adequately representing real-world faults, offering customization, and requiring significant manual effort and expertise. This paper introduces a novel methodology
Externí odkaz:
http://arxiv.org/abs/2404.07491
Publikováno v:
IEEE Security & Privacy, Early Access, February 2024
Recent advances of artificial intelligence (AI) code generators are opening new opportunities in software security research, including misuse by malicious actors. We review use cases for AI code generators for security and introduce an evaluation ben
Externí odkaz:
http://arxiv.org/abs/2402.01219
Advanced Persistent Threats (APTs) represent the most threatening form of attack nowadays since they can stay undetected for a long time. Adversary emulation is a proactive approach for preparing against these attacks. However, adversary emulation to
Externí odkaz:
http://arxiv.org/abs/2311.08274
Evaluating the correctness of code generated by AI is a challenging open problem. In this paper, we propose a fully automated method, named ACCA, to evaluate the correctness of AI-generated code for security purposes. The method uses symbolic executi
Externí odkaz:
http://arxiv.org/abs/2310.18834
AI-based code generators have become pivotal in assisting developers in writing software starting from natural language (NL). However, they are trained on large amounts of data, often collected from unsanitized online sources (e.g., GitHub, HuggingFa
Externí odkaz:
http://arxiv.org/abs/2308.04451
In this work, we present a method to add perturbations to the code descriptions to create new inputs in natural language (NL) from well-intentioned developers that diverge from the original ones due to the use of new words or because they miss part o
Externí odkaz:
http://arxiv.org/abs/2306.05079
Nowadays, industries are looking into virtualization as an effective means to build safe applications, thanks to the isolation it can provide among virtual machines (VMs) running on the same hardware. In this context, a fundamental issue is understan
Externí odkaz:
http://arxiv.org/abs/2303.12817
Publikováno v:
BioMedInformatics, Vol 4, Iss 3, Pp 1589-1619 (2024)
Background: Cognitive loss is one of the biggest health problems for older people. The incidence of dementia increases with age, so Alzheimer’s disease (AD), the most prevalent type of dementia, is expected to increase. Patients with dementia find
Externí odkaz:
https://doaj.org/article/c60b076ceb894c888301a17b57cf1e70