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Roberto Natella

As seen in: arXiv
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Articles by Roberto Natella

Computer Science > Software Engineering

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By Roberto Natella, Naghmeh Ivaki, Nuno Laranjeiro
| arXiv
Assessing vulnerability detection tools for smart contracts requires datasets with known ground truth, yet such datasets are scarce and difficult to build by hand. We propose an approach that uses Large Language Models (LLMs) to automatically inject vulnerabilities into Solidity smart contracts, and demonstrate it in a case study targeting 49 vulnerability types from OpenSCV.
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[2510.24188] Investigating Software Aging in LLM-Generated Software Systems

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By Ermeson Andrade, Roberto Natella
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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[2505.03345] Elevating Cyber Threat Intelligence against Disinformation Campaigns with LLM-based Concept Extraction and the FakeCTI Dataset

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By Roberto Natella, Vittorio Orbinato
| arXiv
[Submitted on 6 May 2025] Title:Elevating Cyber Threat Intelligence against Disinformation Campaigns with LLM-based Concept Extraction and the FakeCTI Dataset View a PDF of the paper titled Elevating Cyber Threat Intelligence against Disinformation Campaigns with LLM-based Concept Extraction and the FakeCTI Dataset, by Domenico Cotroneo and 2 other authors View PDF Abstract:The swift spread of fake news and disinformation campaigns poses a significant threat to public trust, political...
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The Power of Words: Generating PowerShell Attacks from Natural Language

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By Roberto Natella, Christian Marescalco, Vittorio Orbinato, Luciano Pianese
| arXiv
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Vulnerabilities in AI Code Generators: Exploring Targeted Data Poisoning Attacks

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By Cristina Improta, Pietro Liguori, Roberto Natella
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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[2306.05079] Enhancing Robustness of AI Offensive Code Generators via Data Augmentation

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By Pietro Liguori, Roberto Natella, Bojan Cukic, Domenico Cotroneo
| arXiv
In this work, we present a method to add perturbations to the code descriptions, i.e., new inputs in natural language (NL) from well-intentioned developers, in the context of security-oriented code, and analyze how and to what extent perturbations affect the performance of AI offensive code generators.
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