arXiv subjects

Vittorio Orbinato

Publications and source records attributed to Vittorio Orbinato.

9 recordsLinked to original sources

arXiv2026

VulnGym: Evaluating Vulnerability Management Strategies against Advanced Persistent Threats

Enterprise networks are continuously targeted by Advanced Persistent Threats (APTs), attack campaigns exploiting software vulnerabilities to compromise critical assets over time. As disclosed vulnerabilities grow, resource-constrained organizations must prioritize which ones to patch. Existing prioritization standards score vulnerabilities individually and cannot capture how a patching policy performs against an adversary that progresses through the network over time. Previous tools have simulated attack campaigns through Reinforcement Learning (RL), but either omit vulnerability management, leaving the attacker unopposed, or rely on synthetic networks disconnected from real threat data, and so cannot assess how a policy would fare against a realistic adversary. To fill this gap, we propose VulnGym, a simulation tool to evaluate vulnerability management policies. VulnGym simulates an RL-trained attacker, calibrated on real APT profiles, against a defender executing a configurable patching policy over a network with real Common Vulnerabilities and Exposures (CVEs). Both agents act on a shared, evolving network representation, so the attacker's progress is directly shaped by the defender's patching activity, allowing a given policy to be stress-tested against a realistic attack campaign. Experiments based on real-world vulnerabilities and two APTs show that vulnerability management must be tailored to organizational context, adversarial behavior, network topology, and asset criticality.

cs.CR↗

arXiv2026

AI-Generated PowerShell Malware: An Experimental Framework and Dataset

Generative AI has emerged as a significant cybersecurity threat, with several recent attack campaigns leveraging LLMs to generate code for malicious purposes via scripting languages such as PowerShell. Consequently, for cybersecurity analysts, it is imperative to investigate the offensive capabilities of AI code generators. In this paper, we propose an experimental framework to assess LLM-generated PowerShell malware, which comprises a novel sandbox approach for dynamic analysis of AI-generated malware. Furthermore, we present a novel, manually curated dataset of real-world PowerShell malware, annotated in natural language to assist the training and evaluation of LLMs. Finally, this study evaluates permissive, open-weight LLMs adapted to PowerShell malware generation. Our results reveal a high degree of similarity between real malware and LLM-generated ones in terms of triggered OS malicious events, with a median Jaccard index of 84.5% and 48.4% of instances achieving complete overlap.

cs.CR↗

arXiv2025

Elevating Cyber Threat Intelligence against Disinformation Campaigns with LLM-based Concept Extraction and the FakeCTI Dataset

The swift spread of fake news and disinformation campaigns poses a significant threat to public trust, political stability, and cybersecurity. Traditional Cyber Threat Intelligence (CTI) approaches, which rely on low-level indicators such as domain names and social media handles, are easily evaded by adversaries who frequently modify their online infrastructure. To address these limitations, we introduce a novel CTI framework that focuses on high-level, semantic indicators derived from recurrent narratives and relationships of disinformation campaigns. Our approach extracts structured CTI indicators from unstructured disinformation content, capturing key entities and their contextual dependencies within fake news using Large Language Models (LLMs). We further introduce FakeCTI, the first dataset that systematically links fake news to disinformation campaigns and threat actors. To evaluate the effectiveness of our CTI framework, we analyze multiple fake news attribution techniques, spanning from traditional Natural Language Processing (NLP) to fine-tuned LLMs. This work shifts the focus from low-level artifacts to persistent conceptual structures, establishing a scalable and adaptive approach to tracking and countering disinformation campaigns.

cs.CR↗

arXiv2025

CTI-HAL: A Human-Annotated Dataset for Cyber Threat Intelligence Analysis

Organizations are increasingly targeted by Advanced Persistent Threats (APTs), which involve complex, multi-stage tactics and diverse techniques. Cyber Threat Intelligence (CTI) sources, such as incident reports and security blogs, provide valuable insights, but are often unstructured and in natural language, making it difficult to automatically extract information. Recent studies have explored the use of AI to perform automatic extraction from CTI data, leveraging existing CTI datasets for performance evaluation and fine-tuning. However, they present challenges and limitations that impact their effectiveness. To overcome these issues, we introduce a novel dataset manually constructed from CTI reports and structured according to the MITRE ATT&CK framework. To assess its quality, we conducted an inter-annotator agreement study using Krippendorff alpha, confirming its reliability. Furthermore, the dataset was used to evaluate a Large Language Model (LLM) in a real-world business context, showing promising generalizability.

cs.CR↗

arXiv2024

Laccolith: Hypervisor-Based Adversary Emulation with Anti-Detection

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 tools lack the anti-detection abilities of APTs. We introduce Laccolith, a hypervisor-based solution for adversary emulation with anti-detection to fill this gap. We also present an experimental study to compare Laccolith with MITRE CALDERA, a state-of-the-art solution for adversary emulation, against five popular anti-virus products. We found that CALDERA cannot evade detection, limiting the realism of emulated attacks, even when combined with a state-of-the-art anti-detection framework. Our experiments show that Laccolith can hide its activities from all the tested anti-virus products, thus making it suitable for realistic emulations.

cs.CR↗

arXiv2024

The Power of Words: Generating PowerShell Attacks from Natural Language

As the Windows OS stands out as one of the most targeted systems, the PowerShell language has become a key tool for malicious actors and cybersecurity professionals (e.g., for penetration testing). This work explores an uncharted domain in AI code generation by automatically generating offensive PowerShell code from natural language descriptions using Neural Machine Translation (NMT). For training and evaluation purposes, we propose two novel datasets with PowerShell code samples, one with manually curated descriptions in natural language and another code-only dataset for reinforcing the training. We present an extensive evaluation of state-of-the-art NMT models and analyze the generated code both statically and dynamically. Results indicate that tuning NMT using our dataset is effective at generating offensive PowerShell code. Comparative analysis against the most widely used LLM service ChatGPT reveals the specialized strengths of our fine-tuned models.

cs.CR↗

arXiv2022

Automatic Mapping of Unstructured Cyber Threat Intelligence: An Experimental Study

Proactive approaches to security, such as adversary emulation, leverage information about threat actors and their techniques (Cyber Threat Intelligence, CTI). However, most CTI still comes in unstructured forms (i.e., natural language), such as incident reports and leaked documents. To support proactive security efforts, we present an experimental study on the automatic classification of unstructured CTI into attack techniques using machine learning (ML). We contribute with two new datasets for CTI analysis, and we evaluate several ML models, including both traditional and deep learning-based ones. We present several lessons learned about how ML can perform at this task, which classifiers perform best and under which conditions, which are the main causes of classification errors, and the challenges ahead for CTI analysis.

cs.CR↗

arXiv2021

A next-generation platform for Cyber Range-as-a-Service

In the last years, Cyber Ranges have become a widespread solution to train professionals for responding to cyber threats and attacks. Cloud computing plays a key role in this context since it enables the creation of virtual infrastructures on which Cyber Ranges are based. However, the setup and management of Cyber Ranges are expensive and time-consuming activities. In this paper, we highlight the novel features for the next-generation Cyber Range platforms. In particular, these features include the creation of a virtual clone for an actual corporate infrastructure, relieving the security managers from the setup of the training scenarios and sessions, the automatic monitoring of the participants' activities, and the emulation of their behavior.

cs.CR↗

arXiv2021

EVIL: Exploiting Software via Natural Language

Writing exploits for security assessment is a challenging task. The writer needs to master programming and obfuscation techniques to develop a successful exploit. To make the task easier, we propose an approach (EVIL) to automatically generate exploits in assembly/Python language from descriptions in natural language. The approach leverages Neural Machine Translation (NMT) techniques and a dataset that we developed for this work. We present an extensive experimental study to evaluate the feasibility of EVIL, using both automatic and manual analysis, and both at generating individual statements and entire exploits. The generated code achieved high accuracy in terms of syntactic and semantic correctness.

cs.SE↗