3 citations · 8 across the 12 of their papers we have counts for
12 papers
Towards LLM-Enhanced Android Taint Analysis
Nicholas Miazzo, Marco Alecci, Jordan Samhi +2
Taint analysis is a fundamental technique for detecting sensitive data leaks in Android apps. However, traditional static tools, such as FlowDroid, still face well-known challenges…
Old Tricks, New Models: How Simple Image Transformations Break Modern AI-based Content Moderation
Marco Alecci, Francesco Marchiori, Iyiola Emmanuel Olatunji +2
While automated content-moderation systems have become essential for screening harmful content at scale, conventional task-specific classifiers often provide limited policy cov- er…
Evaluating LLMs for Obfuscation Detection and Classification in Android Apps
Luca Ferrari, Marco Alecci, Jordan Samhi +4
Android applications (apps) developers increasingly rely on code obfuscation techniques to hinder reverse engineering and protect intellectual property. However, obfuscation also r…
Exploring Hidden Geographic Disparities in Android Apps
M. Alecci, P. Jiménez, J. Samhi +2
While mobile app evolution has been widely studied, geographical variation in app behavior remains largely unexplored. This paper presents a large-scale study of location-based And…
Evaluating Large Language Models in detecting Secrets in Android Apps
Marco Alecci, Jordan Samhi, Tegawendé F. Bissyandé +1
Mobile apps often embed authentication secrets, such as API keys, tokens, and client IDs, to integrate with cloud services. However, developers often hardcode these credentials int…
MalLoc: Toward Fine-grained Android Malicious Payload Localization via LLMs
Tiezhu Sun, Marco Alecci, Aleksandr Pilgun +5
The rapid evolution of Android malware poses significant challenges to the maintenance and security of mobile applications (apps). Traditional detection techniques often struggle t…
DUMB and DUMBer: Is Adversarial Training Worth It in the Real World?
Francesco Marchiori, Marco Alecci, Luca Pajola +1
Adversarial examples are small and often imperceptible perturbations crafted to fool machine learning models. These attacks seriously threaten the reliability of deep neural networ…
Revisiting Android App Categorization
Marco Alecci, Jordan Samhi, Tegawendé F. Bissyandé +1
Numerous tools rely on automatic categorization of Android apps as part of their methodology. However, incorrect categorization can lead to inaccurate outcomes, such as a malware d…
AndroLibZoo: A Reliable Dataset of Libraries Based on Software Dependency Analysis
Jordan Samhi, Tegawendé F. Bissyandé, Jacques Klein
Android app developers extensively employ code reuse, integrating many third-party libraries into their apps. While such integration is practical for developers, it can be challeng…
Your Attack Is Too DUMB: Formalizing Attacker Scenarios for Adversarial Transferability
Marco Alecci, Mauro Conti, Francesco Marchiori +2
Evasion attacks are a threat to machine learning models, where adversaries attempt to affect classifiers by injecting malicious samples. An alarming side-effect of evasion attacks…
Beware of Pickpockets: A Practical Attack against Blocking Cards
Marco Alecci, Luca Attanasio, Alessandro Brighente +4
Today, we rely on contactless smart cards to perform several critical operations (e.g., payments and accessing buildings). Attacking smart cards can have severe consequences, such…
Mascara: A Novel Attack Leveraging Android Virtualization
Marco Alecci, Riccardo Cestaro, Mauro Conti +2
Android virtualization enables an app to create a virtual environment, in which other apps can run. Originally designed to overcome the limitations of mobile apps dimensions, malic…