Projects

I like to be involved in various projects across different fields. Here you can find my research and hackathon projects spanning Automated Testing, Cybersecurity, AI, DeFi, and blockchain technologies. At the end, you will also find mobile apps I have developed and released few years ago.

Automated Testing of GraphQL APIs (ATG)
GraphQL

Automated Testing of GraphQL APIs (ATG)

This project focuses on advancing automated security testing of GraphQL APIs through innovative research and tooling. We have developed and released two major research contributions that enhance GraphQL testing capabilities through different approaches. Research Contributions Wendigo: Deep Reinforcement Learning for Denial-of-Service Query Discovery in GraphQL Wendigo is a black-box Deep Reinforcement Learning approach that discovers Denial-of-Service exploitable queries against GraphQL applications. Using only the GraphQL schema, Wendigo can discover queries capable of performing DoS attacks with just two requests per hour, as opposed to the high volume required by traditional attacks. BenGQL: An Extensible Benchmarking Framework for Automated GraphQL Testing (ASE 2025) BenGQL is an extensible benchmarking framework containing 23 representative open-source GraphQL server applications. This framework enables rigorous evaluation of automated testing tools across different GraphQL engines and schema complexities. Ongoing Research We are currently exploring advanced AI techniques, including Large Language Models, to further enhance GraphQL security testing capabilities. This research aims to develop more sophisticated and context-aware testing approaches. Collaboration Interested in collaborating on GraphQL security research? We welcome partnerships with researchers and industry professionals working on API security, automated testing, and AI-driven security tools.

Eulibra
PolyMirror.AI
LibAFLstar
TonAIStark
Flare-FL
Proteus
Fuzzing Matter(s)
PECS - Privacy Enrooted Car Systems
Privacy

PECS - Privacy Enrooted Car Systems

People’s privacy control over the personal data that they generate and consume while they drive modern cars is extremely weak at present. There is historical as well as recent evidence that car brands harvest a variety of personal data from drivers and, arguably, full compliance of their processing with the European General Data Protection Regulation is questionable. PECS revolutionises modern car ecosystems for what concerns the processing of personal data. It does so by advancing, tailoring to the specific domain and, ultimately, combining together both soft and hard privacy measures. The project raises drivers’ soft privacy through the PECS interface for static and dynamic control of personal data, so that drivers can decide what to share and with whom and when, as well as follow and control the flows of data at service run time by means of multy-sensory media techniques. Hard privacy thrives in the project through a combination of obfuscation techniques including Federated Analytics, Secure Multi Party Computation and Pseudonymisation, so that drivers are enabled to keep their personal data opaque to anyone from the outset. All developments proceed from the established academic laboratories of UNICT-UNIMORE, then are demonstrated in the operational environment of MASA-UNIMORE, reaching TRL7. The PECS results stem from the open-source, open-Internet approaches, hence bear huge technical, societal and industrial impacts, bringing Europe at the forefront of data protection, at least in the automotive domain. PECS also brings forward a whole new range of business opportunities such as various forms of software support for its technologies, and of renewed car services leveraging privacy-by-design-by-default. Finally, PECS provides the necessary grounds favouring the inception of a new breed of services that would be naturally enrooted on drivers’ sensitive data such as sexual, religious and political orientations, e.g. apps for dating, praying and debate on political topics.

The Water Code
Fantamaniaci (iOS)
Dragomanni Apartments Website
Do a Shot (iOS)
Distantia