Luca Verderame

Despite decades of research, the automatic detection of vulnerabilities in mobile apps remains an open challenge. Among the possible solutions, SAST tools uncover source or compiled code security flaws without needing the app to be executed and tested in a controlled environment. However, SAST tools share several limitations, such as the detection of narrowed vulnerability classes, lack of updates, and limited resiliency to obfuscation techniques. This paper presents SEBASTiAn, a black-box automatic static analysis tool for security vetting Android and iOS apps. It relies on a modular approach to cope with new vulnerabilities.

IoT repackaging refers to an attack devoted to tampering with a legitimate firmware package by modifying its content (e.g., injecting some malicious code) and re-distributing it in the wild. In such a scenario, the firmware delivery and update processes play a central role in ensuring firmware integrity. Unfortunately, several existing solutions lack proper integrity verification, exposing firmware to repackaging attacks. If this is not the case, they still require an external trust anchor (e.g., signing keys or secure storage technologies), which could limit their adoption in resource-constrained environments. In addition, state-of-the-art frameworks do not cope with the entire firmware production and delivery process, thereby failing to protect the content generated by the firmware producers through the whole supply chain. To mitigate such a problem, in this paper, we introduce PARIOT, a novel self-protecting scheme for IoT that allows the injection of integrity checks, called anti-tampering (AT) controls, directly into the firmware. The AT controls enable the runtime detection of repackaging attempts without needing external trust anchors or computationally expensive systems. PARIOT can be adopted on top of existing state-of-the-art solutions ensuring the widest compatibility with current IoT ecosystems and update frameworks. Also, we have implemented this scheme into PARIOTIC, a prototype to automatically protect C/C++ IoT firmware. The evaluation phase of 50 real-world firmware samples demonstrated the feasibility of the proposed methodology and its robustness against practical repackaging attacks without altering the firmware behavior or severe overheads.

This paper focuses on the role of privileges in automation workflows within modern software development practices, which heavily rely on DevOps principles. Automation workflows, which are sets of automated software management processes, have become essential to software development and are integrated into software management platforms such as GitHub, GitLab, and BitBucket. However, privileges are crucial in ensuring the security and integrity of the software development process. This paper aims to identify the phases in which privileges are involved in automation workflows and analyze how these platforms handle the privilege life cycle in automation workflows to provide a better understanding of their security implications. The security discussion highlighted in this analysis aims to stimulate solutions and further research.

Mobile applications (hereafter, apps) collect a plethora of information regarding the user behavior and his device through third-party analytics libraries. However, the collection and usage of such data raised several privacy concerns, mainly because the end-user - i.e., the actual owner of the data - is out of the loop in this collection process. Also, the existing privacy-enhanced solutions that emerged in the last years follow an "all or nothing" approach, leaving the user the sole option to accept or completely deny the access to privacy-related data. This work has the two-fold objective of assessing the privacy implications on the usage of analytics libraries in mobile apps and proposing a data anonymization methodology that enables a trade-off between the utility and privacy of the collected data and gives the user complete control over the sharing process. To achieve that, we present an empirical privacy assessment on the analytics libraries contained in the 4500 most-used Android apps of the Google Play Store between November 2020 and January 2021. Then, we propose an empowered anonymization methodology, based on MobHide, that gives the end-user complete control over the collection and anonymization process. Finally, we empirically demonstrate the applicability and effectiveness of such anonymization methodology thanks to HideDroid, a fully-fledged anonymization app for the Android ecosystem.

Mobile applications (hereafter, apps) collect a plethora of information regarding the user behavior and his device through third-party analytics libraries. However, the collection and usage of such data raised several privacy concerns, mainly because the end-user - i.e., the actual owner of the data - is out of the loop in this collection process. Also, the existing privacy-enhanced solutions that emerged in the last years follow an "all or nothing" approach, leaving the user the sole option to accept or completely deny access to privacy-related data. This work has the two-fold objective of assessing the privacy impact of mobile analytics libraries and proposing a data anonymization methodology that offers a trade-off between the utility and privacy of the collected data and enables complete control over the sharing process. To achieve that, we present an empirical privacy assessment on the analytics libraries used in the 4500 most-used Android apps of the Google Play Store in late 2020. Then, we propose an empowered anonymization methodology, based on MobHide, that gives the end-user complete control over the collection and anonymization process. Finally, we empirically demonstrate the applicability and effectiveness of our solution thanks to HideDroid, a fully-fledged anonymization app for the Android ecosystem.