Leonardo Regano

Daniele Canavese, Leonardo Regano, Bjorn De Sutter et al.

This paper introduces a novel approach for the automated selection of software protections to mitigate MATE risks against critical assets within software applications. We formalize the key elements involved in protection decision-making - including code artifacts, assets, security requirements, attacks, and software protections - and frame the protection process through a game-theoretic model. In this model, a defender strategically applies protections to various code artifacts of a target application, anticipating repeated attack attempts by adversaries against the confidentiality and integrity of the application's assets. The selection of the optimal defense maximizes resistance to attacks while ensuring the application remains usable by constraining the overhead introduced by protections. The game is solved through a heuristic based on a mini-max depth-first exploration strategy, augmented with dynamic programming optimizations for improved efficiency. Central to our formulation is the introduction of the Software Protection Index, an original contribution that extends existing notions of potency and resilience by evaluating protection effectiveness against attack paths using software metrics and expert assessments. We validate our approach through a proof-of-concept implementation and expert evaluations, demonstrating that automated software protection is a practical and effective solution for risk mitigation in software.

Alessandro Sanna, Waldo Verstraete, Leonardo Regano et al.

Evidence on the effectiveness of Man-At-The-End (MATE) software protections, such as code obfuscation, has mainly come from limited empirical research. Recently, however, an automatable method was proposed to obtain statistical models of the required effort to attack (protected) software. The proposed method was sketched for a number of attack strategies but not instantiated, evaluated, or validated for those that require human interaction with the attacked software. In this paper, we present a full instantiation of the method to obtain statistical effort models for game resource localisation attacks, which represent a major step towards creating game cheats, a prime example of MATE attacks. We discuss in detail all relevant aspects of our instantiation and the results obtained for two game use cases. Our results confirm the feasibility of the proposed method and its utility for decision support for users of software protection tools. These results open up a new avenue for obtaining models of the impact of software protections on reverse engineering attacks, which will scale much better than empirical research involving human participants.

Silvia Lucia Sanna, Leonardo Regano, Davide Maiorca et al.

Nowadays, many tools are used to facilitate forensic tasks about data extraction and data analysis. In particular, some tools leverage Artificial Intelligence (AI) to automatically label examined data into specific categories (\ie, drugs, weapons, nudity). However, this raises a serious concern about the robustness of the employed AI algorithms against adversarial attacks. Indeed, some people may need to hide specific data to AI-based digital forensics tools, thus manipulating the content so that the AI system does not recognize the offensive/prohibited content and marks it at as suspicious to the analyst. This could be seen as an anti-forensics attack scenario. For this reason, we analyzed two of the most important forensics tools employing AI for data classification: Magnet AI, used by Magnet Axiom, and Excire Photo AI, used by X-Ways Forensics. We made preliminary tests using about $200$ images, other $100$ sent in $3$ chats about pornography and teenage nudity, drugs and weapons to understand how the tools label them. Moreover, we loaded some deepfake images (images generated by AI forging real ones) of some actors to understand if they would be classified in the same category as the original images. From our preliminary study, we saw that the AI algorithm is not robust enough, as we expected since these topics are still open research problems. For example, some sexual images were not categorized as nudity, and some deepfakes were categorized as the same real person, while the human eye can see the clear nudity image or catch the difference between the deepfakes. Building on these results and other state-of-the-art works, we provide some suggestions for improving how digital forensics analysis tool leverage AI and their robustness against adversarial attacks or different scenarios than the trained one.

Francesco Pagano, Lorenzo Pisu, Leonardo Regano et al.

Code obfuscation is widely adopted in modern software development to protect intellectual property and hinder reverse engineering, but it also provides attackers with a powerful means to conceal malicious logic inside otherwise legitimate JavaScript code. In a software supply chain where a single compromised package can affect thousands of applications, this raises a critical question: how robust are the Static Application Security Testing (SAST) tools that CI/CD pipelines rely on as automated security gatekeepers? This paper answers that question by empirically quantifying the impact of JavaScript obfuscation on state-of-practice SAST. We define a realistic supply-chain threat model in which an adversary injects vulnerable code and iteratively obfuscates it until the pipeline reports a clean scan. To measure the resulting degradation, we introduce the Vulnerability Detection Loss (VDL) metric and conduct a two-phase study. First, we analyze 16 vulnerable-by-design Node.js web applications from the OWASP directory; second, we extend the analysis to 260 in-the-wild JavaScript/Node.js projects from GitHub. Across both datasets, we apply eight semantics-preserving obfuscation techniques and their combinations and evaluate two representative SAST tools, Njsscan and Bearer. Even a single obfuscation technique typically suppresses most baseline findings, including high-severity issues, while stacking techniques yield near-total evasion, with VDL often approaching 100%. Our results show that current JavaScript SAST is fundamentally not robust against commonplace obfuscations and that "clean" reports on obfuscated code may offer only a false sense of security. Finally, we discuss practical mitigation guidelines and directions for obfuscation-aware analysis.

Daniele Canavese, Leonardo Regano, Cataldo Basile et al.

The last years have seen an increase of Man-at-the-End (MATE) attacks against software applications, both in number and severity. However, MATE software protections are dominated by fuzzy concepts and techniques, with security-through-obscurity omnipresent in the field. This paper presents a rationale for adopting and standardizing the protection of software as a risk management process according to the NIST SP800-39 approach. We examine the relevant aspects of formalizing and automating the activities in this process in the context of MATE software protection. We highlight the open issues that the research community still has to address. We discuss the benefits that such an approach can bring to all stakeholders. In addition, we present a Proof of Concept (PoC) of a decision support system that automates many activities in the risk analysis methodology towards the protection of software applications. Despite still being a prototype, the PoC validation with industry experts indicated that several aspects of the proposed risk management process can already be formalized and automated with our existing toolbox, and that it can actually assist decision making in industrially relevant settings

Alessio Viticchié, Leonardo Regano, Marco Torchiano et al.

Obfuscation techniques are a general category of software protections widely adopted to prevent malicious tampering of the code by making applications more difficult to understand and thus harder to modify. Obfuscation techniques are divided in code and data obfuscation, depending on the protected asset. While preliminary empirical studies have been conducted to determine the impact of code obfuscation, our work aims at assessing the effectiveness and efficiency in preventing attacks of a specific data obfuscation technique - VarMerge. We conducted an experiment with student participants performing two attack tasks on clear and obfuscated versions of two applications written in C. The experiment showed a significant effect of data obfuscation on both the time required to complete and the successful attack efficiency. An application with VarMerge reduces by six times the number of successful attacks per unit of time. This outcome provides a practical clue that can be used when applying software protections based on data obfuscation.