At the request of the Open Source Technology Improvement Fund (OSTIF), Quarkslab performed a security audit of Cortex, evaluating the security of its multi-tenant design and the mechanisms protecting ...

The Open Source Technology Improvement Fund (OSTIF) commissioned Quarkslab to extend the BOLT-based static binary analyser in LLVM to support additional compiler flags for security hardening. This wor...

The Scala team has partnered with the Open Source Technology Improvement Fund (OSTIF) to conduct its first security audit. This initiative aims to identify potential vulnerabilities through static and...

This article presents the structure of the Independent Guest Virtual Machine (IGVM) file format, a binary file designed to define and securely launch the initial state of a virtual machine. It bundles...

The OSTIF collaborated with Quarkslab to conduct a security audit of Paramiko, a pure-Python implementation of SSHv2 that provides both client- and server-side functionality. Given the sensitivity and...

The Open Source Technology Improvement Fund, Inc. mandated Quarkslab to perform the first public security audit of Bitcoin core, the reference open-source implementation of the Bitcoin decentralized p...

As part of our application to Graduate, KubeVirt has a security audit performed by a third-party, organised through the CNCF and OSTIF.

This article details two bugs discovered in the NVIDIA Linux Open GPU Kernel Modules and demonstrates how they can be exploited. The bugs can be triggered by an attacker controlling a local unprivileg...

Protecting sensitive program content is a critical concern in various situations, ranging from legitimate use cases to unethical contexts. Obfuscation is one of the most used techniques to ensure such a protection. Consequently, attackers must first detect and characterize obfuscation before launching any attack against it. This paper investigates the problem of function-level obfuscation detection using graph-based approaches, comparing algorithms, from classical baselines to advanced techniques like Graph Neural Networks (GNN), on different feature choices. We consider various obfuscation types and obfuscators, resulting in two complex datasets. Our findings demonstrate that GNNs need meaningful features that capture aspects of function semantics to outperform baselines. Our approach shows satisfactory results, especially in a challenging 11-class classification task and in two practical binary analysis examples. It highlights how much obfuscation and optimization are intertwined in binary code and that a better comprehension of these two principles are fundamental in order to obtain better detection results.

The Open Source Technology Improvement Fund, Inc., engaged with Quarkslab to perform a security audit of the code snippets in the English version of PHP documentation, focused on some specific pages.