In this blog post we present PASTIS, a Python framework for ensemble fuzzing, developed at Quarkslab.

We present TritonDSE, a new tool by Quarkslab. TritonDSE is a Python library, built on top of Triton, that provides easy and customizable Dynamic Symbolic Execution capabilities for binary programs.

The Ethereum Foundation mandated Quarkslab to perform an audit of the herumi libraries. They provide an API to perform BLS signatures, one of the core components of the new iteration of the Ethereum blockchain, named Ethereum 2.0. While reviewing the architecture of these libraries, their back ends and the adherence with the ongoing RFCs to standardize BLS signature usage, we found some issues primarily regarding their design. Although these are not considered critical, they impact the overall reliability of the libraries. We provide recommendations to improve the design of the libraries, the readability of the code and the usability of both projects.

This blog post is a follow-up on the announcement of Triton v0.8, where we explain how we added support for ARMv7 and provide a guideline for adding new architectures.

Quarkslab's team performed a cryptographic and security assessment of the Monero Research Lab’s new Proof-of-Work algorithm, called RandomX [1]. RandomX is a proof-of-work algorithm that is optimized for general-purpose CPUs. RandomX uses random code execution together with several memory-hard techniques to minimize the efficiency advantage of specialized hardware. We only found minor inconsistencies and formulated a few recommendations. These recommendations are mainly relevant when using alternative configurations but they are of less importance with the current configuration and usage of RandomX. The full report of the assessment can be found at the following address: [2]