Triton is a binary analysis framework which is currently mainly focused around the symbolic execution research area. Triton is basically a C++11 library and exposes a C++ API as well as Python bindings. Through this blog post we will see how Triton (build 1189) manages and deals with instruction semantics, then, what kind of information you can get back after processing an instruction into Triton.
Code coverage is mainly used in the vulnerability research area. The goal is to generate inputs which will reach different parts of the program's code. Then, if an input makes the program crash, we check if the crash can be exploited or not. A lot of methods exist to perform code coverage - like random testing or mutation generation - but in this short blog post, we will focus on code coverage using a dynamic symbolic execution (DSE) and explain why it's not a trivial task.
Triton is a Pin-based concolic execution framework which was released on live at SSTIC 2015 and sponsored by Quarkslab. Triton provides components like a taint engine, a dynamic symbolic execution engine, a snapshot engine, translation of x64 instruction into SMT2-LIB, a Z3 interface to solve constraints and Python bindings. Based on these components, you can build tools for automated reverse engineering or vulnerability research. This blog post will describe Triton under the hood, explain how to use it and show what kind of things we can build with it. Note that this blog post is a kind of reference where each chapter can be read separately.