Build a Rop Chain Using a Reinforcement Learning
il y a 14 heures
Quarkslab is a privately held company founded in 2011 with currently more than 100 employees. The company focuses on vulnerability research, reverse engineering and design of security solutions and has developed leading software in the area of threat detection and code/data protection.
Quarkslab provides a broad set of products and services based on deep knowledge in the area of Cybersecurity.
**Job description**:
Return Oriented Programming consists in chaining pieces of codes that already reside in the executable memory to perform what the attackers wants. The aim of this intership is to explore the use of Deep Reinforcement Learning techniques guided by a dataflow graph to generate a program of equivalent semantics.
**What you will do**
You will create a prototype on x86_64 to evaluate the feasibility of using Reinforcement Learning techniques to automatically build a ROP chain from a reduce set of gadgets:
- Study the state of the art
- Generate a dataset of functions to translate
- Setup a sample library with a variety of gadgets
- Create a test bench to execute a ROP chain
- Extract the dataflow graphs from binary code
- Design the action space and reward function
- Integrate a Reinforcement Learning algorithm to drive the chaining
- Experiment the use of dataflow graphs as an observation for the RL agent
You will be aided by Machine Learning and Cyber-security experts.
Assignment
Supposing you want to create a RL environment for the μRTS AI competition using a GNN approach:
- How would you design the observation and action spaces?
- What reward function could you try?
- Which agent algorithm could be well suited?
No code will be asked, but coding skills will be evaluated during the interview.
**Profile**:
- Appetency to python development
- Some idea and interest about Machine Learning algorithms and Deep Learning internals.
- Notions of hacking and low-level stuff (assembly, ASLR...)
- A bit of knowledge in Reinforcement Learning and/or Message Passing Neural Network would be great
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