# Automating Privilege Escalation with Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2110.01362 · HTML: https://mlsanity.com/paper/2110.01362/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 45 references that are in the map.
- First posted on arXiv 4 Oct 2021.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Deep Reinforcement Learning for Cyber Security](https://mlsanity.com/paper/1906.05799/) (2019)
- [Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning](https://mlsanity.com/paper/1801.08917/) (2018)
- [Deep Reinforcement Learning for Green Security Games with Real-Time Information](https://mlsanity.com/paper/1811.02483/) (2018)
- [Robust Deep Reinforcement Learning for Security and Safety in Autonomous Vehicle Systems](https://mlsanity.com/paper/1805.00983/) (2018)
- [Reinforcement Learning for Autonomous Defence in Software-Defined Networking](https://mlsanity.com/paper/1808.05770/) (2018)
- [Security in Mobile Edge Caching with Reinforcement Learning](https://mlsanity.com/paper/1801.05915/) (2018)
- [The Agent Web Model -- Modelling web hacking for reinforcement learning](https://mlsanity.com/paper/2009.11274/) (2020)

## Built on by (most cited first, 5 of 5)

- [Automated Cyber Defence: A Review](https://mlsanity.com/paper/2303.04926/) (2023)
- [Frontier AI's Impact on the Cybersecurity Landscape](https://mlsanity.com/paper/2504.05408/) (2025)
- [SoK: On the Offensive Potential of AI](https://mlsanity.com/paper/2412.18442/) (2024)
- [Raij=u: Reinforcement Learning-Guided Post-Exploitation for Automating Security Assessment of Network Systems](https://mlsanity.com/paper/2309.15518/) (2023)
- [SubstratumGraphEnv: Reinforcement Learning Environment (RLE) for Modeling System Attack Paths](https://mlsanity.com/paper/2603.01340/) (2026)

## Abstract (arXiv)

AI-based defensive solutions are necessary to defend networks and information assets against intelligent automated attacks. Gathering enough realistic data for training machine learning-based defenses is a significant practical challenge. An intelligent red teaming agent capable of performing realistic attacks can alleviate this problem. However, there is little scientific evidence demonstrating the feasibility of fully automated attacks using machine learning. In this work, we exemplify the potential threat of malicious actors using deep reinforcement learning to train automated agents. We present an agent that uses a state-of-the-art reinforcement learning algorithm to perform local privilege escalation. Our results show that the autonomous agent can escalate privileges in a Windows 7 environment using a wide variety of different techniques depending on the environment configuration it encounters. Hence, our agent is usable for generating realistic attack sensor data for training and evaluating intrusion detection systems.