---
title: "LLMs as Hackers: Autonomous Linux Privilege Escalation Attacks (arXiv:2310.11409) | Papers With Code Cyber"
description: "Penetration-testing is crucial for identifying system vulnerabilities, with privilege-escalation being a critical subtask to gain elevated access to protected resources…"
canonical: "https://paperswithcode-cyber.com/paper/2310.11409"
type: "paper"
---

# LLMs as Hackers: Autonomous Linux Privilege Escalation Attacks

arXiv:2310\.11409 · 2023-10-17

Authors: Happe, Andreas, Kaplan, Aaron, Cito, Juergen

## Abstract

Penetration\-testing is crucial for identifying system vulnerabilities, with privilege\-escalation being a critical subtask to gain elevated access to protected resources\. Language Models \(LLMs\) presents new avenues for automating these security practices by emulating human behavior\. However, a comprehensive understanding of LLMs' efficacy and limitations in performing autonomous Linux privilege\-escalation attacks remains under\-explored\. To address this gap, we introduce hackingBuddyGPT, a fully automated LLM\-driven prototype designed for autonomous Linux privilege\-escalation\. We curated a novel, publicly available Linux privilege\-escalation benchmark, enabling controlled and reproducible evaluation\. Our empirical analysis assesses the quantitative success rates and qualitative operational behaviors of various LLMs \-\- GPT\-3\.5\-Turbo, GPT\-4\-Turbo, and Llama3 \-\- against baselines of human professional pen\-testers and traditional automated tools\. We investigate the impact of context management strategies, different context sizes, and various high\-level guidance mechanisms on LLM performance\. Results show that GPT\-4\-Turbo demonstrates high efficacy, successfully exploiting 33\-83% of vulnerabilities, a performance comparable to human pen\-testers \(75%\)\. In contrast, local models like Llama3 exhibited limited success \(0\-33%\), and GPT\-3\.5\-Turbo achieved moderate rates \(16\-50%\)\. We show that both high\-level guidance and state\-management through LLM\-driven reflection significantly boost LLM success rates\. Qualitative analysis reveals both LLMs' strengths and weaknesses in generating valid commands and highlights challenges in common\-sense reasoning, error handling, and multi\-step exploitation, particularly with temporal dependencies\. Cost analysis indicates that GPT\-4\-Turbo can achieve human\-comparable performance at competitive costs, especially with optimized context management\.

## Tasks

- [Exploit construction and validation](https://paperswithcode-cyber.com/tasks/cyberintel-exploit-development)
- [Penetration testing](https://paperswithcode-cyber.com/tasks/cyberintel-penetration-testing)

## Code repositories

- [ipa\-lab/hackingBuddyGPT](https://github.com/ipa-lab/hackingBuddyGPT)

## Paper resources

- [Abstract page](https://arxiv.org/abs/2310.11409v7)
- [PDF](https://arxiv.org/pdf/2310.11409v7.pdf)