# Enhancing Linux Privilege Escalation Attack Capabilities of Local LLM Agents

arXiv: https://arxiv.org/abs/2604.27143 · HTML: https://mlsanity.com/paper/2604.27143/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 27 of its 42 references that are in the map.
- First posted on arXiv 29 Apr 2026.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 27 of 27)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models](https://mlsanity.com/paper/2305.04091/) (2023)
- [Zephyr: Direct Distillation of LM Alignment](https://mlsanity.com/paper/2310.16944/) (2023)
- [To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning](https://mlsanity.com/paper/2409.12183/) (2024)
- [A Survey on Large Language Model Acceleration based on KV Cache Management](https://mlsanity.com/paper/2412.19442/) (2024)
- [A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness](https://mlsanity.com/paper/2411.03350/) (2024)
- [PentestGPT: An LLM-empowered Automatic Penetration Testing Tool](https://mlsanity.com/paper/2308.06782/) (2023)
- [AutoAttacker: A Large Language Model Guided System to Implement Automatic Cyber-attacks](https://mlsanity.com/paper/2403.01038/) (2024)
- [Getting pwn'd by AI: Penetration Testing with Large Language Models](https://mlsanity.com/paper/2308.00121/) (2023)
- [Fine-Tuned 'Small' LLMs (Still) Significantly Outperform Zero-Shot Generative AI Models in Text Classification](https://mlsanity.com/paper/2406.08660/) (2024)
- [Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models](https://mlsanity.com/paper/2310.04027/) (2023)
- [RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models](https://mlsanity.com/paper/2407.05131/) (2024)
- [Finetuning Large Language Models for Vulnerability Detection](https://mlsanity.com/paper/2401.17010/) (2024)
- [PenHeal: A Two-Stage LLM Framework for Automated Pentesting and Optimal Remediation](https://mlsanity.com/paper/2407.17788/) (2024)
- [LLMs as Hackers: Autonomous Linux Privilege Escalation Attacks](https://mlsanity.com/paper/2310.11409/) (2023)
- [Out of the Cage: How Stochastic Parrots Win in Cyber Security Environments](https://mlsanity.com/paper/2308.12086/) (2023)
- [Can LLMs Hack Enterprise Networks? Autonomous Assumed Breach Penetration-Testing Active Directory Networks](https://mlsanity.com/paper/2502.04227/) (2025)
- [CaseGPT: a case reasoning framework based on language models and retrieval-augmented generation](https://mlsanity.com/paper/2407.07913/) (2024)
- [CIPHER: Cybersecurity Intelligent Penetration-testing Helper for Ethical Researcher](https://mlsanity.com/paper/2408.11650/) (2024)
- [Got Root? A Linux Priv-Esc Benchmark](https://mlsanity.com/paper/2405.02106/) (2024)

## Built on by (most cited first, 1 of 1)

- [The Ethics of Autonomous AI Agents for Offensive Security](https://mlsanity.com/paper/2607.20255/) (2026)

## Abstract (arXiv)

Cloud-based Large Language Models (LLMs) can perform autonomous penetration-testing sub-tasks such as Linux privilege escalation, but raise security, privacy, and sovereignty concerns. Locally hosted open-weight models avoid these issues, yet prior work reports that small open-weight models succeed on only 8-16% of standardized privilege-escalation tasks, far below frontier cloud models. This paper is an empirical study of why small models fail at this task and which engineering techniques close the gap. From execution traces we distill six recurring failure modes, map each to an established enhancement technique, and evaluate five (chain-of-thought prompting, retrieval-augmented generation, structured prompting, history compression, and reflective analysis) as reproducible extensions to the open-source hackingBuddyGPT framework. Under a single shared harness and matched conditions, the set of techniques we evaluate raise two SLMs (Llama3.1 8B, Qwen2.5 7B) from 8% to 67% with guidance, matching guided GPT-4o. A larger open-weight reference model (Llama3.1 70B) reaches 83%. A full-factorial ablation shows that reflection-based techniques contribute most and reveals vulnerability discovery, not exploitation, as the main constraint for local models. We report these as transferable lessons for building reliable local offensive agents as well as to inform defenders.