Enhancing Linux Privilege Escalation Attack Capabilities of Local LLM Agents

• URL: http://arxiv.org/abs/2604.27143v1
• Date: Wed, 29 Apr 2026 19:54:27 GMT
• Title: Enhancing Linux Privilege Escalation Attack Capabilities of Local LLM Agents
• Abstract summary: Small open-weight models perform poorly on automated Linux privilege escalation.<n>We present a systematic empirical study of whether targeted system-level and prompting interventions can bridge this performance gap.
• Score: 1.1677624591989955
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Recent research has demonstrated the potential of Large Language Models (LLMs) for autonomous penetration testing, particularly when using cloud-based restricted-weight models. However, reliance on such models introduces security, privacy, and sovereignty concerns, motivating the use of locally hosted open-weight alternatives. Prior work shows that small open-weight models perform poorly on automated Linux privilege escalation, limiting their practical applicability. In this paper, we present a systematic empirical study of whether targeted system-level and prompting interventions can bridge this performance gap. We analyze failure modes of open-weight models in autonomous privilege escalation, map them to established enhancement techniques, and evaluate five concrete interventions (chain-of-thought prompting, retrieval-augmented generation, structured prompts, history compression, and reflective analysis) implemented as extensions to hackingBuddyGPT. Our results show that open-weight models can match or outperform cloud-based baselines such as GPT-4o. With our treatments enabled, Llama3.1 70B exploits 83% of tested vulnerabilities, while smaller models including Llama3.1 8B and Qwen2.5 7B achieve 67% when using guidance. A full-factorial ablation study over all treatment combinations reveals that reflection-based treatments contribute most, while also identifying vulnerability discovery as a remaining bottleneck for local models.

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