Local LLMs now match cloud models for Linux privilege escalation attacks

Researchers have explored methods to improve the effectiveness of locally hosted Large Language Models (LLMs) for Linux privilege escalation attacks. They analyzed failure modes of open-weight models and tested five interventions, including chain-of-thought prompting and retrieval-augmented generation, integrated into a tool called hackingBuddyGPT. The study found that these enhancements allowed models like Llama3.1 70B to achieve an 83% exploit rate, matching or exceeding cloud-based models like GPT-4o, with reflection-based treatments proving most impactful. AI

影响 Enhances local LLM capabilities for security research, potentially improving offensive and defensive cybersecurity tooling.

排序理由 Academic paper detailing empirical study and interventions for LLM capabilities.

在 arXiv cs.AI 阅读 →

• arXiv
• GPT-4o
• hackingBuddyGPT
• Llama3.1 70B
• Llama3.1 8B
• Qwen2.5 7B

• 论文
• 安全

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