Llama-3.1-基礎AI-安全LLM-8B-指令技術報告

摘要

大型語言模型（LLMs）在多個領域展現了顯著的成功，然而其在網絡安全應用中的整合仍受限於通用網絡安全數據的缺乏、表示複雜性以及安全與監管問題。為彌補這一差距，我們先前推出了Foundation-Sec-8B，這是一款專注於網絡安全的LLM，適合在下游任務中進行微調。然而，該模型並非為聊天式互動或指令遵循而設計。在本報告中，我們發布了Foundation-Sec-8B-Instruct：一款專門訓練用於通用網絡安全對話的模型。基於Foundation-Sec-8B構建，它結合了領域特定知識、指令遵循能力、對話能力以及與人類偏好的對齊，以產生高質量且相關的回應。全面評估顯示，Foundation-Sec-8B-Instruct在一系列網絡安全任務上優於Llama 3.1-8B-Instruct，同時在指令遵循性能上與之匹敵。在網絡威脅情報和指令遵循任務上，它也能與GPT-4o-mini競爭。我們預見Foundation-Sec-8B-Instruct將成為網絡安全專業人員日常工作中不可或缺的助手。我們已將該模型公開發布於https://huggingface.co/fdtn-ai/Foundation-Sec-8B-Instruct。

English

Large language models (LLMs) have shown remarkable success across many domains, yet their integration into cybersecurity applications remains limited due to a lack of general-purpose cybersecurity data, representational complexity, and safety and regulatory concerns. To address this gap, we previously introduced Foundation-Sec-8B, a cybersecurity-focused LLM suitable for fine-tuning on downstream tasks. That model, however, was not designed for chat-style interactions or instruction-following. In this report, we release Foundation-Sec-8B-Instruct: a model specifically trained for general-purpose cybersecurity dialogue. Built on Foundation-Sec-8B, it combines domain-specific knowledge with instruction-following, conversational capabilities, and alignment with human preferences to produce high-quality, relevant responses. Comprehensive evaluations show that Foundation-Sec-8B-Instruct outperforms Llama 3.1-8B-Instruct on a range of cybersecurity tasks while matching its instruction-following performance. It is also competitive with GPT-4o-mini on cyber threat intelligence and instruction-following tasks. We envision Foundation-Sec-8B-Instruct becoming an indispensable assistant in the daily workflows of cybersecurity professionals. We release the model publicly at https://huggingface.co/fdtn-ai/Foundation-Sec-8B-Instruct.

Llama-3.1-基礎AI-安全LLM-8B-指令技術報告

Llama-3.1-FoundationAI-SecurityLLM-8B-Instruct Technical Report

摘要

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