专注网络安全对话的8B模型,能准确回答安全问题并保持专业性。
Llama-3.1-FoundationAI-SecurityLLM-8B-Instruct Technical Report
- 基于基础安全模型微调,支持指令跟随与对话交互
- 在安全任务上优于Llama 3.1-8B-Instruct,媲美GPT-4o-mini
- 适合安全工程师日常使用,公开可用
大语言模型在多个领域表现优异,但在网络安全应用中仍受限于通用网络安全数据缺乏、表征复杂及安全与监管顾虑。为此,我们此前提出Foundation-Sec-8B,一个适用于下游任务微调的网络安全专用模型。但该模型未针对对话或指令遵循优化。本报告发布Foundation-Sec-8B-Instruct:一个专为通用网络安全对话设计的模型。基于Foundation-Sec-8B构建,融合领域知识、指令遵循、对话能力与人类偏好对齐,生成高质量、相关性强的回答。全面评估显示,Foundation-Sec-8B-Instruct在多项网络安全任务上超越Llama 3.1-8B-Instruct,指令遵循性能与之相当,且在威胁情报与指令遵循任务上可比肩GPT-4o-mini。我们期待其成为网络安全从业者日常工作中的得力助手。模型已公开发布于https://huggingface.co/fdtn-ai/Foundation-Sec-8B-Instruct。
原文摘要 · Abstract (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.
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