Meta 推出 CyberSecEval 2：評估大語言模型網路安全風險與防護能力的全面性框架
Original: CyberSecEval 2 - A Comprehensive Evaluation Framework for Cybersecurity Risks and Capabilities of Large Language Models
As large language models (LLMs) become increasingly prevalent in software development and automated workflows, their "dual-use" risks in…
Meta 推出開源安全評估框架 CyberSecEval 2，並與 Hugging Face 合作推廣。該框架旨在量化大語言模型（LLM）在網路安全領域的雙重用途風險，新增了自動化漏洞利用、惡意軟體分析及提示詞注入（Prompt Injection）等測試維度。這項工具能幫助開發者與安全研究人員，客觀評估如 Llama Guard 等安全防護模型在實際對抗中的防禦表現。
As large language models (LLMs) become increasingly prevalent in software development and automated workflows, their "dual-use" risks in the cybersecurity domain are drawing growing attention. Models can help security professionals patch vulnerabilities, but they can also potentially be used by malicious actors to write exploit code. To establish standardized security metrics, Meta has launched the CyberSecEval 2 evaluation framework, promoted through the Hugging Face platform, providing the AI community with more transparent model security data.
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