标题： 使用大语言模型的显式漏洞生成：超越对抗攻击的研究 显示英文标题

标题： Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks

摘要： 大型语言模型（LLMs）越来越多地被用作代码助手，但当明确要求生成不安全代码时，它们的行为仍知之甚少。 尽管之前的研究集中在无意的漏洞或对抗性提示技术上，但本研究考察了一个更直接的威胁场景：当提示直接或间接时，开源LLMs生成易受攻击的代码。 我们提出了一种双实验设计：（1）动态提示，系统地在结构化模板中变化漏洞类型、用户角色和直接性；（2）反向提示，从真实的易受攻击代码样本中推导提示，以评估漏洞再现的准确性。 我们使用ESBMC静态分析评估了三个开源7B参数模型（Qwen2、Mistral和Gemma），以评估漏洞的存在性和生成漏洞类型的正确性。 结果表明，所有模型经常生成易受攻击的输出，其中Qwen2的正确率最高。 用户角色显著影响成功率，学生角色比专业角色更容易产生漏洞，而直接提示略为有效。 漏洞再现与循环复杂度呈倒U型关系，在中等范围内达到峰值。 我们的发现揭示了开源模型中安全机制的局限性，特别是对于看似无害的教育请求。 显示英文摘要

摘要： Large Language Models (LLMs) are increasingly used as code assistants, yet their behavior when explicitly asked to generate insecure code remains poorly understood. While prior research has focused on unintended vulnerabilities or adversarial prompting techniques, this study examines a more direct threat scenario: open-source LLMs generating vulnerable code when prompted either directly or indirectly. We propose a dual experimental design: (1) Dynamic Prompting, which systematically varies vulnerability type, user persona, and directness across structured templates; and (2) Reverse Prompting, which derives prompts from real vulnerable code samples to assess vulnerability reproduction accuracy. We evaluate three open-source 7B-parameter models (Qwen2, Mistral, and Gemma) using ESBMC static analysis to assess both the presence of vulnerabilities and the correctness of the generated vulnerability type. Results show all models frequently produce vulnerable outputs, with Qwen2 achieving highest correctness rates. User persona significantly affects success, where student personas achieved higher vulnerability rates than professional roles, while direct prompts were marginally more effective. Vulnerability reproduction followed an inverted-U pattern with cyclomatic complexity, peaking at moderate ranges. Our findings expose limitations of safety mechanisms in open-source models, particularly for seemingly benign educational requests.