Unverified75% confidenceFactTime unknown
研究表明LLM生成的代码在SQL注入、XSS防护、密钥硬编码等方面的错误率显著高于有经验的人类开发者
1
Sources
75%
Confidence
Long-term
Relevance
5/31/2026
First Seen
Sources
Related Claims
UnverifiedResearch shows that the probability of common vulnerabilities such as SQL injection, XSS, and insecure dependency references in code generated by large language models is significantly higher than that of experienced human developers.85% similarUnverifiedLLM在生成多线程代码时因无法对执行时序建立符号化推理模型，竞态条件缺陷发生率显著高于经验丰富的人类开发者75% similarUnverifiedLLM的训练数据包含大量Web开发代码，不加限制时会自然倾向于引入npm包，而这在小程序沙箱环境中会直接导致运行失败71% similarUnverifiedDevelopers commonly use techniques such as OLLVM obfuscation, anti-debugging detection, and string encryption to increase the difficulty of analyzing .so files.71% similarUnverified据GitHub的研究数据，AI生成的代码在安全漏洞密度上显著高于人工编写代码，主要集中在SQL注入、越权访问等漏洞类型70% similar
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