Empirical study of real-world vibe-coded apps finds recurring vulnerabilities like placeholder logic and secret exposure caused by AI agent limitations such as memory loss and insufficient security knowledge.
You still have to study on the security of LLM generated code,
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MA-CoT prompting reduces security findings in LLM-generated code by 57.6% on a 200-task dataset and 94.5% on LLMSecEval across C, Java, and Python, outperforming vanilla, zero-shot, and standard CoT strategies.
citing papers explorer
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Understanding the (In)Security of Vibe-Coded Applications
Empirical study of real-world vibe-coded apps finds recurring vulnerabilities like placeholder logic and secret exposure caused by AI agent limitations such as memory loss and insufficient security knowledge.
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Enhancing Reliability in LLM-Based Secure Code Generation
MA-CoT prompting reduces security findings in LLM-generated code by 57.6% on a 200-task dataset and 94.5% on LLMSecEval across C, Java, and Python, outperforming vanilla, zero-shot, and standard CoT strategies.