Introduction As large language models (LLMs) and deep learning technologies are widely applied in the security field, traditional methods of Webshell detection based on feature codes, abstract syntax trees (AST), or sandbox behavior analysis are gradually evolving towards AI-based semantic analysis. AI models can understand the logical intent of code, thereby identifying malicious scripts that have undergone complex obfuscation. However, AI models have an inherent weakness: when processing code, they often infer the logic of the code alongside "non-executable content" such as comments and metadata within the same semantic space. This poses a risk for prompt injection.

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