Large Language Model (LLM)
: A neural network trained on massive text datasets that can understand and generate human language. LLMs like Claude, GPT-4, and Gemini power AI coding tools by predicting the most likely next tokens given a context. They excel at code generation, explanation, and transformation but can produce plausible-looking code that contains subtle security flaws.
Why It Matters for AI-Coded Apps
LLMs are the engine behind every AI coding tool. Understanding how they work helps developers anticipate their limitations: they predict probable code, not correct or secure code. LLMs reproduce patterns from training data, meaning common insecure patterns (like using eval() for parsing) appear in generated code because they were common in training data.
Real-World Example
When asked to parse JSON configuration, an LLM might generate eval(configString) because this pattern appeared frequently in older training data. The LLM does not understand that eval() executes arbitrary code – it simply predicts the most probable code pattern for the given context.
How to Detect and Prevent It
Never trust LLM output without verification. Understand that LLMs generate statistically probable code, not necessarily correct or secure code. Use strong system prompts that emphasize security requirements. Review generated code with the same rigor as code from a junior developer. Run automated security tools on all LLM-generated code.
Frequently Asked Questions
Do LLMs understand security?
LLMs do not ‘understand’ security in the way humans do. They can reproduce security patterns from training data and identify common vulnerabilities when asked, but they lack true comprehension of attack vectors. They may generate code that looks secure but has subtle flaws that require human expertise to identify.
Why do LLMs generate insecure code?
LLMs learn from training data that includes both secure and insecure code. Insecure patterns are often simpler and more common in training data (tutorials, Stack Overflow answers, older codebases). The model predicts the most probable pattern, which may be the insecure one.
Which LLM is best for code generation?
As of 2026, Claude (Anthropic), GPT-4 (OpenAI), and Gemini (Google) are the leading models for code generation. Each has strengths in different languages and frameworks. Claude tends to produce more security-conscious code, while GPT-4 excels at complex algorithms.
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