AI Code Detection: How to Identify AI-Generated Code in Your Codebase

As AI coding tools become ubiquitous, organizations need visibility into how much of their codebase is AI-generated. This is not about prohibiting AI—it is about informed risk management.

AI-generated code enters codebases through multiple channels: IDE extensions like GitHub Copilot and Cursor that suggest code inline, chat interfaces like ChatGPT and Claude where developers copy-paste solutions, and automated code generation pipelines. Without detection, teams have no visibility into what percentage of their codebase was AI-generated and whether it received adequate review.

Detection matters for several reasons. AI-generated code has higher rates of security vulnerabilities due to missing validation and insecure patterns. Compliance frameworks increasingly require documentation of AI involvement in software development. And from a quality perspective, AI-generated code may reference hallucinated APIs, use deprecated patterns, or introduce subtle logic errors that only surface under edge conditions.

The primary risk is not that AI generates bad code—it is that developers accept AI suggestions without the critical review they would apply to their own code or a colleague's code. This overreliance pattern has measurable consequences:

• Security gaps: AI-generated code frequently omits input validation, error handling, and security checks. A Stanford study found developers using AI assistants produced significantly more security vulnerabilities.
• Hallucinated dependencies: AI models generate import statements for packages that do not exist. Attackers register these names on npm and PyPI, turning hallucinations into supply chain attacks.
• License compliance: AI models trained on open-source code may reproduce copylighted snippets, introducing license compliance risks that legal teams cannot assess without knowing which code is AI-generated.
• Technical debt: AI-generated code may work correctly but use non-idiomatic patterns, making it harder to maintain. When developers do not understand the code they accepted, debugging and extending it becomes more expensive.

Detection provides the foundation for governance: you cannot review what you cannot identify.

AI code detection relies on identifying patterns that distinguish machine-generated code from human-written code. These signals fall into three broad categories:

Hallucination Patterns

AI models generate code referencing functions, methods, parameters, and packages that do not exist in the target language or framework. These hallucinations follow predictable patterns—the model generates plausible-sounding but non-existent APIs based on naming conventions it learned from training data. Hallucination detection identifies these references and flags them for review.

Heuristic Signals

AI-generated code exhibits structural and stylistic characteristics that differ from human-written code. These include specific commenting patterns, particular approaches to error handling, characteristic variable naming, and structural choices that reflect how language models construct code token by token rather than how developers think about problems holistically.

LLM Fingerprints

Different AI models leave identifiable fingerprints in the code they generate. GPT-4, GitHub Copilot, Claude, and Cursor each have characteristic patterns in how they structure code, name variables, write comments, and handle edge cases. These model-specific signatures enable not just detection of AI-generated code, but attribution to specific AI tools.

CodeSlick's AI code detection is an industry-first capability that analyzes code across 150 distinct signals to identify AI-generated content:

• 105 hallucination patterns: Detects references to non-existent APIs, phantom parameters, and fabricated library functions across all five supported languages
• 13 heuristic signals: Identifies structural and stylistic characteristics that distinguish AI-generated code from human-authored code
• 32 LLM fingerprints: Recognizes code patterns specific to GPT-4, GitHub Copilot, Claude, Cursor, and other major AI coding tools

When AI-generated code is detected, CodeSlick flags it for security review and runs the full 294-check security analysis to catch the vulnerabilities AI commonly introduces. This dual-layer approach—detection plus security scanning—ensures AI-generated code meets the same security standards as human-written code.