Rules Files for Safer Vibe Coding

Helping LLMs generate safer and more secure code through open-sourced rules files.

Large Language Models (LLMs) are good at coding. This has powered emerging AI-assisted programming tools, ranging from GitHub Copilot to AI IDEs and IDE extensions like Claude Code, Cursor, Windsurf, Codeium, and Roo.

It’s also democratized some elements of development, birthing Vibe Coding, where you “forget that the code even exists.”

Not all AI-assisted coding is vibe coding. Regardless, security risk emerges the more developers are removed from the details of generated code, and the more non-developers gain access to vibe code risky software.

It’s clear that currently AI-Generated code is not secure by default. Based onBaxBench, somewhere between 25% and 70% of working coding outputs from leading models contain vulnerabilities. Some research has also indicated that users given an AI assistant will produce more vulnerable code, primarily due to excessive confidence in the generated code. Recent model improvements have dramatically reduced vulnerability prevalence, but there is still more to be done.

When we look specifically at vibe coding, there is an even more stark increase in risk. Notable anecdotes have crossed into broad awareness, like the vibe coding SaaS entrepreneur whose application unfortunately became a security piñata from hardcoded secrets, missing authentication and authorization checks, and more:

It’s continues to happen:

Traditional software security tooling still has merit in securing code, no matter the involvement of AI.SAST,SCA, andSecrets scanningall have a role to play. The emergence of AI-Assisted Programming increases the importance of shifting such scans left, to the degree possible, into the IDE. PR time scanning and remediation continues to be crucial. Creation and adoption of secure by default frameworks and libraries can also reduce risk.Legacy approaches aside, AI coding assistants have brought with them one new capability to exert leverage on application security:Rules Files.

Rules files are an emerging pattern to allow you to provide standard guidance to AI coding assistants.

You can use these rules to establish project, company, or developer specific context, preferences, or workflows. Most popular AI coding tools offer rules, ranging from GitHub CopilotRepository Custom Instructionsor CodexAGENTS.md, AiderConventions, ClineCustom Instructions, ClaudeCLAUDE.md, CursorRules, and WindsurfRules.

Research consistently finds that crafted prompts can significantly reduce the number of vulnerabilities in AI-generated code. Rules files offer an ideal method to centralize and standardize these security-focused prompting improvements.

To methodically craft a rules file for security, we consider best practice for rules files, the vulnerabilities most common in AI-Generated code, and research-based best practices in prompting for secure code generation.

Best practices for rules files

focus on crafting instructions that are clear, concise, and actionable

tailor rules to their relevant scope, such as a particular programming language

break down complex guidelines into smaller, atomic, and composable rules

keep the overall rules concise; under 500 lines

Insecure habits in AI-generated code

In general, systematic review shows that CWE-94 (Code Injection), CWE-78 (OS Command Injection), CWE-190 (Integer Overflow or Wraparound), CWE-306 (Missing Authentication for Critical Function), and CWE-434 (Unrestricted File Upload) are all common. Of course, there is methodological bias here, as most studies specifically targetMITRE’s CWE Top 25 Most Dangerous Software Weaknesses.

Unsurprisingly, language also matters when it comes to software vulnerabilities. These patterns, of course, map to common vulnerabilities, regardless of AI involvement. For example, memory management issues are overrepresented in C/C++. For Python, on the other hand, deserialization of untrusted data and XML-related vulnerabilities are more common.

Research based best practices in prompting for secure code generation

Early investigations in this domainfound that “a simple addition of the term "secure" to the prompt led to a reduction in the average weakness density of the generated code by 28.15%, 37.03%, and 42.85% for GPT-3, GPT-3.5, and GPT-4.” The same paper found that a prompt highlighting top CWEs was most effective (“Generate secure Python code that prevents top security weaknesses listed in CWE for the following:”), compared to persona-based and naive security prompts.Of possible generic prefixes,a different studyfound “You are a developer who is very security-aware and avoids weaknesses in the code.” was able to reduce the risk of vulnerable code in a single pass by 47-56% on average, depending on the model. This outperformed alternatives like “make sure every line is secure” and  “examine your code line by line and make sure that each line is secure.”

Rules files have yet to see broad adoption for security use cases. The best security rules file is one custom to your organization. However, to help unstick the blank page problem,we’re open sourcing a set of baseline secure rules files. These rules were created with the help of Gemini, using a prompt that encodes the guidance outlined above.

We’ve generated rules targeting the a set of common languages and frameworks:

Python: Flask, Django

Javascript: React, Node.js

Java: Spring

.NET: ASP.NET Core

Compatible rules are available for all of the following popular AI coding assistants and tools:

Cursor Rules

Cline Rules

Claude CLAUDE.md

Windsurf Rules

Codex AGENTS.md

Aider Conventions

GitHub Copilot Copilot Instructions

If you're currently generating code with AI, and you're not taking advantage of a rules file for security benefit, start here.

We’re alsoopen sourcing the prompt used. We welcome contributions, whether they add coverage for additional languages and frameworks, or offer evidence based improvements to the prompt.

Vibe Coding: Shipping Features or Shipping Vulnerabilities?

Vibe Coding is a Dangerous Fantasy

Can AI “Vibe Coding” Be Trusted? It Depends…

Not all AI-assisted programming is vibe coding (but vibe coding rocks)

Andrej Karpathytweet coining “Vibe Coding”

Code of Silence – The Hidden Risks of AI-Generated Code

Secure Vibe Coding Made Simple

Finding more vulnerabilities in vibe coded apps

The Rise of Slopsquatting

Secure Vibe Coding Guide

Securing React: Prompt Engineering for Robust and Secure Code Generation- Jim Manico

Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions

SecRepoBench: Benchmarking LLMs for Secure Code Generation in Real-World Repositories

The Hidden Risks of LLM-Generated Web Application Code: A Security-Centric Evaluation of Code Generation Capabilities in Large Language Models

A Comprehensive Study of LLM Secure Code Generation

Benchmarking Prompt Engineering Techniques for Secure Code Generation with GPT Models

CWEVAL: Outcome-driven Evaluation on Functionality and Security of LLM Code Generation

Security and Quality in LLM-Generated Code: A Multi-Language, Multi-Model Analysis

Secure Coding with AI – From Creation to Inspection

Do Users Write More Insecure Code with AI Assistants?

Artificial-Intelligence Generated Code Considered Harmful: A Road Map for Secure and High-Quality Code Generation

Prompting Techniques for Secure Code Generation: A Systematic Investigation

How Secure is Code Generated by ChatGPT?

Is Your AI-Generated Code Really Safe? Evaluating Large Language Models on Secure Code Generation with CodeSecEval

Is GitHub’s Copilot as Bad as Humans at Introducing Vulnerabilities in Code?

Assessing the Security of GitHub Copilot’s Generated Code - A Targeted Replication Study

A systematic literature review on the impact of AI models on the security of code generation

Just another copy and paste? Comparing the security vulnerabilities of ChatGPT generated code and StackOverflow answers

Can You Really Trust Code Copilots? Evaluating Large Language Models from a Code Security Perspective

SOK: Exploring Hallucinations and Security Risks in AI-Assisted Software Development with Insights for LLM Deployment

From Vulnerabilities to Remediation: A Systematic Literature Review of LLMs in Code Security

We Have a Package for You! A Comprehensive Analysis of Package Hallucinations by Code Generating LLMs

HexaCoder: Secure Code Generation via Oracle-Guided Synthetic Training Data

SALLM: Security Assessment of Generated Code

Over 50% of Wiz customers have reduced their cloud risk by reaching Zero Critical Issues

The Wiz Threat Research team has identified a widespread cryptojacking campaign targeting commonly used DevOps applications including Nomad and Consul.

Empower platform teams and developers to reduce noise, scale ownership, and accelerate remediation across cloud-native apps.

Get a personalized demo

©2026Wiz, Inc.

StatusPrivacy PolicyTerms of UseModern Slavery StatementCookie Settings