Safe AI Coding: An Open-Source Security Toolkit for AI Coding Agents
AI coding agents are changing how developers build software. With tools like Claude Code, developers can generate features, refactor applications, debug errors, and automate repetitive tasks using natural language.
But there is an important question we should ask:
How do we make AI-assisted development safer without slowing down the development process?
AI-generated code can contain security vulnerabilities, incorrect assumptions, missing implementations, or dangerous changes. Coding agents can also encounter malicious instructions hidden inside repositories, configuration files, or other project content.
That is why I built Safe AI Coding — a free, open-source project designed to add security-focused checks and review workflows to AI-assisted development.
🔗 GitHub Repository:
https://github.com/sami-hussain-dev/safe-ai-coding
What is Safe AI Coding?
Safe AI Coding is a Claude Code skill designed to help developers identify risky operations, review AI-generated changes, and inspect repositories for potential security issues.
It focuses on several areas:
- Risk-aware workflows before potentially destructive operations.
- Detection of suspicious instructions and prompt-injection patterns.
- Checks for common security problems in code.
- Identification of potentially incomplete or hallucinated code references.
- Review of changes from technical and human perspectives.
- Optional PDF reports documenting findings and fixes.
The project is open source under the MIT License, so developers can explore the implementation and evaluate whether it fits their workflows.
Why does secure vibe coding matter?
Vibe coding makes it easier to turn an idea into a working application. However, generating code quickly does not automatically make that code secure or production-ready.
Consider these examples:
1. Destructive commands
An agent might propose deleting files, rewriting Git history, or running a database operation that could remove important data.
2. Prompt injection
A repository may contain instructions that attempt to manipulate an AI agent into revealing secrets or performing unintended actions.
3. Insecure application code
AI-generated code may introduce SQL injection, cross-site scripting (XSS), unsafe redirects, weak authentication checks, or other vulnerabilities.
4. Incomplete implementations
A generated feature might reference a missing class, route, method, configuration file, or view.
5. Dependency risks
Incorrect or unverified package references and suspicious installation hooks can introduce supply-chain risks.
These are reasons to combine AI-assisted development with security checks, testing, explicit permissions, and human review.
Key features of Safe AI Coding
1. Risk-aware operation checks
The skill is designed to pause before potentially risky actions, explain the possible consequences, and request approval.
Examples include file deletion, database writes, dependency installation, Git pushes, and changes to security-related configuration.
The actual enforcement depends on the agent's environment and permissions. For stronger protection, the project also recommends configuring explicit Claude Code permission rules.
2. Prompt-injection and repository inspection
Repositories can contain untrusted instructions in files such as CLAUDE.md, AGENTS.md, and editor-specific AI rules.
Safe AI Coding includes checks for suspicious instruction patterns, malicious Git configuration or hooks, symlinks to sensitive files, and hidden Unicode characters.
The goal is to help developers identify potentially dangerous repository content before trusting it.
3. AI-generated code review
The skill checks for issues such as:
- References to missing classes, methods, routes, views, and imports.
- Placeholder comments that may indicate incomplete implementations.
- Pasted chat content or unresolved merge conflicts.
- Tests that have been weakened or skipped.
- Common application security vulnerabilities.
These checks can help surface problems, but no automated review catches every defect.
4. Security and data-safety checks
The project documents checks for several risk categories, including:
- SQL, command, and path injection.
- Cross-site scripting (XSS) and server-side request forgery (SSRF).
- Exposed API keys, tokens, and sensitive identifiers.
- Unsafe database operations and destructive migrations.
- Removed authentication checks and potentially inefficient database queries.
- Suspicious dependency and package-installation configurations.
5. Two-perspective code review
Safe AI Coding is designed to review changes from two perspectives:
- Technical perspective: What could break, introduce a vulnerability, or behave incorrectly?
- Human perspective: Is the change understandable, appropriate, and ready for review or release?
The documented review outcomes are Ready, Ready with notes, and Not ready.
These outcomes are review guidance, not a guarantee that the application is free from vulnerabilities.
6. Optional PDF reports
Developers can request a PDF report summarizing identified issues, changes, fixes, and supporting evidence.
The repository documents English and Roman Urdu report options, which can make the results more accessible to different developers.
7. Built-in self-tests
The README documents a self-test suite containing 95 cases intended to check detection behavior, legitimate code, and handling of potentially malicious repositories.
Developers should run the self-test in their own environment and review the results before relying on the tool in a production workflow.
How to install Safe AI Coding
The recommended installation method is through the Claude Code plugin marketplace.
Open Claude Code and run:
/plugin marketplace add sami-hussain-dev/safe-ai-coding
/plugin install safe-ai-coding@safe-ai-coding
Alternatively, on Windows PowerShell, you can clone the repository into your personal Claude skills directory:
git clone --depth 1 https://github.com/sami-hussain-dev/safe-ai-coding.git "$env:USERPROFILE\.claude\skills\safe-ai-coding"
Use one installation method rather than both. Follow the repository's README for prerequisites, updates, and troubleshooting.
How to use it
After installation, you can use the documented commands for common workflows.
Scan a project:
/safe-ai-coding:safe-ai-coding scan
Use this when beginning work in an unfamiliar repository and looking for potentially suspicious instructions or configuration.
Build a feature:
/safe-ai-coding:safe-ai-coding build Add employee attendance API
Use this workflow to request a feature and have the changes reviewed.
Review changes before merging:
/safe-ai-coding:safe-ai-coding review main
Use this to request a review against the main branch.
Run the self-test:
/safe-ai-coding:safe-ai-coding self-test
Review the output and confirm that the expected checks work in your environment.
Check the repository documentation for the current command syntax and supported setup.
Does Safe AI Coding replace security testing?
No.
A coding skill can provide useful guidance and automated checks, but it cannot guarantee that an AI agent will never make a mistake or that every vulnerability will be detected.
For a stronger development workflow, combine it with:
- Explicit permissions and restrictions for dangerous commands.
- Automated tests and static analysis.
- Dependency and secret scanning.
- Human review of sensitive changes.
- Backups and a controlled deployment process.
- Security testing appropriate to the application.
Safe AI Coding is intended to support these practices, not replace them.
Who can benefit from this project?
The project may be useful for:
- Developers who use Claude Code for everyday programming.
- Vibe coders building applications with AI assistance.
- Laravel, PHP, JavaScript, TypeScript, and Python developers.
- Developers reviewing unfamiliar or untrusted repositories.
- Teams that want more structured checks around AI-generated changes.
- Open-source contributors interested in AI agent security.
Although the project documents support for multiple programming stacks, its current implementation and compatibility should be evaluated for your particular environment.
Try it and share your feedback
I built Safe AI Coding to explore how security checks and review workflows can fit into AI-assisted development without making the process unnecessarily complicated.
I welcome feedback, bug reports, feature suggestions, and contributions from developers who want to help improve the project.
Explore the source code, review the documented checks, and try it in a test project before using it in critical workflows.
🔗 GitHub: https://github.com/sami-hussain-dev/safe-ai-coding
What security risk concerns you most when using AI coding agents: prompt injection, insecure generated code, exposed secrets, or destructive operations?
I'd love to hear your thoughts and learn from your experiences.
Author: Sami Hussain | Software Engineer and creator of Safe AI Coding
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