If you use Claude Code, Codex, Lovable, Bolt, Cursor, Replit, or any other AI coding agent, there is a very real chance your sensitive data is already exposed without you realizing it. AI coding tools move fast, and that speed is exactly what makes them dangerous. They can hardcode API keys, leave passwords in files, miss obvious vulnerabilities like SQL injection, and push insecure code straight into GitHub before anyone stops them.
That is the problem. The fix is adding a security layer that catches those mistakes before they ship. One tool built specifically for this is Rafter. It scans repositories, checks live sites, finds leaked secrets, flags insecure dependencies, highlights code quality issues, and then gives you plain-English instructions you can hand directly back to your coding agent to clean things up fast.
If AI-assisted development is now part of your workflow, AI code security cannot be optional anymore. It needs to be part of the process from the beginning.
Why AI coding agents create a security problem
AI coding agents are amazing at momentum. You ask for a landing page, a backend integration, a database flow, or a full app feature, and they generate it quickly. That speed feels like magic right up until you realize the code may include mistakes a security-conscious engineer would catch immediately.
The issue is not that these tools are useless. The issue is that they are often confident and productive before they are careful.
Common problems include:
- API keys embedded directly in source files
- Passwords or credentials committed into repositories
- Weak or missing validation that opens the door to injection attacks
- Insecure project configuration
- Dependencies with known risks
- Generated code that works, but violates security best practices
And when code is created and pushed rapidly, insecure patterns can end up in both public and private repos. Private does not always mean safe either. A bad secret in a private repository can still cause major damage if it reaches production systems, internal services, or shared environments.
This is one of the biggest hidden costs of vibe coding. You get speed, but you also get sloppiness unless you put a checkpoint in place.
What Rafter does
Rafter is designed to act as that checkpoint. Instead of expecting you to manually audit every file your AI tool creates, it connects to your workflow and scans for the things most likely to hurt you later.
It checks for:
- Vulnerabilities in your codebase
- Secrets and credentials that should never be committed
- Insecure dependencies
- Code quality issues
- Infrastructure-related risks
- AI-generated mistakes that slip into projects built quickly
The practical part is what makes it useful. It does not just tell you that something is wrong. It points to the exact file, the exact line, and explains how to fix it in plain English. That means you do not need to be a security expert to understand the report. You can simply take the suggested fix and hand it back to Claude Code, Codex, or whichever agent you use.
How to set up Rafter in an AI coding workflow
The setup is intentionally simple. You sign up, copy the configuration snippet or setup instructions, and paste them into your coding environment. From there, the tool walks through what needs to be connected.
One of the smartest ways to configure it is to make it run on all future projects. Instead of remembering to add security later, you bake it into the default workflow. Every time the agent writes code, the scanner is there to catch issues early.
The process generally looks like this:
- Connect Rafter to your coding environment.
- Add your Rafter API key where needed.
- Set it to run automatically for future projects.
- Connect your GitHub account and authorize repository access.
- Choose a repository and branch to scan.
- Run a fast scan or a fuller scan depending on your needs.
Once that is in place, you have a security layer sitting between your AI agent and your repository. That is the sweet spot. Instead of fixing disasters after a push, you stop them before they go live.
What the scan results actually look like
When Rafter finishes a scan, it gives you a score and breaks findings into categories such as secret detection, fundamentals, AI-related concerns, and infrastructure. It also groups issues by severity, including critical errors, warnings, and lower-priority improvements.
If a repository is clean, the report shows that clearly. If it is not, the report becomes even more valuable because it explains exactly what needs attention.
You can expect details like:
- Which file contains the issue
- The specific line involved
- Why the problem matters
- How to fix it in straightforward language
- A ready-made AI prompt to speed up remediation
That last piece matters a lot. Instead of writing your own prompt engineering every time, you can copy the suggested prompt and feed it directly into your coding assistant. That turns your AI tool into something much closer to a security-aware developer.
Use case 1: Scan your entire GitHub repository before shipping
The most obvious use case is also one of the most important. Connect your GitHub account, pick a repository and branch, and run a scan across the whole project.
This works for both public and private repositories. That is a big deal because security issues are not limited to public code. In fact, some of the worst leaks sit unnoticed in private repos for months.
A full repository scan is useful when:
- You are about to deploy a new project
- You have been coding rapidly with AI and want a cleanup pass
- You inherited a codebase and do not trust its current state
- You want to audit older projects for forgotten secrets or weak patterns
In one example, a scan produced a low score, which immediately signaled that the project needed work. The secret detection category looked fine, but the fundamentals category was weak, which revealed broader structural and best-practice issues. Instead of guessing what to fix, the report provided an AI-ready prompt to handle the remediation.
That is the real value. It shortens the path from problem found to problem fixed.
Use case 2: Run Rafter inside Claude Code or another coding agent
This is where things get really powerful.
Imagine you ask your coding agent to build a coffee shop landing page. It creates the HTML, writes the CSS, and starts assembling the project fast. Before anything gets committed or deployed, you trigger a Rafter scan from inside that same workflow.
Now the code gets checked before it spreads anywhere.
That means:
- No accidental secret leaks slipping into version control
- No vulnerable patterns surviving by default
- No guessing whether the generated code is safe enough
If the tool needs a key for setup, you provide it once, and then the security assessment becomes part of the coding process. This is much better than doing a security audit after the fact, because the whole point is to stop bad output before it becomes someone else’s problem.
For anyone heavily relying on Claude Code tips, Codex automation, or AI pair programming, this should be close to mandatory.
Use case 3: Hand the fix directly back to your coding agent
One of the best features in Rafter is the ability to copy a ready-made prompt based on the finding. This prompt frames the issue in a way that turns your coding agent into a focused troubleshooting assistant.
Instead of saying something vague like “fix the security issue,” the prompt provides context, describes the task, and asks for direct, actionable remediation.
That helps in two ways:
- You do not need to know how to explain the issue properly.
- Your AI agent gets a far better instruction set and usually returns a better fix.
This matters because many people using AI coding agents are not security engineers or even experienced developers. They are builders trying to move quickly. If the scanner can identify the problem and package the fix request clearly, it removes a huge amount of friction.
It also reduces the odds of half-fixing something or introducing a new problem while trying to patch the old one.
Use case 4: Scan live websites without touching the code
Rafter is not limited to repositories. You can also create a project using a live domain and scan the site itself.
You paste in the URL, choose the pages you want to monitor, and run the audit. The tool then checks the live site for things like:
- Security hygiene
- Best practices
- SEO
- DNS issues
- Speed and performance signals
- Accessibility concerns
This is useful for people who manage websites but do not necessarily want to dig through the underlying codebase right away. It also works well as a first-pass audit on client sites, side projects, landing pages, and older websites that may have drifted away from best practices.
If a live site is public, every accessible URL is worth checking. A homepage may look fine while deeper pages have weak configuration, missing hygiene, or other avoidable issues.
Another nice touch is that the scan can generate a prompt you can use to get AI help implementing the recommended changes.
Use case 5: Audit AI skills, extensions, and add-ons before installing them
This one is easy to overlook, but it may become one of the biggest security topics over the next few years.
More and more AI tools now support skills, plugins, or extensions. People routinely download these from the internet and install them with almost no scrutiny. The problem is that these add-ons may have access to local files, project data, credentials, and other sensitive information.
If a skill is poorly written or intentionally risky, it can become a serious attack surface.
That is why auditing these components matters. You can point Rafter at them, look for vulnerabilities, identify unsafe behaviour, and understand what information might be exposed before trusting them on your machine.
This applies whether the skill came from a random download, a community source, or even your own AI-assisted generation. Just because a tool works does not mean it is safe.
As AI ecosystems mature, this category of risk is only going to grow. People will install whatever promises convenience, and many of those tools will operate with broad access. Security checks need to become normal before installation, not after something suspicious happens.
What makes this especially useful for non-experts
A lot of security tools are powerful but intimidating. They assume you know what every alert means and how to fix it. That is not realistic for most people using AI coding agents.
Rafter is useful because it translates technical problems into something actionable.
You do not need deep expertise to get value out of it because it:
- Surfaces problems automatically
- Shows exactly where they live
- Explains them in plain language
- Provides prompts that help AI tools fix them
That closes a dangerous gap. Without a tool like this, a beginner or intermediate builder may never even know a secret was leaked or a vulnerability was introduced.
The bigger lesson: fast AI coding still needs guardrails
The main takeaway here is not that AI coding agents are bad. They are incredibly useful. The real lesson is that fast code generation without security checks is asking for trouble.
AI tends to optimise for completion. It wants to produce something that works. It does not always prioritise what is safe, maintainable, or production-ready unless you force that into the process.
That is why the right model is not:
- Generate code
- Commit code
- Hope for the best
It should be:
- Generate code
- Scan code
- Fix findings
- Then ship
That extra step can save you from leaked credentials, insecure deployments, preventable downtime, and the nightmare of discovering months later that something sensitive was exposed the entire time.
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FAQ
Can Claude Code really leak sensitive data?
Yes. Any AI coding agent can accidentally expose sensitive data if it hardcodes secrets, stores credentials in source files, or generates insecure patterns that get committed without review.
What kind of issues can Rafter detect?
Rafter can detect vulnerabilities, exposed secrets, credentials, insecure dependencies, infrastructure issues, and code quality problems. It can also scan live sites for security, SEO, DNS, speed, and accessibility concerns.
Does Rafter work with private GitHub repositories?
Yes. It can scan both public and private repositories once connected and authorized through your GitHub account.
Do I need to be a security expert to use it?
No. One of the biggest advantages is that it explains findings in plain English and gives you prompts you can pass directly to your AI coding agent for remediation.
Can it scan websites even if I do not access the code?
Yes. You can create a project from a live URL and run an audit on the site itself to evaluate security hygiene, best practices, SEO, DNS, speed, accessibility, and related issues.
Final thoughts
If you are building with AI, security needs to be built into the workflow too. That is the whole game now. The faster these tools get, the more important it becomes to add something that catches the messy parts before they become expensive problems.
Rafter fills that role well by scanning your repositories, checking your live sites, auditing risky add-ons, and turning security findings into fixes your AI coding agent can actually use. That makes it practical, not just theoretical.
If you rely on Claude Code, Codex, or similar tools every day, it is worth treating AI code security as a standard step, not an optional cleanup task. Run the scan, patch the issues, and ship with a lot more confidence.
If you want to keep tightening up your AI workflow, explore related articles on prompt engineering, GitHub hygiene, and secure AI development practices. And if this helped, share it with someone who is shipping code a little too fast.
