Securing Vibe Coding: Security for AI-Generated Development

AI coding assistants are reshaping how software is written. Developers increasingly rely on models to read repositories and generate or modify files directly inside local projects, often introducing dependencies, configuration changes and large sections of application logic.

Guardrails often exist in the form of configuration files or model instructions intended to constrain how the assistant behaves. In practice, these guardrails are easy to bypass. They can be modified, ignored or removed entirely during development, making them difficult for security teams to enforce consistently.

Most security controls still operate downstream in repositories or CI pipelines, long after AI-generated code has already been written. This leaves a critical stage of development largely unmonitored: the moment when AI-generated code enters the project.

Closing this visibility gap requires security enforcement to operate where AI coding actually happens - inside the development workflow itself, at the moment AI-generated code changes the repository.

The Operational Reality of AI-Assisted Development

AI code assistants are quickly becoming standard developer tooling. Engineers now rely on models to:

• Generate implementation logic.
• Refactor existing code.
• Scaffold dependencies.
• Modify configuration files.
• Accelerate debugging and experimentation.

AI coding tools accelerate developer productivity through vibe coding, where developers guide the model using natural language prompts. As AI begins generating and modifying code directly inside repositories, a new control point emerges in the software lifecycle: the moment the AI writes the code.

The Guardrail Problem in AI Coding Assistants

Configuration files, such as CLAUDE.md are commonly used to guide AI coding assistants, aiming to restrict code generation, enforce coding standards or prevent risky dependencies. However, these files are not effective as security controls in practice because they are easily bypassed. Developers can circumvent these guardrails by:

• Modifying the rules.
• Removing the configuration files entirely.
• Explicitly instructing the model to ignore the rules.

Since these controls operate within the AI code editor, their effectiveness relies on the developer's cooperation. This makes AI guardrails function more as guidance than strict enforcement.

For security teams tasked with enforcing policy across large engineering organizations, this approach is not scalable. Effective security controls must be implemented outside the AI model itself.

The New Security Gap in AI Coding Workflows

Traditional DevSecOps pipelines assume vulnerabilities are introduced through manual development. Security scanners therefore activate when code is:

• Committed to a repository.
• Submitted through a pull request.
• Packaged into an artifact.
  

But AI coding assistants generate and modify code continuously inside the developer environment. By the time CI scans run:

• Vulnerable dependencies may already exist in the codebase.
• Additional logic may depend on the generated code.
• Remediation becomes more disruptive.
  

Security pipelines were designed for commit-driven development, not AI-driven generation workflows.

Moving Security Enforcement Into the Development Workflow

The most effective way to manage AI-generated code risk is to move enforcement earlier to where code is created. Instead of relying on model rules that developers can bypass, security validation must operate independently of the AI assistant.

Kodem approaches this by combining two layers:

• Claude Skill - AI Workflow Integration: Provides security awareness within the AI coding environment.
• Kodem CLI - Policy Enforcement Engine: Automatically scans repository changes and evaluates them against configured SCM policies in Kodem.
  

This architecture allows security validation to run every time the codebase changes, regardless of how the change was generated.

How the Architecture Works

When a developer uses an AI coding assistant:

1. The model generates or modifies files.
2. File changes trigger a security hook.
3. The repository is scanned automatically.
4. Results are evaluated against SCM open-source and code policies configured in Kodem.

When the scan completes, results are evaluated against the SCM policies configured in the platform. Depending on the policy configuration, the action may warn and pass or fail the change. 

Security Enforcement in Practice

A simple example of how the skill works below:

In this scenario, a developer asks Claude Code to modify a project.

1. Claude introduces a vulnerable dependency into the code.
2. This change automatically triggers a local security hook.
3. The hook runs the Kodem CLI, evaluating the repository against the pre-configured Kodem protection policy.
4. During the scan, Kodem immediately detects the following:
   
   • The vulnerable dependency.
   • CVE-2023-26152
   • A violation of the high-severity policy configured in Kodem.
5. As a result, Kodem blocks the change, preventing it from proceeding into the codebase.
   

Since the proposed change violates the configured policy, the workflow automatically halts and Claude can’t proceed. The developer receives immediate remediation guidance, all within the same development context.

Kodem blocks insecure AI-generated code before it even reaches Git.

What Security Teams Gain

Extending security enforcement into AI-assisted development gives teams visibility into a stage of the lifecycle that previously existed outside security tooling. Teams gain visibility into:

• Dependencies introduced by AI.
• Insecure code patterns during development.
• Policy violations in local repositories.
• Attempts to bypass AI guardrails.

Since evaluation occurs immediately after code changes, developers can resolve issues while still working in the same context.

Bottom Line

AI coding assistants are accelerating software development across engineering teams, but the speed of AI generation also means vulnerabilities can propagate faster than traditional security workflows were designed to handle.

Protecting modern development environments requires security controls that operate at the moment code is generated, not only after it reaches the repository. Extending enforcement into the development workflow allows organizations to adopt AI-assisted development without losing control over the security of the code being created.