AI-Assisted Code Review With Claude Code (Terminal)

A security-first walkthrough with hands-on prompts and sample code.

AI-assisted code review can dramatically speed up how you find bugs, edge cases, and security issues — especially during development, before a human review cycle even begins. In this article, we’ll walk through using Claude Code, an AI assistant that runs in your terminal. We’ll cover installation, the most important security step (restricting file access), and then we’ll run a few practical, realistic code review examples you can copy/paste into your own workflow.

The core idea: use AI as a reviewer you can query on-demand, while keeping strict control over what code and files it can see.

Prerequisites

Before you start, make sure you have:

• A Claude AI or Claude Console account
• Node.js v18+
• A terminal on macOS/Linux/Windows
• A project directory (or a test folder)

1. Install Claude Code

If Node.js is installed (v18+), you can install Claude Code globally:

bashCopy Codenpm install -g @anthropic-ai/claude-code

This makes the claude command available system-wide.

First Run (Login)

Run:

bashCopy Codeclaude

On the first run, you’ll be prompted to authenticate. After that, you typically won’t need to log in again unless your session expires.

2. Start Claude Code Inside Your Project

Claude Code operates within the context of your current directory. For a quick sandbox:

bashCopy Codemkdir claude-review-sandbox
cd claude-review-sandbox
claude

Now Claude is ready to help, so the next step is to ensure it cannot access secrets.

3. Configure Project-Specific Security Boundaries (Critical Step)

One of the biggest risks with AI tooling is accidental exposure of sensitive files (tokens, keys, credentials, internal certificates). Claude Code supports project-level settings so you can define guardrails once and share them across your team.

Create the Settings Directory and File

From your project root:

bashCopy Codemkdir .claude
touch .claude/settings.json

Deny Access to .env* and secrets/

Edit .claude/settings.json:

jsonCopy Code{
  "permissions": {
    "deny": [
      "**/.env",
      "**/.env.*",
      "**/secrets/**"
    ]
  }
}

What it does:

• Blocks .env files (including .env.local, .env.production, etc.)
• Blocks anything in a secrets/ directory
• Reduces the chance that an AI review session accidentally reads credentials

Team Workflow Tip

Commit this file to source control so everyone shares the same boundary:

bashCopy Codegit add .claude/settings.json
git commit -m "Add Claude Code project permissions"

4. How to Ask for an AI Review (The “Focused Prompt” Rule)

The most effective AI-assisted code review isn’t:

“Review my whole app.”

It’s closer to:

“Review this function for edge cases and security issues. Focus on timing attacks and input validation.”

Focused prompts give:

• More actionable answers
• Less generic advice
• Less need to share broad context

Practical Examples (Copy/Paste-Friendly)

Below are practical examples you can include in your article to make it feel real and hands-on. Each example includes:

• A small code sample
• What to ask Claude Code
• What kinds of issues you should expect it to catch
• A “next prompt” that deepens the review

Example 1: Authentication Function Review (Edge Cases + Timing Leaks)

Sample code (intentionally flawed): Create a file like auth.js:

jsCopy Code// auth.js
const users = [
  { username: "alice", password: "password123" },
  { username: "bob", password: "qwerty" }
];

export function login(username, password) {
  if (!username || !password) return { ok: false, message: "Missing fields" };

  const user = users.find(u => u.username === username);
  if (!user) return { ok: false, message: "User not found" };

  if (user.password !== password) return { ok: false, message: "Bad password" };

  return { ok: true, message: "Welcome!" };
}

Ask Claude (targeted prompt): In Claude Code, you might ask:

“Review login() for edge cases and security vulnerabilities. Focus on timing attacks, information leakage, and brute-force resistance.”

What Claude will likely point out:

• Plaintext password storage (should hash)
• Password compare not constant-time
• Different error messages reveal whether username exists
• No rate limiting/lockout/throttling
• Potential timing differences based on early returns

Follow-up prompts that improve results:

• “Show a safer pattern that returns a uniform error message for auth failures.”
• “How can I reduce timing differences between user-not-found and wrong-password?”
• “Suggest minimal changes that improve security without rewriting the whole system.”

This shows readers how to iterate rather than treating AI as a one-shot answer machine.

Example 2: Timing Attack Exploration (Make the Concept Concrete)

If your article discusses timing attacks, make it practical with a “walkthrough prompt”:

Prompt:

“Walk through each execution path in this function and explain what an attacker could infer from response timing differences.”

Then:

“How would an attacker statistically measure these differences over many requests? What signals would they look for?”

This moves the article from abstract security theory into a realistic attacker mindset — without requiring you to publish exploit code.

Example 3: Hard-Coded Credentials in Config (Common Real-World Issue)

Sample code: Create config.js:

jsCopy Code// config.js
export const config = {
  dbHost: "prod.db.company.internal",
  dbUser: "admin",
  dbPassword: "SuperSecretPassword!",
  apiKey: "abcd-1234-SECRET-KEY",
  jwtSecret: "replace-me-with-a-real-secret"
};

Ask Claude:

“Identify the security risks in this config file. Refactor it to use environment variables, and propose a safe local dev workflow.”

What a good answer includes:

• Replace hard-coded values with process.env.*
• Provide .env.example (safe to commit)
• Add .env to .gitignore
• Recommend secret rotation and access control
• Add startup validation so the app fails fast if env vars are missing

Practical follow-up:

“Add validation that throws an error if any required env var is missing. Keep it simple and readable.”

This turns the AI review into an immediate implementation improvement.

Example 4: Insecure Logging (Acidental Secret Leaks)

This is a practical example many teams relate to.

Sample code:

jsCopy Codeexport function handleLogin(req) {
  console.log("LOGIN REQUEST:", req.body); // might contain password!
  // ...
}

Ask Claude:

“Review this for sensitive data exposure. What should never be logged? Suggest a safer logging strategy.”

What readers learn:

• Don’t log passwords, tokens, session IDs, auth headers
• Use structured logging with redaction
• Log metadata (request id, user id if safe), not raw payloads

Follow-up prompt:

“Rewrite this log statement to keep it useful for debugging but redact sensitive fields.”

Example 5: Dependency and Supply-Chain Review Prompts

Even without running a full SCA tool, AI can help you reason about risk and cleanup.

Prompts:

• “Review this package.json for suspicious or outdated dependencies and suggest cleanup.”
• “What are best practices for pinning versions and handling lockfiles?”

This helps your article show that AI review isn’t only about “code bugs”—it’s also about safe engineering habits.

A Reusable Prompt Checklist (Add This to the Article)

Here’s a prompt library you can include as a boxed section:

Security-Focused Prompts

• “List the top 5 security risks in this function and rank them by severity.”
• “Are there any injection risks (SQL/command/path)? Explain where.”
• “Does this error handling leak sensitive information?”
• “Are there timing differences that reveal anything sensitive?”

Quality-Focused Prompts

• “What are the edge cases that would break this?”
• “Where should input validation happen, and what rules should apply?”
• “Suggest tests for the most important failure modes.”

Minimal-Change Prompts (Very Practical)

• “Suggest the smallest change that improves safety.”
• “Refactor this to be safer but keep the same behavior and signature.”

Closing: Responsible AI Review Is Configuration + Good Questions

Claude Code becomes dramatically safer and more useful when you combine:

1. Project security boundaries (.claude/settings.json)
2. Focused review questions (avoid “review everything”)
3. Iterative investigation (ask follow-ups to validate and deepen)

That combination gives you fast feedback during development—without losing control over sensitive data.