AI Coding Assistant Security Analysis: Claude Code Leads with Zero Exposed Credentials

Published on • 8 min read
Security AI Coding Assistants Claude Code Cursor Data Analysis

Key Finding: Analysis of 43 GitHub repositories reveals Claude Code users have zero exposed credentials, while Continue.dev had 576 credentials in a single repo. Security scores range from 41-47/100 across all tools.

Executive Summary

We analyzed 43 real GitHub repositories using five major AI coding assistants to measure their security posture. The analysis scanned for security advisories, exposed credentials, and overall repository health.

Methodology: GitHub repository analysis using automated security scanning across repos with CLAUDE.md, .cursorrules, copilot-instructions.md, .continue.md, and .aider.md files.

Security Scores: Claude Code Leads

Tool Repos Scanned Avg Security Score Exposed Credentials Security Advisories
Claude Code 10 47/100 0 0
Continue.dev 10 42/100 576 (1 repo) 0
Aider 3 42/100 0 0
Cursor 10 41/100 1 (1 repo) 5 (1 repo)
GitHub Copilot 10 41/100 5 (2 repos) 0

The Credential Exposure Problem

The most significant security difference wasn't the average score—it was credential exposure:

Critical Finding: One Continue.dev repository (hujianli94/my_Go_Py_blog) contained 576 exposed credentials. This is a massive security risk representing API keys, passwords, or tokens committed to version control.

Credentials Exposed by Tool

Why This Matters

Exposed credentials in public repositories create immediate security risks:

  1. Automated Scanning: Bots scan GitHub constantly for API keys and passwords
  2. Permanent Record: Even deleted commits remain in Git history
  3. Lateral Movement: One exposed credential can compromise entire systems
  4. Financial Impact: Unauthorized API usage can cost thousands in hours

Security Advisories: Cursor's Single Problem Repo

Only one repository across all 43 scanned had security advisories: heyverse/hey using Cursor.

Finding: heyverse/hey had 5 security advisories and scored 0/100—the lowest security score in the entire analysis. This appears to be an outlier rather than a systematic Cursor issue.

Risk Distribution Analysis

Repositories were classified into risk categories based on security scores:

Tool 🔴 Critical
(0-30)		 🟠 High
(31-60)		 🟡 Medium
(61-80)		 🟢 Low
(81-100)
Claude Code 4 5 1 0
Cursor 6 2 2 0
GitHub Copilot 4 5 1 0
Continue.dev 6 3 0 1
Aider 1 2 0 0

What the Scores Don't Tell You

The 6-point spread in average scores (41-47/100) is less meaningful than the credential exposure data:

Insight: Security is about minimums, not averages. A single repository with hundreds of exposed credentials creates more risk than improving average scores by 6 points.

Sample Sizes and Statistical Limitations

This analysis has important limitations:

Actionable Recommendations

For Developers Using Any AI Coding Assistant

  1. Enable Pre-Commit Hooks: Use tools like git-secrets or gitleaks to prevent credential commits
  2. Use .gitignore Properly: Exclude .env files, credentials, and API keys
  3. Rotate Exposed Credentials: If you find exposed credentials in Git history, rotate them immediately
  4. Audit Git History: Use git log -p to search for accidentally committed secrets
  5. Configure AI Tools Safely: Store API keys in environment variables, not code

For AI Tool Vendors

  1. Default to Secure: Provide secure .gitignore templates when creating config files
  2. Credential Detection: Warn users when AI-generated code contains potential credentials
  3. Security Education: Link to security best practices in documentation
  4. Pre-Commit Guidance: Recommend git-secrets or similar tools during onboarding

Conclusion: Security Is a Practice, Not a Tool

Claude Code's zero exposed credentials and 47/100 security score represent the best performance in this analysis. However, the 6-point score difference between tools is less significant than the credential exposure data.

Bottom Line: No AI coding assistant can prevent you from committing credentials to Git. Security depends on developer practices, pre-commit hooks, and organizational policies—not tool selection.

Key Takeaways

  1. Claude Code users showed zero credential exposure across 10 repositories
  2. Continue.dev had one repository with 576 exposed credentials—a critical outlier
  3. Cursor had one repository with 5 security advisories and a 0/100 score
  4. Average security scores (41-47/100) show limited variation across tools
  5. Credential exposure matters more than average security scores
  6. Pre-commit hooks are essential regardless of AI tool choice

Methodology Note: This analysis scanned GitHub repositories containing configuration files for each AI coding assistant. Security scores were calculated based on dependency vulnerabilities, credential exposure, and repository maintenance indicators. The sample size is small (n=43) and results should not be generalized without additional research.

Data Collection Date:

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