Show HN: VibeGuard – security linter for AI-generated code

AI coding assistants — GitHub Copilot, Cursor, Claude, ChatGPT — write code fast. Really fast. Faster than any security review can keep up with.

The problem is they also confidently produce the same security mistakes over and over. Not because they are bad tools. Because they were trained on millions of code examples — and millions of those examples had security vulnerabilities in them.

The exact mistakes AI coding assistants make repeatedly:

• SQL queries built with string concatenation instead of parameterized queries
• Secrets and API keys hardcoded directly into source files
• User input passed to eval()

,exec()

,subprocess.shell=True

without validation - JWT tokens verified without checking the algorithm — the alg:none

bypass - XML parsers configured to allow external entities — XXE vulnerabilities

• Insecure random number generation used for security-sensitive values
• Path traversal — user-controlled file paths with no sanitization
• CORS configured to accept any origin
• Debug mode left enabled in production configuration
• Pickle deserialization of untrusted data — remote code execution

Traditional linters like Bandit and Semgrep catch some of these. But they use generic rules that were not built around the specific patterns AI tools produce. VibeGuard is different — every rule was written by studying actual AI-generated code and cataloguing the exact vulnerability patterns these tools produce.

$ vibeguard scan --path ./my-ai-generated-project

[*] VibeGuard v1.0.0 — AI-Generated Code Security Linter
[*] Scanning: ./my-ai-generated-project
[*] Running 47 AI-pattern rules...

app/database.py:34     CRITICAL  SQL_INJECTION      f-string used in SQL query — classic Copilot pattern
app/auth.py:12         CRITICAL  HARDCODED_SECRET   API key assigned to variable — detected by entropy
app/utils.py:89        HIGH      COMMAND_INJECTION   subprocess called with shell=True + user input
app/api.py:156         HIGH      JWT_ALG_NONE        JWT decoded without algorithm verification
config/settings.py:8   HIGH      DEBUG_PRODUCTION   DEBUG=True in production settings file
app/files.py:44        MEDIUM    PATH_TRAVERSAL      User input used in file path without sanitization
app/xml_parser.py:23   MEDIUM    XXE_INJECTION       XML parser allows external entities

[*] Grade: D  (7 findings — 2 critical, 3 high, 2 medium)
[*] Report saved to vibeguard-report.json

Fix these first:
  app/database.py:34 → Use cursor.execute(query, params) instead of f-strings
  app/auth.py:12     → Move to environment variable: os.environ.get('API_KEY')

Developers using Copilot, Cursor, Claude, or ChatGPT to write codeSecurity engineers reviewing AI-generated pull requestsEngineering teams who have adopted AI coding tools and want automated security checksDevSecOps teams who want AI-specific security gates in their CI/CD pipelineStudents learning about the security implications of AI-generated code

python3 --version

You need version 3.10 or higher.

git --version
git clone https://github.com/zeroFhacker/vibeguard.git
cd vibeguard

python3 -m venv venv
source venv/bin/activate   # Windows: venv\Scripts\activate

pip install -r requirements.txt
PYTHONPATH=. python -m vibeguard.cli scan --path ./my-project
PYTHONPATH=. python -m vibeguard.cli scan --path ./app/database.py
PYTHONPATH=. python -m vibeguard.cli scan --path . --ci --fail-on high
PYTHONPATH=. python -m vibeguard.cli scan --path . --severity critical
PYTHONPATH=. python -m vibeguard.cli scan --path . --output report.json
PYTHONPATH=. python -m vibeguard.cli rules list

• SQL injection via f-string or concatenation

• Command injection via shell=True

• LDAP injection

• XPath injection

• Template injection

• API keys assigned to variables

• Hardcoded passwords in source

• AWS/GCP/Azure credentials in code

• Private keys in source files

• Database connection strings with credentials

• JWT decoded without algorithm verification

• JWT secret hardcoded

• Weak session secret

• Missing authentication on sensitive endpoints

• Insecure password hashing (MD5, SHA1)

• Path traversal via user-controlled file paths

• XML external entity injection

• Eval/exec with user input

• Pickle deserialization of untrusted data

• YAML load instead of safe_load

• Debug mode enabled in production

• CORS wildcard origin

• Insecure cookie settings (no HttpOnly, no Secure)

• Weak TLS configuration

• Default admin credentials

• MD5 used for security-sensitive hashing

• SHA1 used for security-sensitive hashing

• Weak random (random module) for security values

• ECB mode encryption

• Hardcoded encryption key

Add to .github/workflows/security.yml

:

name: VibeGuard Security Scan

on: [push, pull_request]

jobs:
  vibeguard:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v4
        with:
          python-version: '3.11'
      - run: pip install -r requirements.txt
      - name: Run VibeGuard
        run: |
          PYTHONPATH=. python -m vibeguard.cli scan \
            --path . \
            --ci \
            --fail-on high \
            --output vibeguard-report.json
      - name: Upload report
        uses: actions/upload-artifact@v4
        with:
          name: vibeguard-security-report
          path: vibeguard-report.json

New AI-pattern rules are always welcome. To add a rule:

• Add a RulePattern

tovibeguard/rules/patterns.py

• Write the regex or AST check
• Include: name, description, severity, AI tool that commonly produces this, plain English fix
• Add a test in tests/test_rules.py

See CONTRIBUTING.md

for full guidance.

MIT — see LICENSE

Part of the open-source security toolkit at github.com/zeroFhacker