code-validator

What is code-validator? A static security scanner purpose-built for code produced by AI assistants (Claude, ChatGPT, GitHub Copilot, Cursor). Detects hardcoded credentials, CORS misconfigurations, SQL injection patterns, and dependency CVEs — in under 1 second per file, fully offline, with zero external API calls.

Why this exists: AI assistants frequently emit code that looks correct but contains subtle security defects — hardcoded API keys in examples, allow_origins=["*"] with allow_credentials=True, string-concatenated SQL. code-validator is the lightweight CI gate that catches these before they reach main.


🎯 Use case	Block insecure AI-generated code at PR time
⚡ Speed	<1s per file, `--git-diff` mode scans only changed files
🔒 Privacy	100% offline. No code leaves your machine. Only dependency: `pydantic`
🧪 Detection rules	20 rules across security, quality, and dependency layers (SEC001–SEC013 excluding SEC007, QUAL001–QUAL002, DEP001–DEP006)
📦 Install	`pip install -r requirements.txt` — done

Features

Security Scanning

Hardcoded credentials: API keys (OpenAI, Anthropic, Google, GitHub tokens), passwords, database URLs, Django/Flask SECRET_KEY, AWS access keys, and embedded PEM private keys
Dangerous CORS configurations: wildcard origins, and wildcard origins combined with allow_credentials=True
SQL injection patterns: f-string interpolation, + concatenation, str.format(), and % operator
Command injection: os.system / os.popen / subprocess(..., shell=True)
Unsafe deserialization: pickle / marshal / shelve / yaml.load without a safe loader
Dynamic code execution: eval / exec (ast.literal_eval is excluded)

Code Quality Checks

Lines exceeding the configured maximum length (default: 120 characters, set via quality_rules.max_line_length)
Unused import detection via Python's ast, gated by a textual usage check to avoid false positives. __init__.py, from __future__ import, wildcard imports, and # noqa lines are exempt
Function complexity: not implemented (stub reserved for a cyclomatic complexity tool)

Dependency Auditing

Python: delegates to pip-audit when available
Node.js: delegates to npm audit when available
Fails loudly when the audit cannot run. A resolution error, timeout, or crash is reported as HIGH (DEP005 / DEP006) rather than silently passing, so "0 findings" never means "nothing was inspected"

Suppression

Intentionally vulnerable code (test fixtures, documented exceptions) can be exempted inline:

API_KEY = "sk-..."                       # code-validator: ignore
API_KEY = "sk-..."                       # code-validator: ignore[SEC001]
# code-validator: ignore-file[SEC004]    # whole file, selected rules
# code-validator: ignore-file            # whole file, all rules

File-level markers exist because some rules (CORS) are reported against the file rather than a single line.

Reporting

HTML: human-readable browser report with color-coded severity cards
JSON: machine-readable output for CI/CD integration
Console: summary printed to stdout with per-severity counts

Git Integration

--git-diff mode: scans only files changed since HEAD, keeping CI runs fast

Tech Stack

Component	Detail
Language	Python 3.9+
Core dependency	`pydantic == 2.13.4`
Optional	`pip-audit >= 2.6.0` (Python dep auditing)
CI	GitHub Actions

No external API calls. Runs fully offline.

Setup

# Clone or copy the repository
git clone https://github.com/TTMK7777/code-validator.git
cd code-validator

# Install dependencies (only pydantic is required)
pip install -r requirements.txt

# Optional: enable Python dependency auditing
pip install pip-audit

Python 3.9 or later is required.

Usage

Scan a directory

python validator.py --path /path/to/project

Scan only files changed in the latest commit (recommended for CI)

python validator.py --git-diff

Scan a specific commit range

python validator.py --git-diff --from HEAD~3 --to HEAD

Generate an HTML report

python validator.py --path . --output report.html --format html

Generate a JSON report

python validator.py --path . --output report.json --format json

Use a custom configuration file

python validator.py --path . --config config/validator_config.json

CLI reference

usage: validator.py [-h] [--path PATH] [--git-diff] [--output OUTPUT]
                    [--format {html,json}] [--config CONFIG]

optional arguments:
  --path PATH      Project path to scan (default: current directory)
  --git-diff       Scan only files changed since HEAD
  --output OUTPUT  Output file path for the report
  --format         Report format: html | json (default: html)
  --config CONFIG  Path to a custom JSON configuration file

Exit codes: 0 = no critical/high issues found, 1 = at least one critical or high issue detected (useful for blocking CI pipelines).

Configuration

Edit config/validator_config.json to customize behavior:

{
  "exclude_patterns": [
    "**/node_modules/**",
    "**/venv/**",
    "**/__pycache__/**",
    "**/.git/**"
  ],
  "file_extensions": [".py", ".js", ".ts", ".tsx", ".json", ".yaml", ".yml"],
  "security_rules": {
    "check_credentials": true,
    "check_cors": true,
    "check_sql_injection": true,
    "check_dangerous_calls": true
  },
  "quality_rules": {
    "max_line_length": 120,
    "check_unused_imports": true,
    "check_complex_functions": true
  },
  "dependency_rules": {
    "check_python": true,
    "check_node": true
  }
}

Key	Effect
`exclude_patterns`	Glob patterns excluded from scanning (matched against both the project-relative and absolute path)
`file_extensions`	Extensions collected when scanning a directory
`security_rules.check_credentials`	SEC001–SEC003, SEC008–SEC010
`security_rules.check_cors`	SEC004–SEC005
`security_rules.check_sql_injection`	SEC006
`security_rules.check_dangerous_calls`	SEC011–SEC013
`quality_rules.max_line_length`	QUAL001 threshold
`quality_rules.check_unused_imports`	QUAL002
`quality_rules.check_complex_functions`	Reserved; the check is a stub and emits nothing
`dependency_rules.check_python`	`pip-audit` delegation (DEP001, DEP002, DEP005)
`dependency_rules.check_node`	`npm audit` delegation (DEP003, DEP004, DEP006)

A config file is only applied when passed explicitly via --config. Without it, the defaults above are used.

CI/CD Integration

GitHub Actions

Add the following workflow to your repository (.github/workflows/code-validation.yml):

name: Code Validation

on:
  push:
    branches: [main, develop]
  pull_request:
    branches: [main, develop]

jobs:
  validate:
    runs-on: ubuntu-latest

    steps:
      - uses: actions/checkout@v4
        with:
          fetch-depth: 2  # required for --git-diff

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.11'

      - name: Install dependencies
        run: |
          pip install -r requirements.txt
          pip install pip-audit || echo "pip-audit not available"

      - name: Run Code Validator
        run: python validator.py --git-diff --output validation-report.json --format json

      - name: Upload validation report
        uses: actions/upload-artifact@v4
        if: always()
        with:
          name: validation-report
          path: validation-report.json

The validator exits with code 1 when critical or high severity issues are found, which automatically blocks the CI job.

GitLab CI

code-validation:
  image: python:3.11-slim
  script:
    - pip install -r requirements.txt
    - pip install pip-audit || true
    - python validator.py --git-diff --output validation-report.json --format json
  artifacts:
    paths:
      - validation-report.json
    when: always

Detection Rules

Rule ID	Severity	Category	Description
SEC001	Critical	Security	Hardcoded API key detected (OpenAI / Anthropic / Google / GitHub)
SEC002	Critical	Security	Hardcoded password detected
SEC003	Critical	Security	Hardcoded database credentials detected
SEC004	Critical	Security	CORS wildcard origins + credentials enabled
SEC005	High	Security	CORS wildcard origins (production risk)
SEC006	High	Security	Potential SQL injection via f-string, `+`, `.format()`, or `%`
SEC008	Critical	Security	Hardcoded `SECRET_KEY` (Django / Flask session signing)
SEC009	Critical	Security	Hardcoded AWS access key or secret access key
SEC010	Critical	Security	PEM private key embedded in source
SEC011	High	Security	Command injection via shell execution
SEC012	High	Security	Unsafe deserialization (`pickle` / `marshal` / `yaml.load`)
SEC013	High	Security	Dynamic code execution (`eval` / `exec`)
QUAL001	Low	Quality	Line exceeds maximum length
QUAL002	Low	Quality	Unused import
DEP001	Info	Dependencies	`pip-audit` not installed
DEP002	High	Dependencies	Python package with known CVE
DEP003	Variable	Dependencies	Node.js package with known CVE
DEP004	Info	Dependencies	`npm` not installed
DEP005	High	Dependencies	`pip-audit` could not run — dependency audit did not happen
DEP006	High	Dependencies	`npm audit` could not run — dependency audit did not happen

On DEP005 / DEP006: these are deliberately HIGH, which fails CI. An audit that could not execute is not the same as an audit that found nothing — treating it as INFO (the previous behavior) let unresolvable dependency trees pass as green.

Example Output

============================================================
Validation Summary
============================================================
Project: /home/user/my-project
Files scanned: 42
Execution time: 0.83s

Issues by severity:
  Critical : 0
  High     : 1
  Medium   : 2
  Low      : 5
  Info     : 1
============================================================

How code-validator Compares

Tool	Target	Speed	Offline	AI-code focus	Dependency-free
code-validator	AI-generated code in CI	<1s/file	✅ Yes	✅ Yes (purpose-built)	✅ pydantic only
Bandit	General Python	Fast	✅ Yes	❌ No	❌ Multiple deps
Semgrep	Multi-language patterns	Medium	⚠️ Hybrid	❌ No	❌ Heavy
GitGuardian	Secrets in git history	Slow (API)	❌ No	❌ No	❌ SaaS
TruffleHog	Secrets in git history	Slow	✅ Yes	❌ No	❌ Multiple deps

Positioning: code-validator is the only tool in this list specifically tuned for the failure modes of AI-generated code (e.g., the CORS wildcard + allow_credentials=True pattern that LLMs disproportionately emit, or eval on model-produced strings).

Scope honesty: this is a line-oriented pattern scanner, not a dataflow analyzer. It does not perform taint tracking, so it flags shapes known to be risky rather than proving a path from untrusted input to a sink. For deep dataflow analysis, pair it with Semgrep or CodeQL — the point of this tool is a sub-second gate, not a replacement.

FAQ

Q: Why a separate tool for AI-generated code? Can't I just use Bandit or Semgrep?

General-purpose linters were designed for human-written code. AI assistants exhibit specific failure modes — hardcoded example credentials, overly permissive CORS for demos, string-concat SQL because the model "remembered" pre-ORM patterns. code-validator's rule set is tuned for these patterns and weights severity accordingly.

Q: Does code-validator send my code anywhere?

No. The scanner runs fully offline. The only optional network call is pip-audit for CVE lookups, and that contacts only the official PyPA advisory database — never your source code.

Q: How is this different from running Bandit + `truffleHog` + `pip-audit` separately?

code-validator unifies them into a single CI step with a coherent severity model and one report format (HTML / JSON). For --git-diff mode, only files changed in the current PR are scanned, keeping CI fast.

Q: Can I customize the detection rules?

Yes — see config/validator_config.json, and pass it with --config. You can disable rule categories, change the line-length threshold, and add exclude patterns. Every key in that file is honored; see the table in Configuration.

Q: How do I exempt intentionally vulnerable code?

Use an inline # code-validator: ignore comment — see Suppression. This is how this repository's own test fixtures pass the gate.

Q: What happens if `pip-audit` fails to run?

You get a HIGH finding (DEP005), which fails CI. This is deliberate: an audit that could not execute must not be reported as green.

Q: Does it work with Claude Code, Cursor, GitHub Copilot output?

Yes. It scans the resulting source files regardless of which AI assistant generated them. The detection patterns target the output, not the tool.

Q: What Python version do I need?

Python 3.9 or later.

Q: Is there a pre-commit hook?

Use python validator.py --git-diff in a pre-commit hook — exit code 1 blocks the commit when critical/high issues are found.

Author

Built by Taimu Tsuji (辻大夢) — Founder of Tsuji Lab, Applied AI Architect specializing in multi-agent AI coordination and AI-assisted software development at scale.

Experience: ~1.4M lines of AI-assisted code shipped across 4 concurrent projects; ~8,000 engineering hours saved per year via Claude Code automation.
Expertise: Claude Agent SDK, MCP, multi-agent orchestration, AI security gating.
Why I built this: After watching AI-generated PRs land with allow_origins=["*"] more than once, I needed a gate that ran in <1s and didn't ship code off-box. Existing tools were either too heavy, too noisy, or required SaaS.

GitHub: @TTMK7777

Structured Data (Schema.org)

For AI search engines and developer-tooling indexes:

{
  "@context": "https://schema.org",
  "@type": "SoftwareApplication",
  "name": "code-validator",
  "alternateName": "AI Code Security Scanner",
  "applicationCategory": "DeveloperApplication",
  "operatingSystem": "Linux, macOS, Windows",
  "description": "Static security scanner for AI-generated code. Detects hardcoded credentials, CORS misconfigurations, SQL injection patterns, and dependency CVEs in under 1 second per file, fully offline.",
  "url": "https://github.com/TTMK7777/code-validator",
  "license": "https://opensource.org/licenses/MIT",
  "programmingLanguage": "Python",
  "softwareRequirements": "Python 3.9+",
  "author": {
    "@type": "Person",
    "name": "Taimu Tsuji",
    "alternateName": "辻大夢",
    "jobTitle": "Founder, Tsuji Lab",
    "url": "https://github.com/TTMK7777"
  },
  "keywords": "AI security, static analysis, code validation, AI-generated code, CI/CD security, secret detection, CORS validation, dependency audit, Claude Code, GitHub Copilot, LLM security"
}

License

MIT License. See LICENSE for details.

Contributing

Issues and PRs welcome. For security-related findings, see SECURITY.md.

Name		Name	Last commit message	Last commit date
.github		.github
config		config
docs		docs
specs		specs
src		src
tests		tests
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
CLAUDE.md		CLAUDE.md
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
QUICKSTART.md		QUICKSTART.md
README.md		README.md
SECURITY.md		SECURITY.md
llms.txt		llms.txt
plan.md		plan.md
requirements.txt		requirements.txt
spec.md		spec.md
validator.py		validator.py

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code-validator

Features

Security Scanning

Code Quality Checks

Dependency Auditing

Suppression

Reporting

Git Integration

Tech Stack

Setup

Usage

Scan a directory

Scan only files changed in the latest commit (recommended for CI)

Scan a specific commit range

Generate an HTML report

Generate a JSON report

Use a custom configuration file

CLI reference

Configuration

CI/CD Integration

GitHub Actions

GitLab CI

Detection Rules

Example Output

How code-validator Compares

FAQ

Q: Why a separate tool for AI-generated code? Can't I just use Bandit or Semgrep?

Q: Does code-validator send my code anywhere?

Q: How is this different from running Bandit + truffleHog + pip-audit separately?

Q: Can I customize the detection rules?

Q: How do I exempt intentionally vulnerable code?

Q: What happens if pip-audit fails to run?

Q: Does it work with Claude Code, Cursor, GitHub Copilot output?

Q: What Python version do I need?

Q: Is there a pre-commit hook?

Author

Structured Data (Schema.org)

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Q: How is this different from running Bandit + `truffleHog` + `pip-audit` separately?

Q: What happens if `pip-audit` fails to run?