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Py Security

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l-mb
py-security

Security vulnerability detection and remediation for Python codebases. Identifies SQL injection, hardcoded secrets, weak cryptography, and other OWASP vulnerabilities.

Overview

Publisherl-mb
Repositorypython-refactoring-skills
Skill namepy-security
Stars
57
Forks
1
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by l-mb on GitHub. Read the source before you install it.

Installation

Install the Py Security AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/l-mb/python-refactoring-skills/tree/main/skills/py-security
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/l-mb/python-refactoring-skills.git /tmp/python-refactoring-skills
mkdir -p .claude/skills
cp -r /tmp/python-refactoring-skills/skills/py-security .claude/skills/py-security
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Py Security in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Py Security on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Py Security is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Python Security Analysis and Remediation

Find and fix security vulnerabilities in Python code following Engineering Charter security principles.

Objectives

  1. Detect security vulnerabilities using automated scanners
  2. Identify SQL injection risks
  3. Find hardcoded secrets and credentials
  4. Detect weak cryptographic practices
  5. Fix vulnerabilities following secure coding patterns
  6. Prevent secrets from being committed to git

Required Tools

Add to [dependency-groups] dev: "bandit", "ruff"

  • bandit: AST-based security scanner
  • ruff --select S: Built-in Bandit rules (faster alternative)

Permissions: Run py-quality-setup first to configure .claude/settings.local.json with all needed tool permissions.

Discovery Phase

Run Security Scanners

bash
# Using bandit (comprehensive reports)
bandit -r . -f json -o bandit_report.json
bandit -r . -ll  # Show only medium/high severity

# Using ruff (faster, integrates with existing tooling)
ruff check . --select S
ruff check . --select S --output-format=json > security_report.json

Common Vulnerability Categories

High severity (fix immediately):

  • S608: SQL injection via string formatting
  • S105, S106, S107: Hardcoded passwords, secrets, keys
  • S501, S506: Weak SSL/TLS configuration
  • S324: Insecure hash functions (MD5, SHA1)
  • S602, S603, S604, S605, S606, S607: Shell injection risks

Medium severity:

  • S311: Insecure random number generation
  • S301, S302, S303: Insecure deserialization (pickle)
  • S701, S702: Jinja2 autoescape issues

Low severity (consider context):

  • S101: Use of assert (disabled in production)
  • S104: Binding to all interfaces (0.0.0.0)

Remediation Patterns

SQL Injection Prevention

python
# BEFORE - Vulnerable to SQL injection
def get_user(user_id: str) -> dict:
    cursor.execute(f"SELECT * FROM users WHERE id = {user_id}")
    return cursor.fetchone()

# AFTER - Use parametrized queries
def get_user(user_id: str) -> dict:
    cursor.execute("SELECT * FROM users WHERE id = %s", (user_id,))
    return cursor.fetchone()

# For dynamic table/column names (cannot be parametrized)
from psycopg2 import sql

def get_data(table_name: str, column: str) -> list:
    # Validate inputs first!
    allowed_tables = {"users", "orders", "products"}
    if table_name not in allowed_tables:
        raise ValueError(f"Invalid table: {table_name}")

    query = sql.SQL("SELECT {column} FROM {table}").format(
        column=sql.Identifier(column),
        table=sql.Identifier(table_name)
    )
    cursor.execute(query)
    return cursor.fetchall()

Hardcoded Secrets Removal

python
# BEFORE - Hardcoded secrets (NEVER commit these)
API_KEY = "sk-1234567890abcdef"
DATABASE_URL = "postgres://user:password@localhost/db"
SECRET_TOKEN = "my-secret-token-123"

# AFTER - Use environment variables
import os

API_KEY = os.environ["API_KEY"]  # Raises if not set
DATABASE_URL = os.getenv("DATABASE_URL", "postgres://localhost/db")  # With default
SECRET_TOKEN = os.environ.get("SECRET_TOKEN")  # Returns None if not set

# For development, use python-dotenv
from dotenv import load_dotenv
load_dotenv()  # Loads from .env file (add .env to .gitignore!)
API_KEY = os.environ["API_KEY"]

Secure Random Number Generation

python
# BEFORE - Insecure for security-sensitive operations
import random

session_token = random.randint(100000, 999999)
api_key = ''.join(random.choices('abcdef0123456789', k=32))

# AFTER - Use secrets module for cryptographic randomness
import secrets

session_token = secrets.randbelow(900000) + 100000
api_key = secrets.token_hex(16)  # 32 hex characters
secure_password = secrets.token_urlsafe(32)  # URL-safe token

Secure Cryptography

python
# BEFORE - Weak hash functions
import hashlib

password_hash = hashlib.md5(password.encode()).hexdigest()  # Weak!
checksum = hashlib.sha1(data).hexdigest()  # Weak for security!

# AFTER - Use strong hash functions
import hashlib

# For passwords - use bcrypt or argon2
from argon2 import PasswordHasher
ph = PasswordHasher()
password_hash = ph.hash(password)
ph.verify(password_hash, password)  # Verify

# For data integrity - use SHA256 or better
checksum = hashlib.sha256(data).hexdigest()
secure_hash = hashlib.blake2b(data).hexdigest()

Shell Command Injection Prevention

python
# BEFORE - Shell injection vulnerability
import os
import subprocess

filename = user_input
os.system(f"cat {filename}")  # Dangerous!
subprocess.call(f"ls {directory}", shell=True)  # Dangerous!

# AFTER - Use subprocess with list arguments (no shell)
import subprocess
from pathlib import Path

# Validate input first
filename = user_input
if not Path(filename).exists():
    raise ValueError("File not found")

# Use list form, no shell=True
subprocess.run(["cat", filename], check=True)
subprocess.run(["ls", directory], check=True)

# Even better - use Python libraries instead of shell commands
with open(filename) as f:
    content = f.read()

Git Hooks Integration

Add secret detection to git hooks (complement to py-git-hooks):

bash
# In .git/hooks/pre-commit, add before other checks:

# Detect potential secrets
if git diff --cached | grep -qiE '(password|secret|api[_-]?key|token|credentials)'; then
    echo "⚠️  Warning: Potential secrets detected in staged changes"
    echo "Review carefully before committing"
    read -p "Continue? (y/n) " -n 1 -r
    echo
    if [[ ! $REPLY =~ ^[Yy]$ ]]; then
        exit 1
    fi
fi

Verification Checklist

  • ruff check . --select S reports no security issues
  • bandit -r . -ll reports no medium/high severity issues
  • No hardcoded secrets in codebase (grep for password, secret, api_key, token)
  • Secrets loaded from environment variables or .env file
  • .env file is in .gitignore
  • All SQL queries use parametrized queries
  • All tests pass after security fixes

Examples

Example: Security audit workflow

1. Scan: ruff check . --select S; bandit -r . -ll
2. Found: SQL injection (S608), hardcoded secret (S105)
3. Fix SQL: Use parametrized query with %s placeholders
4. Fix secret: Move to .env, use os.environ["API_KEY"]
5. Verify: Scans clean, tests pass

Related Skills

  • Prerequisites: py-quality-setup (configure ruff for security checks)
  • Enforcement: py-git-hooks (add security scanning to pre-commit)
  • See also: py-code-health (cleanup after security fixes)

Frequently asked questions

What does the Py Security AI skill do?

Security vulnerability detection and remediation for Python codebases. Identifies SQL injection, hardcoded secrets, weak cryptography, and other OWASP vulnerabilities.

Why use Py Security on TypingMind?

Because you install it once and use it with any model. Py Security is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Py Security in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/l-mb/python-refactoring-skills/tree/main/skills/py-security. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Py Security?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Py Security?

As many as you like. As long as a model supports skills, you can use Py Security with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Py Security AI skill free?

Yes. It is published on GitHub by l-mb under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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