Now liveThe Skillselion MCP - thousands of ranked skills, loaded into your agent mid-task. No install.Get it →
hieutrtr avatar

Code Review Security

  • 399 installs
  • 8 repo stars
  • Updated February 6, 2026
  • hieutrtr/ai1-skills

code-review-security is an agent skill that runs AST-based Python security scans before merge or release for developers who need to catch eval/exec, injection, and hardcoded secret patterns with severitized JSON output.

About

code-review-security is an agent skill from hieutrtr/ai1-skills built around security-scan.py, an AST-based scanner for common Python vulnerability patterns. The script scans directories for eval(), exec(), compile(), subprocess with shell=True, pickle.loads() on untrusted data, raw SQL f-string construction, yaml.load() without SafeLoader, hardcoded API keys and passwords, weak MD5/SHA1 password hashes, and os.system() calls. Run with `python security-scan.py --path ./app --output-dir ./security-results` and optional `--severity high` filtering. Developers reach for code-review-security before merge when Python services need automated secret and injection detection with JSON severity output.

  • AST-based security-scan.py for Python codebases
  • Detects eval/exec/compile, subprocess shell=True, and os.system usage
  • Flags pickle.loads, unsafe yaml.load, and raw SQL f-string construction
  • Hardcoded secret and weak hash (MD5/SHA1) pattern rules with CWE metadata
  • CLI emits JSON findings with configurable minimum severity filter

Code Review Security by the numbers

  • 399 all-time installs (skills.sh)
  • +4 installs in the week ending Aug 2, 2026 (Skillselion tracking)
  • Ranked #557 of 2,203 Security skills by installs in the Skillselion catalog
  • Security screen: HIGH risk (skills.sh audit)
  • Data as of Aug 2, 2026 (Skillselion catalog sync)
npx skills add https://github.com/hieutrtr/ai1-skills --skill code-review-security

Add your badge

Show developers this skill is listed on Skillselion. Paste this into your README.

Listed on Skillselion
Installs399
repo stars8
Security audit2 / 3 scanners passed
Last updatedFebruary 6, 2026
Repositoryhieutrtr/ai1-skills

How do you scan Python code for security vulnerabilities?

Run AST-based Python security scans before merge or release to catch eval/exec, injection, and secret patterns with severitized JSON output.

Who is it for?

Python backend developers who need AST-based pre-merge security scans with severitized JSON output before release.

Skip if: Non-Python codebases or teams needing full DAST or infrastructure penetration testing should skip code-review-security.

When should I use this skill?

A developer wants to security-scan Python source before merge, filter findings by severity, or detect hardcoded secrets and injection patterns.

What you get

Severitized JSON security scan results listing eval/exec, injection, secret, and weak-hash findings across scanned Python files.

  • Severitized JSON scan report
  • Per-file vulnerability findings

By the numbers

  • Scans 9 Python vulnerability pattern categories including eval/exec, subprocess shell=True, and hardcoded secrets
  • Supports --severity filtering and JSON output via security-scan.py CLI flags

Files

SKILL.mdMarkdownGitHub ↗

Code Review Security

When to Use

Activate this skill when:

  • Reviewing pull requests for security vulnerabilities
  • Auditing authentication or authorization code changes
  • Reviewing code that handles user input, file uploads, or external data
  • Checking for OWASP Top 10 vulnerabilities in new features
  • Validating that secrets are not committed to the repository
  • Scanning dependencies for known vulnerabilities
  • Reviewing API endpoints that expose sensitive data

Output: Write findings to security-review.md with severity, file:line, description, and recommendations.

Do NOT use this skill for:

  • Deployment infrastructure security (use docker-best-practices)
  • Incident response procedures (use incident-response)
  • General code quality review without security focus (use pre-merge-checklist)
  • Writing implementation code (use python-backend-expert or react-frontend-expert)

Instructions

OWASP Top 10 Checklist

Review every PR against the OWASP Top 10 (2021 edition). Each category below includes specific checks for Python/FastAPI and React codebases.

---

A01: Broken Access Control

What to look for:

  • Missing authorization checks on endpoints
  • Direct object reference without ownership verification
  • Endpoints that expose data without role-based filtering
  • Missing Depends() for auth on new routes

Python/FastAPI checks:

# BAD: No authorization check -- any authenticated user can access any user
@router.get("/users/{user_id}")
async def get_user(user_id: int, db: Session = Depends(get_db)):
    return await user_repo.get(user_id)

# GOOD: Verify the requesting user owns the resource or is admin
@router.get("/users/{user_id}")
async def get_user(
    user_id: int,
    current_user: User = Depends(get_current_user),
    db: Session = Depends(get_db),
):
    if current_user.id != user_id and current_user.role != "admin":
        raise HTTPException(status_code=403, detail="Forbidden")
    return await user_repo.get(user_id)

Review checklist:

  • [ ] Every route has authentication (Depends(get_current_user))
  • [ ] Resource access is verified against the requesting user
  • [ ] Admin-only endpoints check role == "admin"
  • [ ] List endpoints filter by user ownership (unless admin)
  • [ ] No IDOR (Insecure Direct Object Reference) vulnerabilities

---

A02: Cryptographic Failures

What to look for:

  • Passwords stored in plaintext or with weak hashing
  • Sensitive data in logs or error messages
  • Hardcoded secrets, API keys, or tokens
  • Weak JWT configuration

Python checks:

# BAD: Weak password hashing
import hashlib
password_hash = hashlib.md5(password.encode()).hexdigest()

# GOOD: Use bcrypt via passlib
from passlib.context import CryptContext
pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto")
password_hash = pwd_context.hash(password)

# BAD: Secret in code
SECRET_KEY = "my-super-secret-key-123"

# GOOD: Secret from environment
SECRET_KEY = os.environ["SECRET_KEY"]

Review checklist:

  • [ ] Passwords hashed with bcrypt (never MD5, SHA1, or plaintext)
  • [ ] JWT secret loaded from environment, not hardcoded
  • [ ] Sensitive data excluded from logs (passwords, tokens, PII)
  • [ ] HTTPS enforced for all external communication
  • [ ] No secrets in source code (check .env.example has placeholders only)

---

A03: Injection

What to look for:

  • Raw SQL queries with string interpolation
  • eval(), exec(), compile() with user input
  • subprocess calls with shell=True
  • Template injection

Python checks:

# BAD: SQL injection via string formatting
query = f"SELECT * FROM users WHERE email = '{email}'"
db.execute(text(query))

# GOOD: Parameterized query
db.execute(text("SELECT * FROM users WHERE email = :email"), {"email": email})

# GOOD: SQLAlchemy ORM (always parameterized)
user = db.query(User).filter(User.email == email).first()

# BAD: Command injection
subprocess.run(f"convert {filename}", shell=True)

# GOOD: Pass arguments as a list
subprocess.run(["convert", filename], shell=False)

# BAD: Code execution with user input
result = eval(user_input)

# GOOD: Never eval user input. Use ast.literal_eval for safe parsing.
result = ast.literal_eval(user_input)  # Only for literal structures

Review checklist:

  • [ ] No raw SQL with string interpolation (use ORM or parameterized queries)
  • [ ] No eval(), exec(), or compile() with external input
  • [ ] No subprocess.run(..., shell=True) with dynamic arguments
  • [ ] No pickle.loads() on untrusted data
  • [ ] All user input validated by Pydantic schemas before use

---

A04: Insecure Design

What to look for:

  • Missing rate limiting on authentication endpoints
  • No account lockout after failed login attempts
  • Missing CAPTCHA on public-facing forms
  • Business logic flaws (e.g., negative amounts, self-privilege-escalation)

Review checklist:

  • [ ] Rate limiting on login, registration, and password reset
  • [ ] Account lockout or exponential backoff after 5+ failed attempts
  • [ ] Business logic validates constraints (positive amounts, valid transitions)
  • [ ] Sensitive operations require re-authentication

---

A05: Security Misconfiguration

What to look for:

  • Debug mode enabled in production
  • CORS configured with wildcard * origins
  • Default credentials or admin accounts
  • Verbose error messages exposing stack traces

Python/FastAPI checks:

# BAD: Wide-open CORS
app.add_middleware(CORSMiddleware, allow_origins=["*"])

# GOOD: Explicit allowed origins
app.add_middleware(
    CORSMiddleware,
    allow_origins=["https://app.example.com"],
    allow_methods=["GET", "POST", "PUT", "DELETE"],
    allow_headers=["Authorization", "Content-Type"],
)

# BAD: Debug mode in production
app = FastAPI(debug=True)

# GOOD: Debug only in development
app = FastAPI(debug=settings.DEBUG)  # DEBUG=False in production

Review checklist:

  • [ ] CORS origins are explicit (no wildcard in production)
  • [ ] Debug mode disabled in production configuration
  • [ ] Error responses do not expose stack traces or internal details
  • [ ] Default admin credentials are changed or removed
  • [ ] Security headers set (X-Content-Type-Options, X-Frame-Options, etc.)

---

A06: Vulnerable and Outdated Components

Review checklist:

  • [ ] No known CVEs in Python dependencies (pip-audit or safety check)
  • [ ] No known CVEs in npm dependencies (npm audit)
  • [ ] Dependencies pinned to specific versions in lock files
  • [ ] No deprecated packages still in use

---

A07: Identification and Authentication Failures

What to look for:

  • Weak password policies
  • Session tokens that do not expire
  • Missing multi-factor authentication for admin actions
  • JWT tokens without expiration

Python checks:

# BAD: JWT without expiration
token = jwt.encode({"sub": user_id}, SECRET_KEY, algorithm="HS256")

# GOOD: JWT with expiration
token = jwt.encode(
    {"sub": user_id, "exp": datetime.utcnow() + timedelta(minutes=30)},
    SECRET_KEY,
    algorithm="HS256",
)

Review checklist:

  • [ ] JWT tokens have expiration (exp claim)
  • [ ] Refresh tokens are stored securely and can be revoked
  • [ ] Password policy enforces minimum length (12+) and complexity
  • [ ] Session invalidation on password change or logout
  • [ ] No user enumeration via login error messages

---

A08: Software and Data Integrity Failures

Review checklist:

  • [ ] CI/CD pipeline validates artifact integrity
  • [ ] No unsigned or unverified packages
  • [ ] Deserialization of untrusted data uses safe methods (no pickle.loads)
  • [ ] Database migrations are reviewed before execution

---

A09: Security Logging and Monitoring Failures

Review checklist:

  • [ ] Authentication events are logged (login, logout, failed attempts)
  • [ ] Authorization failures are logged with context
  • [ ] Sensitive data is NOT included in logs (passwords, tokens, PII)
  • [ ] Log entries include timestamp, user ID, IP address, action
  • [ ] Alerting configured for suspicious patterns (brute force, unusual access)

---

A10: Server-Side Request Forgery (SSRF)

What to look for:

  • User-supplied URLs used in server-side requests
  • Redirect endpoints that accept arbitrary URLs

Python checks:

# BAD: Fetch arbitrary URL from user input
url = request.query_params["url"]
response = httpx.get(url)  # SSRF: can access internal services

# GOOD: Validate URL against allowlist
ALLOWED_HOSTS = {"api.example.com", "cdn.example.com"}
parsed = urlparse(url)
if parsed.hostname not in ALLOWED_HOSTS:
    raise HTTPException(400, "URL not allowed")
response = httpx.get(url)

Review checklist:

  • [ ] No server-side requests to user-controlled URLs without validation
  • [ ] URL allowlists used for external integrations
  • [ ] Internal service URLs not exposed in error messages

---

Python-Specific Security Checks

Beyond OWASP, review Python code for these patterns:

PatternRiskFix
eval(user_input)Remote code executionRemove or use ast.literal_eval
pickle.loads(data)Arbitrary code executionUse JSON or msgpack
subprocess.run(cmd, shell=True)Command injectionPass args as list, shell=False
yaml.load(data)Code executionUse yaml.safe_load(data)
os.system(cmd)Command injectionUse subprocess.run([...])
Raw SQL stringsSQL injectionUse ORM or parameterized queries
hashlib.md5(password)Weak hashingUse bcrypt via passlib
jwt.decode(token, options={"verify_signature": False})Auth bypassAlways verify signature
open(user_path)Path traversalValidate path, use pathlib.resolve()
tempfile.mktemp()Race conditionUse tempfile.mkstemp()

React-Specific Security Checks

PatternRiskFix
dangerouslySetInnerHTMLXSSUse text content or sanitize with DOMPurify
javascript: in hrefXSSValidate URLs, allow only https:
window.location = userInputOpen redirectValidate against allowlist
Storing tokens in localStorageToken theft via XSSUse httpOnly cookies
Inline event handlers from dataXSSUse React event handlers
eval() or Function()Code executionRemove entirely
Rendering user HTMLXSSUse a sanitization library

React code review:

// BAD: XSS via dangerouslySetInnerHTML
<div dangerouslySetInnerHTML={{ __html: userBio }} />

// GOOD: Sanitize first, or use text content
import DOMPurify from "dompurify";
<div dangerouslySetInnerHTML={{ __html: DOMPurify.sanitize(userBio) }} />

// BETTER: Use text content when HTML is not needed
<p>{userBio}</p>

// BAD: javascript: URL
<a href={userLink}>Click</a>  // userLink could be "javascript:alert(1)"

// GOOD: Validate protocol
const safeHref = /^https?:\/\//.test(userLink) ? userLink : "#";
<a href={safeHref}>Click</a>

Severity Classification

Classify each finding by severity for prioritization:

SeverityDescriptionExamplesSLA
CriticalExploitable remotely, no auth needed, data breachSQL injection, RCE, auth bypassBlock merge, fix immediately
HighExploitable with auth, privilege escalationIDOR, broken access control, XSS (stored)Block merge, fix before release
MediumRequires specific conditions to exploitCSRF, XSS (reflected), open redirectFix within sprint
LowDefense-in-depth, informationalMissing headers, verbose errorsFix when convenient
InfoBest practice recommendationsDependency updates, code styleTrack in backlog

Finding Report Format

When reporting security findings, use this format for consistency:

## Security Finding: [Title]

**Severity:** Critical | High | Medium | Low | Info
**Category:** OWASP A01-A10 or custom category
**File:** path/to/file.py:42
**CWE:** CWE-89 (if applicable)

### Description
Brief description of the vulnerability and its impact.

### Vulnerable Code

The problematic code

vulnerable_function(user_input)


### Recommended Fix

The secure alternative

safe_function(sanitize(user_input))


### Impact
What an attacker could achieve by exploiting this vulnerability.

### References
- Link to relevant OWASP page
- Link to relevant CWE entry

Automated Scanning

Use scripts/security-scan.py to perform AST-based scanning for common vulnerability patterns in Python code. The script scans for:

  • eval() / exec() / compile() calls
  • subprocess with shell=True
  • pickle.loads() on potentially untrusted data
  • Raw SQL string construction
  • yaml.load() without Loader=SafeLoader
  • Hardcoded secret patterns (API keys, passwords)
  • Weak hash functions (MD5, SHA1 for passwords)

Run: python scripts/security-scan.py --path ./app --output-dir ./security-results

Dependency scanning (run separately):

# Python dependencies
pip-audit --requirement requirements.txt --output json > dep-audit.json

# npm dependencies
npm audit --json > npm-audit.json

Examples

Example Review Comment (Critical)

SECURITY: SQL Injection (Critical, OWASP A03)

>

File: app/repositories/user_repository.py:47

>

```python
query = f"SELECT * FROM users WHERE name LIKE '%{search_term}%'"
```

>

This constructs a raw SQL query with string interpolation, allowing SQL injection.
An attacker could input '; DROP TABLE users; -- to destroy data.

>

Fix: Use SQLAlchemy ORM filtering:
```python
users = db.query(User).filter(User.name.ilike(f"%{search_term}%")).all()
```

Example Review Comment (Medium)

SECURITY: Missing Rate Limiting (Medium, OWASP A04)

>

File: app/routes/auth.py:12

>

The /auth/login endpoint has no rate limiting. An attacker could perform brute-force
password attacks at unlimited speed.

>

Fix: Add rate limiting middleware:
```python
from slowapi import Limiter
limiter = Limiter(key_func=get_remote_address)

>

@router.post("/login")
@limiter.limit("5/minute")
async def login(request: Request, ...):
```

Output File

Write security findings to security-review.md:

# Security Review: [Feature/PR Name]

## Summary
- Critical: 0 | High: 1 | Medium: 2 | Low: 1

## Findings

### [CRITICAL] SQL Injection in user search
- **File:** app/routes/users.py:45
- **OWASP:** A03 Injection
- **Description:** Raw SQL with string interpolation
- **Recommendation:** Use SQLAlchemy ORM filtering

### [HIGH] Missing authorization check
...

## Passed Checks
- No hardcoded secrets found
- Dependencies up to date

Related skills

How it compares

Pick code-review-security over generic linters when you need Python-specific AST secret and injection detection with severitized JSON—not style or type checks.

FAQ

What vulnerability patterns does code-review-security detect?

code-review-security detects eval/exec/compile, subprocess shell=True, unsafe pickle and yaml.load, raw SQL f-strings, hardcoded secrets, weak MD5/SHA1 password hashes, and os.system() calls in Python source via AST analysis.

How do you run the code-review-security scanner?

code-review-security runs `python security-scan.py --path ./app --output-dir ./security-results`. Add `--severity high` to filter findings. Results are written as severitized JSON files in the output directory.

Is Code Review Security safe to install?

skills.sh reports 2 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.

Securityappsecaudit

This week in AI coding

Five minutes, every Monday - the tools, releases and tactics for developers.

unsubscribe anytime.