Building a api / backend service with Python? Let us review it.
Expert code review for api / backend services built with Python. We fix Python-specific security gaps, optimize performance, and handle deployment. From $19.
Common Python issues we find
Real problems from Python codebases we've reviewed.
Django debug mode in production
DEBUG=True left enabled in production, exposing stack traces, database queries, and configuration to attackers.
Missing CSRF protection
CSRF middleware disabled or bypassed for convenience, allowing cross-site request forgery attacks.
Insecure deserialization
Using pickle or yaml.load with untrusted data, enabling remote code execution.
Slow database queries
ORM queries that generate inefficient SQL, N+1 query patterns, and missing database indexes.
API / Backend Service challenges to solve
Key api / backend service concerns that AI-generated code often misses.
Authentication and API keys
Every endpoint needs to verify the caller's identity. AI tools create endpoints without auth, or with auth that's easy to bypass. You need token-based auth, API key management, and proper session handling.
Input validation
Every parameter, request body, and header value must be validated before use. AI-generated APIs trust client data, which leads to injection attacks, data corruption, and crashes from unexpected input.
Rate limiting and abuse prevention
Without rate limits, anyone can hammer your API — brute-forcing passwords, scraping data, or running up your infrastructure costs. Rate limiting must be per-user and per-endpoint.
Error handling and status codes
APIs should return appropriate HTTP status codes (400 for bad input, 401 for unauthorized, 404 for not found, 500 for server errors) with helpful error messages. AI tools often return 200 for everything or expose internal error details.
What we check
Key areas we review for Python api / backend service projects.
Authentication on every endpoint — no unprotected routes
Input validation — every parameter validated with schema
Rate limiting — per-user, per-endpoint limits configured
Error responses — correct status codes, no internal detail leaks
Not sure if your app passes? Our code audit ($19) checks all of these and more.
Start with a self-serve audit
Get a professional review of your Python api / backend service project at a fixed price.
External Security Scan
Black-box review of your public-facing app. No code access needed.
- OWASP Top 10 vulnerability check
- SSL/TLS configuration analysis
- Security header assessment
- Expert review within 24h
Code Audit
In-depth review of your source code for security, quality, and best practices.
- Security vulnerability analysis
- Code quality review
- Dependency audit
- Architecture review
- Expert + AI code analysis
Complete Bundle
Both scans in one package with cross-referenced findings.
- Everything in both products
- Cross-referenced findings
- Unified action plan
100% credited toward any paid service. Start with an audit, then let us fix what we find.
How it works
Tell us about your app
Share your project details and what you need help with.
Expert + AI audit
A human expert assisted by AI reviews your code within 24 hours.
Launch with confidence
We fix what needs fixing and stick around to help.
Frequently asked questions
Can you review a api / backend service built with Python?
Yes. We regularly audit Python api / backend service projects and understand the specific patterns and pitfalls of this combination. Our review covers security, performance, and deployment readiness.
What issues do you find in Python api / backend services?
Common issues include django debug mode in production and missing csrf protection on the Python side, combined with api / backend service-specific concerns like authentication and api keys and input validation. We check for all of these and more.
How do I make my Python api / backend service production-ready?
Start with our code audit ($19) to get a prioritized list of issues. For Python api / backend service projects, the typical path is: fix security gaps, address api / backend service-specific requirements, optimize Python performance, then configure deployment. We provide a fixed quote after the audit.
How long does it take to audit a Python api / backend service?
Our code audit delivers a full report within 24 hours. For Python api / backend service projects, we check security, architecture, performance, and deployment readiness across all Python-specific patterns. Fixes are scoped separately with a fixed quote.
Related resources
Python by Use Case
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