Last updated on October 11th, 2023
Python powers many mission-critical systems, so securing Python code and apps is crucial. In this beginner’s guide, we’ll explore core Python security principles, common vulnerabilities, and best practices for writing more secure Python code.
We’ll cover essential security measures like input validation, authentication, access controls and cryptography without assuming prior security experience. Our goal is to provide the foundation for creating robust and safe Python applications.
Why Python Security Matters
With Python’s rise in popularity, it now runs many sensitive systems:
- Web applications and APIs
- Backend infrastructure and services
- Scientific computing and machine learning
- Embedded systems and IoT
This means malicious actors increasingly target Python apps for exploits. Lax security opens the door to damaging data breaches, service outages, regulatory infractions, and more.
Thankfully, Python provides many tools to reduce risks and limit impacts of compromise. Let’s get started securing Python apps the right way.
Core Python Security Principles
Below are fundamental security principles to follow:
- Least privilege – Limit permissions and access to only what is strictly needed. Use roles and scopes judiciously.
- Separation of concerns – Isolate components so compromise in one area is contained. Separate production and development resources.
- Fail safely – When failures occur, fail without catastrophic side effects or exposures of data.
- Input validation – Never trust inputs. Validate and sanitize all data.
- Principle of least astonishment – Design components and UIs carefully to avoid surprising behaviors that could confuse users or lead to misuse.
- Defense in depth – Use layers of overlapping controls and safeguards against threats.
- Simplicity – Minimize complex code that is harder to validate and audit. When possible, rely on well-vetted frameworks and libraries with security baked in.
Adopting these fundamental principles will steer you towards more secure Python code.
Common Python Security Vulnerabilities
Below are some common Python-specific vulnerabilities and exposures to be aware of:
- Injection attacks – Inserting malicious commands and data into inputs passed to interpreters. Use validation and escaping.
- Broken authentication – Not properly validating credentials or poor session management enabling account takeovers. Enforce strong authentication.
- Sensitive data exposure – Cacheing or logging sensitive data in cleartext enabling exfiltration. Follow data security best practices.
- XML external entity attacks – Malicious XML data accesses local file contents. Use safe parsers.
- Insecure deserialization – Converting untrusted data into objects leading to remote code execution. Validate inputs.
Many common app security risks have Python-specific variants developers must remain vigilant about.
Securing Python Web Applications
Web apps top the list of targets for attackers. Focus security efforts on:
- HTTPS everywhere – Encrypt all web traffic with TLS certificates to prevent interception.
- Input validation – Aggressively validate and sanitize all data entered into forms, URLs, APIs and more. Never trust inputs.
- SQL injection prevention – User input used in queries can be exploited. Use parameterized queries.
- Cross-site scripting defenses – Encode/escape all data rendered by the app.
- Secure cookies – Prevent cookie manipulation using httpOnly and secure flags.
- Content security policy – Whitelist allowed origins for assets and scripts.
- Access controls – Limit web functionality based on user roles and permissions.
Web app security aligns closely with general Python input validation and output encoding.
Securing APIs and Services
Exposed APIs and microservices also warrant protections:
- Authentication – Require auth tokens or API keys for access. Apply rate limiting.
- Access controls – Restrict API calls to those necessary per user role.
- Request validation – Check call parameters and payload data matches expectations.
- Output encoding – Sanitize API JSON/XML output fields that may contain user data.
- TLS termination – Decrypt HTTPS traffic at the load balancer rather than internally.
- Monitoring – Monitor traffic for anomalies, attempted hacks, and suspicious patterns.
Lock down integrations and services to prevent API abuse.
Python Code Security Best Practices
For application code, adhere to:
- Least privilege – Follow principle of least privilege in granting permissions and privileges.
- Secret management – Avoid hardcoded secrets. Use environments or secret managers.
- Input validation – Never trust input data. Always validate and sanitize.
- Output encoding – Encode data from input sources before rendering output.
- Minimize dependencies – Reduce third-party code dependencies to lower risk surface.
- Code auditing – Perform static analysis on code with tools like bandit to catch common mistakes.
- Logging – Log user access and application errors to identify attack attempts.
There are many ways to write more secure Python beyond just app protections.
Python-specific Security Tools
Take advantage of purpose-built Python security tools:
- Bandit – Find common security issues with static analysis of Python code.
- Safety – Check installed dependencies for known vulnerabilities.
- sqlmap – Detect SQL injection flaws.
- SSH bastion – Provides secure proxy SSH access to isolated Python environments.
- pyt – Detect XSS and code injection vulnerabilities.
- mimesis – Generates fake data for testing.
- tamper – Test web app robustness via attack simulation.
These tools give expansive testing and auditing capabilities tailored for Python code.
Securing Passwords with Python
Proper password handling is critical in Python:
- Use bcrypt – Store hashed passwords using the bcrypt algorithm instead of insecure MD5, SHA1.
- Salt passwords – Add salt during hashing to prevent rainbow table attacks.
- Argon2 for speed – Argon2 allows faster secure hashing than bcrypt.
- Prevent plaintext – Never store unhashed passwords or send over unencrypted connections.
- Require strong passwords – Enforce password complexity, length, exclusion of old passwords.
- Use a password vault – Store passwords securely encrypted rather than hardcoded.
Smart Python password handling limits exposure when breaches do occur.
Python Cryptography Best Practices
For encrypting data in Python:
- Use libs not raw – Rely on industry vetted cryptography libraries instead of inventing own schemes.
- Do key management properly – Securely generate, store and handle cryptographic keys.
- Prefer asymmetric encryption – Use public-key encryption instead of symmetric when possible.
- Always hash passwords – One way hash passwords rather than reversible encryption. Argon2 is preferred.
- Use authenticated encryption – Encryption schemes that also verify data integrity like AES-GCM.
- Understand IVs – Properly implement initialization vectors to avoid predictability.
Correct use of vetted Python crypto libraries prevents many disasters.
Python powers critical systems and applications driving the need for strong security foundations. Core principles like least privilege, input validation and fail safety provide guardrails.
Common Python vulnerabilities such as SQL injection must be understood to mitigate exposures. Python-security tools and proper password handling give you the tools to implement robust protections.
Adopting sound security practices avoids easily preventable flaws and positions applications to better withstand attacks. Keep these beginner guidelines in mind as you build safe systems with Python.
Frequently Asked Questions
Q: Are web frameworks like Django and Flask secure out of the box?
A: They provide good defaults like SQL injection protection but still require proper input validation, avoiding XSS, and additional hardening for production.
Q: Does Python have any built-in security features?
A: Some like TLS support and warning against use of weak/insecure cryptographic algorithms. Overall library support is stronger than built-in features.
Q: What level of security knowledge is needed to harden Python apps?
A: Basic familiarity with risks like XSS and injection attacks goes a long way. Understanding core principles and language features provides a good foundation.
Q: Are there any common mistakes that lead to insecure Python code?
A: Improper input validation and output encoding, hardcoded secrets, relying on weak hashing algorithms, not using parameterized queries.
Q: Where can I find more resources for learning Python security best practices?
A: OWASP Python Security Project, Python Security Documentation, PyCQA projects, and platform docs from AWS, GCP, Azure.