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Course Outline
Python Secure Foundations & Tooling
- Python 3.x security baseline: version considerations, PEP standards, and secure installation practices
- Professional IDE configuration: VS Code/PyCharm security extensions, linters (Flake8, Pylint), and debuggers
- Environment isolation:
venv/conda, containerization, and reproducible lab setups - Lab: Provisioning a secure Python workspace with integrated security linting and dependency tracking
Core Language Security & Safe Data Handling
- Numeric types & precision: avoiding floating-point manipulation attacks and safe type casting
- Strings & encoding: Unicode normalization, encoding validation, and preventing interpolation vulnerabilities
- Lists, dictionaries, and collections: safe data structures, hash collision mitigation, and secure serialization
- Regex & pattern matching: constructing safe regular expressions (preventing ReDoS), input validation patterns
- Lab: Rewriting insecure data-handling code into secure, validated, and type-hinted implementations
Control Flow, Functions & Secure Architecture
- Python statements & expressions: safe assignments, exception handling, and avoiding silent failure modes
- If tests & syntax rules: secure conditional logic, preventing dynamic execution vulnerabilities (
eval/exec/pickle) - Repetition statements: secure loop constructs, resource exhaustion prevention, and timeout handling
- Functions & encapsulation: secure parameter passing, type hinting, and function-level threat modeling
- Lab: Refactoring vulnerable control flow into secure, auditable, and defensive code patterns
Modules, Packages & Environment-Scoped Security (Python skope-rules)
- Module import security: avoiding circular imports, secure package resolution, and namespace isolation
- Dependency management:
pip/requirements.txt, lockfiles, supply chain security, and vulnerable package detection - Secret & credential management: environment variables,
.envbest practices, and preventing hardcoded secrets skope-rulesimplementation: scope-bound access controls, runtime policy enforcement, and dependency isolation- Lab: Auditing a Python project’s dependency tree and implementing environment-scoped security policies
Python-Specific Vulnerabilities & Mitigation
- OWASP Top 10 for Python/WSGI/ASGI apps: injection, authentication bypass, insecure deserialization, SSRF, and path traversal
- Secure I/O & file handling: safe file descriptors, directory traversal prevention, and sandboxed execution
- Web & API security in Python: safe request handling, output encoding, and framework-level protections (FastAPI/Flask/Django)
- Lab: Identifying and patching Python-specific vulnerabilities in a sample application using secure alternatives
Automated Security Testing & DevSecOps Integration
- SAST tools for Python: Bandit, Semgrep, and custom rule creation for scoped vulnerability detection
- DAST & dependency scanning:
pip-audit, Safety, and OWASP ZAP integration for runtime threat discovery - CI/CD pipeline security: GitHub Actions/GitLab CI workflows for automated Python security gates and compliance reporting
- Secure testing methodologies: threat modeling for Python microservices, fuzzing basics, and runtime protection
- Lab: Building an automated Python security scan pipeline and interpreting remediation reports
Capstone, Review & Secure Development Pathways
- End-to-end secure Python development workflow simulation
- Code review for security: identifying anti-patterns, applying secure fixes, and documenting decisions
- Q&A, resource distribution (secure coding cheat sheets, Python security libraries, official standards,
skope-rulestemplates) - Course close and next steps for Python security mastery
Requirements
Basics of any programming language
Basics of information Security
14 Hours
Testimonials (2)
Hands-on exercises related to content really helps to understand more about each topic. Also, style of start class with lecture and continue with hands-on exercise is good and helpful to relate with the lecture that presented earlier.
Nazeera Mohamad - Ministry of Science, Technology and Innovation
Course - Introduction to Data Science and AI using Python
Examples/exercices perfectly adapted to our domain
