Full course

Secure Coding with AI

This intensive course teaches developers how to use AI coding assistants such as GitHub Copilot, Codex and Claude Code to write, review and fix secure code. It combines OWASP secure-coding best practices with hands-on labs on vulnerable Java and .NET applications.

Details

Course overview

This course has two goals: to align web application security skills across the development team, including the attacker's perspective, and to teach developers how to use AI coding assistants effectively to identify, fix and prevent security vulnerabilities, both in existing codebases and when building new applications. The methodology alternates theory with hands-on labs: participants exploit vulnerabilities on realistic vulnerable applications, then fix them with AI and verify the results, while incrementally building a new secure application as each vulnerability class is covered. The training is grounded in OWASP best practices and uses GitHub Copilot (with OpenAI and Anthropic models), Codex, Claude Code, Burp Suite and Semgrep; the language of the new application is agreed with the client beforehand. This course is aimed at software developers who want to strengthen their application security skills and learn to use AI as a reliable aid for writing secure code; no prior experience with AI tools is required.

Requirements

  • Basic knowledge of the architecture and functioning of web applications

  • Basic knowledge of at least one backend language (e.g. Java or .NET); no prior experience with AI tools required

Course content

  • Introduction to secure coding: fundamental principles, the OWASP Top 10 and the Secure SDLC.

  • How AI coding assistants work: a conceptual overview of LLMs and their role in software development.

  • Limits and risks of AI-generated code: insecure-by-default output, outdated or insecure libraries, and hallucinated packages (slopsquatting).

  • Governance of AI tools in the enterprise: usage policies, protection of sensitive data, and cost and usage management.

  • Burp Suite introduction: intercepting and inspecting application traffic to understand its behaviour.


  • How prompt formulation affects the security of the generated code, and why generic instructions are not enough.

  • Security-focused prompting: stating input validation, output encoding, error handling and cryptography requirements explicitly.

  • Recursive Criticism and Improvement (RCI): generating, self-reviewing and fixing code iteratively to reduce vulnerability density.

  • Zero-Shot Chain-of-Thought, and the pitfalls of generic and persona-based prompting.

  • Security instruction files for AI assistants: what they are, how to structure them and best practices.


  • Authentication vulnerabilities and defense: user enumeration, authentication bypass, default credentials and password reset flows.

  • JWT: understanding JSON Web Tokens and their secure use in authentication.

  • Authorization and Access Control: parameter manipulation, access matrix and business-logic bypass.

  • SQL Injection: exploitation and prevention through parameterized queries, fixed and verified with AI.


  • Cross-Site Scripting (XSS) and output encoding.

  • Open Redirect: identification and prevention.

  • CSRF and CORS security.

  • Arbitrary File Upload: secure handling of uploaded files.

  • XXE (in-band and out-of-band) and SSRF: exploited on the vulnerable lab applications, then remediated with AI and verified.


  • Integrating SAST tools (Semgrep) with AI as a verification loop, and how they differ from AI-only analysis.

  • Supply-chain risks of AI-suggested dependencies (hallucinated or outdated packages) and how to verify them.

  • Offensive use of AI: payload generation and attack-surface analysis.

  • Final security testing and retest of the application developed during the course.


Your instructor

  • TBD Senior Instructor

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