Code responsibly with generative AI in Python
31 Labs
14 Case Studies
Platform
Generative AI, Web
Audience
Python developers using Copilot or other GenAI tools
Preparedness
General Python and Web development
Standards and references
OWASP, CWE and Fortify Taxonomy
Group size
12 participants
Outline
- Coding responsibly with GenAI
- The OWASP Top Ten from Copilot's perspective
- Wrap up
What you will learn
- Understanding the essentials of responsible AI
- Getting familiar with essential cyber security concepts
- Understanding how cryptography supports security
- Learning how to use cryptographic APIs correctly in Python
- Understanding Web application security issues
- Detailed analysis of the OWASP Top Ten elements
- Putting Web application security in the context of Python
- Going beyond the low hanging fruits
- Managing vulnerabilities in third party components
- All this put into the context of GitHub Copilot
Description
Generative AI is transforming the software industry, with tools like GitHub Copilot and Codeium enabling developers to achieve unprecedented levels of efficiency. While this is exciting progress, it also raises important concerns, encouraging stakeholders to approach these technologies with care. Current AI tools often lack the nuanced understanding necessary to address subtle, yet critical aspects of software development, particularly in the domain of security.
This course provides a comprehensive insight into the responsible use of generative AI in coding. Participants delve into topics in software development that are most likely to be impacted by careless use of generative AI, including authentication, authorization, and cryptography. The curriculum also includes an analysis of how AI tools like Copilot handle secure coding practices related to key vulnerabilities outlined in the OWASP Top Ten, such as path traversal, SQL injection, or cross-site scripting.
Through hands-on learning and experimenting, participants will get a solid understanding of both the strengths and limitations of AI-assisted development. In addition, case studies of real-world incidents showcase the consequences of insecure code and demonstrate the dual nature of generative AI as both a resource and a potential risk.
By the end of the course, developers will be equipped with the knowledge and skills to integrate AI tools into the software development lifecycle responsibly, enhancing efficiency without compromising security or product quality.
Table of contents
- Coding responsibly with GenAI
- What is responsible AI?
- What is security?
- Threat and risk
- Cyber security threat types – the CIA triad
- Consequences of insecure software
- Security and responsible AI in software development
- GenAI tools in coding: Copilot, Codeium and others
- The OWASP Top Ten from Copilot’s perspective
- The OWASP Top Ten 2021
- A01 – Broken Access Control
- Access control basics
- Failure to restrict URL access
- Confused deputy
- Insecure direct object reference (IDOR)
- Path traversal
- Lab – Insecure Direct Object Reference
- Path traversal best practices
- Lab – Experimenting with path traversal in Copilot
- Authorization bypass through user-controlled keys
- Case study – Remote takeover of Nexx garage doors and alarms
- Lab – Horizontal authorization (exploring with Copilot)
- File upload
- Unrestricted file upload
- Good practices
- Lab – Unrestricted file upload (exploring with Copilot)
- A02 – Cryptographic Failures
- Cryptography for developers
- Cryptography basics
- Cryptography in Python
- Elementary algorithms
- Hashing
- Hashing basics
- Hashing in Python
- Lab – Hashing in Python (exploring with Copilot)
- Random number generation
- Pseudo random number generators (PRNGs)
- Cryptographically secure PRNGs
- Weak PRNGs
- Using random numbers
- Lab – Using random numbers in Python (exploring with Copilot)
- Lab – Secure PRNG use in Copilot
- Case study – Equifax credit account freeze
- Case study – weak randomness in OpenVPN Access Server
- Hashing
- Confidentiality protection
- Symmetric encryption
- Block ciphers
- Modes of operation
- Modes of operation and IV – best practices
- Symmetric encryption in Python
- Lab – Symmetric encryption in Python (exploring with Copilot)
- Asymmetric encryption
- Combining symmetric and asymmetric algorithms
- Symmetric encryption
- Cryptography for developers
- The OWASP Top Ten from Copilot’s perspective
- A03 – Injection
- Injection principles
- Injection attacks
- SQL injection
- SQL injection basics
- Lab – SQL injection
- Attack techniques
- Content-based blind SQL injection
- Time-based blind SQL injection
- SQL injection best practices
- Input validation
- Parameterized queries
- Lab – Using prepared statements
- Lab – Experimenting with SQL injection in Copilot
- Database defense in depth
- Case study – SQL injection against US airport security
- Code injection
- Code injection via input()
- OS command injection
- Lab – Command injection
- OS command injection best practices
- Avoiding command injection with the right APIs
- Lab – Command injection best practices
- Lab – Experimenting with command injection in Copilot
- Case study – Shellshock
- Lab – Shellshock
- Case study – Command injection in Ivanti security appliances
- HTML injection – Cross-site scripting (XSS)
- Cross-site scripting basics
- Cross-site scripting types
- Persistent cross-site scripting
- Reflected cross-site scripting
- Client-side (DOM-based) cross-site scripting
- Lab – Stored XSS
- Lab – Reflected XSS
- Case study – XSS to RCE in Teltonika routers
- XSS protection best practices
- Protection principles – escaping
- XSS protection APIs in Python
- XSS protection in Jinja2
- Lab – XSS fix / stored (exploring with Copilot)
- Lab – XSS fix / reflected (exploring with Copilot)
- Case study – XSS vulnerabilities in DrayTek Vigor routers
- A04 – Insecure Design
- The STRIDE model of threats
- Secure design principles of Saltzer and Schroeder
- Economy of mechanism
- Fail-safe defaults
- Complete mediation
- Open design
- Separation of privilege
- Least privilege
- Least common mechanism
- Psychological acceptability
- Client-side security
- Same Origin Policy
- Simple request
- Preflight request
- Cross-Origin Resource Sharing (CORS)
- Lab – Same-origin policy demo
- Frame sandboxing
- Cross-Frame Scripting (XFS) attacks
- Lab – Clickjacking
- Clickjacking beyond hijacking a click
- Clickjacking protection best practices
- Lab – Using CSP to prevent clickjacking (exploring with Copilot)
- Same Origin Policy
- A03 – Injection
- The OWASP Top Ten from Copilot’s perspective
- A05 – Security Misconfiguration
- Configuration principles
- Server misconfiguration
- Python configuration best practices
- Configuring Flask
- Cookie security
- Cookie attributes
- XML entities
- DTD and the entities
- Entity expansion
- External Entity Attack (XXE)
- File inclusion with external entities
- Server-Side Request Forgery with external entities
- Lab – External entity attack
- Preventing XXE
- Lab – Prohibiting DTD
- Case study – XXE vulnerability in Ivanti products
- Lab – Experimenting with XXE in Copilot
- A06 – Vulnerable and Outdated Components
- Using vulnerable components
- Untrusted functionality import
- Malicious packages in Python
- Case study – The Polyfill.io supply chain attack
- Vulnerability management
- Lab – Finding vulnerabilities in third-party components
- Security of AI generated code
- Practical attacks against code generation tools
- Dependency hallucination via generative AI
- Case study – A history of GitHub Copilot weaknesses (up to mid 2024)
- A07 – Identification and Authentication Failures
- Authentication
- Authentication basics
- Multi-factor authentication (MFA)
- Case study – The InfinityGauntlet attack
- Time-based One Time Passwords (TOTP)
- Password management
- Inbound password management
- Storing account passwords
- Password in transit
- Lab – Is just hashing passwords enough?
- Dictionary attacks and brute forcing
- Salting
- Adaptive hash functions for password storage
- Lab – Using adaptive hash functions in Python
- Lab – Using adaptive hash functions in Copilot
- Password policy
- NIST authenticator requirements for memorized secrets
- Password database migration
- Inbound password management
- Authentication
- A08 – Software and Data Integrity Failures
- Integrity protection
- Message Authentication Code (MAC)
- Calculating HMAC in Python
- Lab – Calculating MAC in Python
- Digital signature
- Digital signature in Python
- Message Authentication Code (MAC)
- Subresource integrity
- Importing JavaScript
- Lab – Importing JavaScript (exploring with Copilot)
- Case study – The British Airways data breach
- Integrity protection
- A10 – Server-side Request Forgery (SSRF)
- Server-side Request Forgery (SSRF)
- Case study – SSRF in Ivanti Connect Secure
- A05 – Security Misconfiguration
- Wrap up
- Secure coding principles
- Principles of robust programming by Matt Bishop
- And now what?
- Software security sources and further reading
- Python resources
- Responsible AI principles in software development
- Generative AI – Resources and additional guidance
- Secure coding principles
Pricing
3 days Session Price
2250 EUR / person
- Live, instructor led classroom training
- Discussion and insight into the hacker’s mindset
- Hands-on practice using case studies based on high-profile hacks and live lab exercises
Customized course
Tailor a course to your preferences
- Send us a brief description of your business’s training needs
- Include your contact information
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Inquiry
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