Code responsibly with generative AI in C# (desktop applications)
30 Labs
15 Case Studies
Platform
Desktop, Generative AI
Audience
C# developers using Copilot or other GenAI tools
Preparedness
General C# development
Standards and references
CWE and Fortify Taxonomy
Group size
12 participants
Outline
- Coding responsibly with GenAI
- Input validation
- Security features
- Denial of service
- Using vulnerable components
- Cryptography for developers
- Common software security weaknesses
- Wrap up
What you will learn
- Understanding the essentials of responsible AI
- Getting familiar with essential cyber security concepts
- Input validation approaches and principles
- Identify vulnerabilities and their consequences
- Learn the security best practices in C#
- Correctly implementing various security features
- Managing vulnerabilities in third party components
- Understanding how cryptography supports security
- Learning how to use cryptographic APIs correctly in C#
- 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
- Input validation
- Input validation principles
- Denylists and allowlists
- What to validate – the attack surface
- Where to validate – defense in depth
- When to validate – validation vs transformations
- Injection
- Code injection
- 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 – Command injection in Ruckus
- OS command injection
- Code injection
- Integer handling problems
- Representing signed numbers
- Integer visualization
- Integer overflow
- Lab – Integer overflow
- Signed / unsigned confusion
- Case study – The Stockholm Stock Exchange
- Lab – Signed / unsigned confusion
- Lab – Experimenting with signed / unsigned confusion in Copilot
- Integer truncation
- Best practices
- Upcasting
- Precondition testing
- Postcondition testing
- Integer handling in C#
- Lab – Checked arithmetics
- Lab – Experimenting with integer overflow in Copilot
- Files and streams
- Path traversal
- Lab – Path traversal
- Additional challenges in Windows
- Case study – File spoofing in WinRAR
- Path traversal best practices
- Lab – Path canonicalization
- Lab – Experimenting with path traversal in Copilot
- Input validation
- Unsafe reflection
- Reflection without validation
- Lab – Unsafe reflection
- Lab – Experimenting with unsafe reflection in Copilot
- Unsafe native code
- Native code dependence
- Lab – Unsafe native code
- Best practices for dealing with native code
- Unsafe reflection
- Security features
- 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 C#
- Lab – Using adaptive hash functions in Copilot
- Case study – Veeam missing authentication and cleartext password storage
- Password policy
- NIST authenticator requirements for memorized secrets
- Password database migration
- Hard coded passwords
- Best practices
- Lab – Hardcoded password
- Protecting sensitive information in memory
- Challenges in protecting memory
- Case study – Microsoft secret key theft via dump files
- Storing sensitive data in memory
- Case study – KeePass password leakage via strings
- Information exposure
- Exposure through extracted data and aggregation
- Case study – Strava data exposure
- Platform security
- .NET platform security
- Protecting .NET code and applications
- Code signing
- Protecting .NET code and applications
- .NET platform security
- Authentication
- Denial of service
- Flooding
- Resource exhaustion
- Algorithmic complexity issues
- Regular expression denial of service (ReDoS)
- Lab – ReDoS
- Lab – Experimenting with ReDoS in Copilot
- Dealing with ReDoS
- Regular expression denial of service (ReDoS)
- Using vulnerable components
- 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)
- Cryptography for developers
- Cryptography basics
- Crypto APIs in C#
- Elementary algorithms
- Hashing
- Hashing basics
- Hashing in C#
- Lab – Hashing in C# (exploring with Copilot)
- Random number generation
- Pseudo random number generators (PRNGs)
- Cryptographically secure PRNGs
- Weak and strong PRNGs
- Using random numbers in C#
- Lab – Using random numbers in C# (exploring with Copilot)
- Case study – Equifax credit account freeze
- Hashing
- Confidentiality protection
- Symmetric encryption
- Block ciphers
- Modes of operation
- Modes of operation and IV – best practices
- Symmetric encryption in C#
- Symmetric encryption in C# with streams
- Lab – Symmetric encryption in C# (exploring with Copilot)
- Case study – Padding oracle used in RCE against Citrix ShareFile
- Asymmetric encryption
- The RSA algorithm
- RSA in C#
- The RSA algorithm
- Combining symmetric and asymmetric algorithms
- Key exchange and agreement
- Key exchange
- Diffie-Hellman key agreement algorithm
- Key exchange pitfalls and best practices
- Symmetric encryption
- Integrity protection
- Message Authentication Code (MAC)
- Calculating HMAC in C#
- Lab – Calculating MAC in C#
- Digital signature
- Digital signature with RSA
- Elliptic Curve Cryptography
- ECC basics
- Digital signature with ECC
- Digital signature in C#
- Lab – Digital signature with ECDSA in C#
- Message Authentication Code (MAC)
- Common software security weaknesses
- Code quality
- Code quality and security
- Data handling
- Initialization and cleanup
- Class initialization cycles
- Lab – Initialization cycles (exploring with Copilot)
- Initialization and cleanup
- Object oriented programming pitfalls
- Inheritance and overriding
- Mutability
- Lab – Mutable object (exploring with Copilot)
- Serialization
- Serialization and deserialization challenges
- Integrity – deserializing untrusted streams
- Integrity – deserialization best practices
- Look ahead deserialization
- Property Oriented Programming (POP)
- Creating a POP payload
- Lab – Creating a POP payload
- Lab – Using the POP payload
- Case study – Deserialization RCE in Veeam
- Code quality
- Wrap up
- Secure coding principles
- Principles of robust programming by Matt Bishop
- Secure design principles of Saltzer and Schroeder
- And now what?
- Software security sources and further reading
- .NET and C# 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
- One of our colleagues will be in touch to schedule a free consultation about training requirements
Inquiry
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