Desktop application security in Python

CYDPyDsk3d
3 days
Python

Platform

Linux, Windows

Preparedness

General Python development

Audience

Python developers working on desktop applications

Group size

12 participants

Labs

Hands-on

Outline

  • Cyber security basics
  • Input validation
  • Security features
  • Using vulnerable components
  • Cryptography for developers
  • Common software security weaknesses
  • Wrap up

Objective list

  • Getting familiar with essential cyber security concepts
  • Handling security challenges in your Python code
  • Identify vulnerabilities and their consequences
  • Learn the security best practices in Python
  • Understanding how cryptography can support security of your products
  • Learning how to use cryptographic APIs correctly in Python

Description

Your application written in Python works as intended, so you are done, right? But did you consider feeding in incorrect values? 16Gbs of data? A null? An apostrophe? Negative numbers, or specifically -232? Because that’s what the bad guys will do – and the list is far from complete.

Handling security needs a healthy level of paranoia, and this is what this course provides: a strong emotional engagement by lots of hands on labs and stories from real life, all to substantially improve code hygiene. Mistakes, consequences and best practices are our blood, sweat and tears.

All this is put in the context of Python, and extended by core programming issues, discussing security pitfalls of the programming language.

So that you are prepared for the forces of the dark side.

So that nothing unexpected happens.

Nothing.

Table of contents

  • Cyber security basics
    • What is security?
    • Threat and risk
    • Cyber security threat types
    • Consequences of insecure software
      • Constraints and the market
      • The dark side
  • Input validation
    • Input validation principles
      • Blacklists and whitelists
      • Data validation techniques
      • Lab – Input validation
      • What to validate – the attack surface
      • Where to validate – defense in depth
      • How to validate – validation vs transformations
      • Output sanitization
      • Encoding challenges
      • Lab – Encoding challenges
      • Validation with regex
    • 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
        • Additional considerations
        • Lab – SQL injection best practices
        • Case study – Hacking Fortnite accounts
      • 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
          • Case study – Shellshock
          • Lab – Shellshock
          • Case study – Command injection via ping
        • Script injection
          • Python module hijacking
          • Lab – Module hijacking
      • Injection best practices
  • Input validation
    • Integer handling problems
      • Representing signed numbers
      • Integer visualization
      • Integers in Python
      • Integer overflow
      • Integer overflow with ctypes and numpy
      • Lab – Integer problems
      • Other numeric problems
        • Division by zero
        • Other numeric problems in Python
        • Working with floating-point numbers
        • Lab – Integer handling in Python
    • Files and streams
      • Path traversal
      • Path traversal-related examples
      • Lab – Path traversal
      • Additional challenges in Windows
      • Virtual resources
      • Path traversal best practices
      • Format string issues
    • Unsafe native code
      • Native code dependence
      • Lab – Unsafe native code
      • Best practices for dealing with native code
  • Security features
    • Authentication
      • Authentication basics
      • Authentication weaknesses
      • Case study – PayPal 2FA bypass
      • User interface best practices
      • Password management
        • Inbound password management
          • Storing account passwords
          • Password in transit
          • Lab – Why is just hashing passwords not enough?
          • Dictionary attacks and brute forcing
          • Salting
          • Adaptive hash functions for password storage
          • Password policy
            • NIST authenticator requirements for memorized secrets
            • Password length
            • Password hardening
            • Using passphrases
          • The Ashley Madison data breach
            • The dictionary attack
            • The ultimate crack
            • Exploitation and the lessons learned
          • Password database migration
            • (Mis)handling None passwords
        • Outbound password management
          • Hard coded passwords
          • Best practices
          • Lab – Hardcoded password
          • Protecting sensitive information in memory
            • Challenges in protecting memory
    • Authorization
      • Access control basics
      • Access control in databases
        • Lab – Database access control
        • MySQL best practices
      • Privileges and permissions
        • Permission manipulation
        • Incorrect use of privileged APIs
        • Permission best practices
          • Principle of least privilege
          • Principle of separation of privilege
          • Permission granting and handling
    • Python platform security
      • The Python ecosystem and its attack surface
      • Python bytecode and security
      • Security features offered by the Python runtime
      • PEP 578 and audit hooks
      • Sandboxing Python
    • Information exposure
      • Exposure through extracted data and aggregation
      • Case study – Strava fitness app data exposure
      • System information leakage
        • Leaking system information
      • Information exposure best practices
  • Using vulnerable components
    • Assessing the environment
    • Hardening
    • Malicious packages in Python
    • Vulnerability management
      • Patch management
      • Vulnerability databases
      • The build process and CI / CD
      • Dependency checking in Python
      • Lab – Detecting vulnerable components
  • Cryptography for developers
    • Cryptography basics
    • Cryptography in Python
    • Elementary algorithms
      • Random number generation
        • Pseudo random number generators (PRNGs)
        • Cryptographically strong PRNGs
        • Using virtual random streams
        • Weak and strong PRNGs
        • Using random numbers in Python
        • Case study – Equifax credit account freeze
        • Lab – Using random numbers in Python
      • Hashing
        • Hashing basics
        • Common hashing mistakes
        • Hashing in Python
        • Lab – Hashing in Python
    • Confidentiality protection
      • Symmetric encryption
        • Block ciphers
        • Modes of operation
        • Modes of operation and IV – best practices
        • Encryption in Python
        • Lab – Encryption in Python
      • Asymmetric encryption
        • The RSA algorithm
          • Using RSA – best practices
          • Combining symmetric and asymmetric algorithms
    • Integrity protection
      • Message Authentication Code (MAC)
        • Calculating MAC in Python
        • Lab – Calculating MAC in Python
      • Digital signatures
      • Digital signature
        • Digital signature with RSA
    • Public Key Infrastructure (PKI)
      • Some further key management challenges
      • Certificates
        • Chain of trust
        • Certificate management – best practices
  • Common software security weaknesses
    • Time and state
      • Race conditions
        • File race condition
          • Insecure temporary file
        • Avoiding race conditions in Python
          • Thread safety and the Global Interpreter Lock (GIL)
          • Case study: TOCTTOU in Calamares
    • Errors
      • Error and exception handling principles
      • Error handling
        • Returning a misleading status code
        • Information exposure through error reporting
      • Exception handling
        • In the except,catch block. And now what?
        • Empty catch block
        • The danger of assert statements
        • Lab – Exception handling mess
    • Code quality
      • Data
        • Initialization and cleanup
        • Unreleased resource
      • Language elements
        • Using dangerous language elements
        • Using obsolete language elements
        • Portability flaw
        • Module injection and monkey patching
        • Dangers of compile(), exec() and eval()
        • Sandboxing Python
      • Object oriented programming pitfalls
        • Private attributes and name mangling
        • Multiple inheritance and security
        • Mutability
      • Serialization
        • Serialization and deserialization challenges
        • Serializing sensitive data
        • Serialization best practices
        • Deserializing untrusted streams
        • Deserialization with pickle
        • Lab – Deserializing with Pickle
        • Deserialization with PyYAML
        • Deserializing best practices
  • Wrap up
    • Secure coding principles
      • Principles of robust programming by Matt Bishop
      • Secure design principles of Saltzer and Schröder
    • And now what?
      • Further sources and readings
      • Python resources

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

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