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Desktop application security in Python

CYDPyDsk3d
3 days
On-site or online
Hands-on
Python
Developer
Pricing Inquiry Download PDF
labs

21 Labs

case_study

8 Case Studies

Audience

Python developers working on desktop applications

Preparedness

General Python development

Standards and references

CWE and Fortify Taxonomy

Group size

12 participants

Outline

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

What you'll have learned

  • Getting familiar with essential cyber security concepts
  • Identify vulnerabilities and their consequences
  • Learn the security best practices in Python
  • Input validation approaches and principles
  • Managing vulnerabilities in third party components
  • Understanding how cryptography supports security
  • 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 -1 or -231? 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
      • When to validate – validation vs transformations
      • Output sanitization
      • Encoding challenges
      • Unicode challenges
      • Validation with regex
      • Regular expression denial of service (ReDoS)
      • Lab – ReDoS in Python
      • Dealing with ReDoS
    • 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 – SQL injection best practices
        • Additional considerations
        • 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
          • Python module hijacking
          • Lab – Library hijacking in Python
  • Input validation
    • Integer handling problems
      • Representing signed numbers
      • Integer visualization
      • Integers in Python
      • Integer overflow
      • Integer overflows in ctypes and numpy
      • Other numeric problems
        • Working with floating-point numbers
    • Files and streams
      • Path traversal
      • Lab – Path traversal
      • Path traversal-related examples
      • Additional challenges in Windows
      • Virtual resources
      • Path traversal best practices
      • Lab – Path canonicalization
    • 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
      • Multi-factor authentication
      • Time-based One Time Passwords (TOTP)
      • Authentication weaknesses
      • Case study – PayPal 2FA bypass
      • 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
          • Password policy
          • Case study – The Ashley Madison data breach
            • The dictionary attack
            • The ultimate crack
            • Exploitation and the lessons learned
          • Password database migration
        • Outbound password management
          • Hard coded passwords
          • Best practices
          • Lab – Hardcoded password
          • Protecting sensitive information in memory
            • Challenges in protecting memory
    • Information exposure
      • Exposure through extracted data and aggregation
      • Case study – Strava data exposure
      • System information leakage
        • Leaking system information
      • Information exposure best practices
    • Platform security
      • Python platform security
        • The Python ecosystem and its attack surface
        • Python bytecode and security
        • Security features offered by Python
        • PEP 578 and audit hooks
        • Sandboxing Python
  • Using vulnerable components
    • Assessing the environment
    • Hardening
    • Case study – The British Airways data breach
    • Vulnerability management
  • 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
        • Lab – Using random numbers in Python
        • Case study – Equifax credit account freeze
      • 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
        • Symmetric encryption in Python
        • Lab – Symmetric encryption in Python
      • Asymmetric encryption
        • The RSA algorithm
          • Using RSA – best practices
          • RSA in Python
      • Combining symmetric and asymmetric algorithms
      • Key exchange and agreement
        • Key exchange
        • Diffie-Hellman key agreement algorithm
        • Key exchange pitfalls and best practices
    • Integrity protection
      • Authenticity and non-repudiation
      • Message Authentication Code (MAC)
        • MAC in Python
        • Lab – Calculating MAC in Python
      • Digital signature
        • Digital signature with RSA
        • Elliptic Curve Cryptography
          • ECC basics
          • Digital signature with ECC
        • Digital signature in Python
          • Lab – Digital signature with ECDSA in Python
    • Public Key Infrastructure (PKI)
      • Some further key management challenges
      • Certificates
        • Certificates and PKI
        • X.509 certificates
        • Chain of trust
        • PKI actors and procedures
        • PGP – Web of Trust
        • Certificate revocation
  • Common software security weaknesses
    • Errors
      • Error and exception handling principles
      • Error handling
        • Returning a misleading status code
        • Information exposure through error reporting
      • Exception handling
        • In the except block. And now what?
        • Empty except block
        • Lab – Exception handling mess
    • Code quality
  • Wrap up
    • Secure coding principles
      • Principles of robust programming by Matt Bishop
      • Secure design principles of Saltzer and Schröder
    • And now what?
      • Software security sources and further reading
      • 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
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