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Machine learning security

CYDMLPy
4 days
On-site or online
Hands-on
Python
Machine learning
Developer
Instructor-led
Pricing Inquiry Download PDF
labs

30 Labs

case_study

14 Case Studies

Platform

Desktop

Audience

Python developers working on machine learning systems

Preparedness

General machine learning and Python development

Standards and references

CWE and Fortify Taxonomy

Group size

12 participants

Outline

  • Cyber security basics
  • Machine learning security
  • Input validation
  • Security features
  • Time and state
  • Errors
  • Using vulnerable components
  • Cryptography for developers
  • Wrap up

What you will learn

  • Getting familiar with essential cyber security concepts
  • Learning about various aspects of machine learning security
  • Attacks and defense techniques in adversarial machine learning
  • Input validation approaches and principles
  • Identify vulnerabilities and their consequences
  • Learn the security best practices in Python
  • Correctly implementing various security features
  • Managing vulnerabilities in third party components
  • Understanding how cryptography supports security
  • Learning how to use cryptographic APIs correctly in Python

Description

Your machine learning application works as intended, so you are done, right? But did you consider somebody poisoning your model by training it with intentionally malicious samples? Or sending specially-crafted input – indistinguishable from normal input – to your model that will get completely misclassified? Feeding in too large samples – for example, an image of 16Gbs to crash the application? Because that’s what the bad guys will do. And the list is far from complete.

As a machine learning practitioner, you need to be paranoid just as any developer out there. Interest in attacking machine learning solutions is gaining momentum, and therefore protecting against adversarial machine learning is essential. This needs not only awareness, but also specific skills to protect your ML applications. The course helps you gain these skills by introducing cutting edge attacks and protection techniques from the ML domain.

Machine learning is software after all. That’s why in this course we also teach common secure coding skills and discuss security pitfalls of the Python programming language. Both adversarial machine learning and core secure coding topics come with lots of hands on labs and stories from real life, all to provide a strong emotional engagement to security and to substantially improve code hygiene.

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

So that nothing unexpected happens.

Nothing.

Table of contents

  • Cyber security basics
  • Machine learning security
    • Cyber security in machine learning
      • ML-specific cyber security considerations
      • What makes machine learning a valuable target?
      • Possible consequences
      • Inadvertent AI failures
      • Some real-world abuse examples
      • ML threat model
        • Creating a threat model for machine learning
        • Machine learning assets
        • Security requirements
        • Attack surface
        • Attacker model – resources, capabilities, goals
        • Confidentiality threats
        • Integrity threats (model)
        • Integrity threats (data, software)
        • Availability threats
        • Dealing with AI/ML threats in software security
        • Lab – Compromising ML via model editing
      • Using ML in cybersecurity
        • Static code analysis and ML
        • ML in fuzz testing
        • ML in anomaly detection and network security
        • Limitations of ML in security
      • Malicious use of AI and ML
        • Social engineering attacks and media manipulation
        • Vulnerability exploitation
        • Malware automation
        • Endpoint security evasion
      • Security of large language models (LLMs)
        • Security of LLMs vs ML security
        • BIML top 10 LLM security risks
        • OWASP LLM Top 10
        • Practical attacks on LLMs
        • Practical LLM defenses
    • Adversarial machine learning
      • Threats against machine learning
      • Attacks against machine learning integrity
        • Poisoning attacks
        • Poisoning attacks against supervised learning
        • Poisoning attacks against unsupervised and reinforcement learning
        • Lab – ML poisoning attack
        • Case study – ML poisoning against Warfarin dosage calculations
        • Evasion attacks
        • Common white-box evasion attack algorithms
        • Common black-box evasion attack algorithms
        • Lab – ML evasion attack
        • Case study – Classification evasion via 3D printing
        • Transferability of poisoning and evasion attacks
        • Lab – Transferability of adversarial examples
      • Some defense techniques against adversarial samples
        • Adversarial training
        • Defensive distillation
        • Gradient masking
        • Feature squeezing
        • Using reformers on adversarial data
        • Provable defenses against adversarial attacks
        • Lab – Adversarial training
        • Caveats about the efficacy of current adversarial defenses
        • Simple practical defenses
      • Attacks against machine learning confidentiality
        • Model extraction attacks
        • Defending against model extraction attacks
        • Lab – Model extraction
        • Model inversion attacks
        • Defending against model inversion attacks
        • Lab – Model inversion
  • Input validation
    • Input validation principles
    • Denylists and allowlists
    • 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
    • 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 – Using prepared statements
          • Database defense in depth
          • Case study – Hacking Fortnite accounts
      • Code injection
        • Code injection via input() in Python
        • OS command injection
          • Lab – Command injection
          • OS command injection best practices
          • Avoiding command injection with the right APIs in Python
          • Lab – Command injection best practices
          • Case study – Shellshock
          • Lab – Shellshock
          • Case study – Command injection via ping
          • Python module hijacking
    • Input validation in machine learning
      • Misleading the machine learning mechanism
      • Sanitizing data against poisoning and RONI
      • Code vulnerabilities causing evasion, misprediction, or misclustering
      • Typical ML input formats and their security
  • Input validation
    • 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
      • Format string issues in Python
    • Unsafe native code
      • Native code dependence
      • Lab – Unsafe native code in Python
      • Best practices for dealing with native code
  • Security features
    • Authentication
      • Authentication basics
      • Multi-factor authentication (MFA)
      • Time-based One Time Passwords (TOTP)
      • Case study – PayPal 2FA bypass
      • Password management
        • Inbound password management
        • 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
      • Privacy violation
        • Privacy essentials
        • Related standards, regulations and laws in brief
        • Privacy violation and best practices
        • Privacy in machine learning
          • Privacy challenges in classification algorithms
          • Machine unlearning and its challenges
  • Time and state
    • Race conditions
      • File race condition
        • Time of check to time of usage – TOCTTOU
        • TOCTTOU attacks in practice
        • Lab – TOCTTOU
        • Insecure temporary file
  • Errors
    • Error and exception handling principles
    • Error handling
      • Returning a misleading status code
      • Information exposure through error reporting
        • Lab – Flask information leakage
    • Exception handling
      • In the except block. And now what?
      • Empty except block
      • Lab – Exception handling mess
  • Using vulnerable components
    • Malicious packages in Python
    • Vulnerability management
    • ML supply chain risks
      • Common ML system architectures
      • ML system architecture and the attack surface
      • Case study – BadNets
      • Protecting data in transit – transport layer security
      • Protecting data in use – homomorphic encryption
      • Protecting data in use – differential privacy
      • Protecting data in use – multi-party computation
    • ML frameworks and security
      • General security concerns about ML platforms
      • TensorFlow security issues and vulnerabilities
      • Case study – TensorFlow vulnerability in parsing BMP files (CVE-2018-21233)
  • Cryptography for developers
    • Cryptography basics
    • Cryptography in Python
    • Elementary algorithms
      • Hashing
        • Hashing basics
        • Common hashing mistakes
        • Hashing in Python
        • Lab – Hashing
      • Random number generation
        • Pseudo random number generators (PRNGs)
        • Cryptographically secure PRNGs
        • Using virtual random streams
        • Weak PRNGs in Python
        • Using random numbers in Python
        • Lab – Using random numbers
        • Case study – Equifax credit account freeze
    • Confidentiality protection
      • Symmetric encryption
        • Block ciphers
        • Modes of operation
        • Modes of operation and IV – best practices
        • Symmetric encryption in Python
        • Lab – Symmetric encryption
      • Asymmetric encryption
        • The RSA algorithm
          • Using RSA – best practices
          • RSA in Python
      • Combining symmetric and asymmetric algorithms
      • Homomorphic encryption
        • Basics of homomorphic encryption
        • Types of homomorphic encryption
        • FHE in machine learning
    • Integrity protection
      • Message Authentication Code (MAC)
        • Calculating HMAC in Python
        • Lab – Calculating MAC
      • Digital signature
        • Digital signature with RSA
        • Elliptic Curve Cryptography
          • ECC basics
          • Digital signature with ECC
        • Digital signature in Python
          • Lab – Digital signature with ECDSA
    • Public Key Infrastructure (PKI)
      • Some further key management challenges
      • Certificates
        • Certificates and PKI
        • X.509 certificates
        • Chain of trust
        • PKI actors and procedures
        • Certificate revocation
  • 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
      • Python resources
      • Machine learning security resources

Pricing

4 days Session Price

3000 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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