Machine Learning Security

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DAY 1

Cyber security basics

What is security?
Threat and risk
Cyber security threat types
Consequences of insecure software
- Constraints and the market
- The dark side
Categorization of bugs
- The Seven Pernicious Kingdoms
- Common Weakness Enumeration (CWE)
- CWE Top 25 Most Dangerous Software Errors
- Vulnerabilities in the environment and dependencies

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
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
  - 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
Denial of service
- Denial of Service
- Resource exhaustion
- Cash overflow
- Flooding
- Algorithm complexity issues
- Denial of service in ML
  - Accuracy reduction attacks
  - Denial-of-information attacks
  - Catastrophic forgetting in neural networks
  - Resource exhaustion attacks against ML
  - Best practices for protecting availability in ML systems

DAY 2

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
- Regular expression denial of service (ReDoS)
- Lab – Regular expression denial of service (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
  - Additional considerations
  - Lab – SQL injection best practices
  - Case study – Hacking Fortnite accounts
  - SQL injection and ORM
- Code injection
  - Code injection via input()
  - OS command injection
    - Lab – Command injection in Python
    - OS command injection best practices
    - Avoiding command injection with the right APIs in Python
    - Lab – Command injection best practices in Python
    - Case study – Shellshock
    - Lab – Shellshock
    - Case study – Command injection via ping
    - Python module hijacking
    - Lab – Module hijacking
- General protection best practices
Integer handling problems
- Representing signed numbers
- Integer visualization
- Integers in Python
- Integer overflow
- Integer overflow with ctypes and numpy
- Lab – Integer problems in Python
- Other numeric problems
  - Division by zero
  - Other numeric problems in Python
  - Working with floating-point numbers
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
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

DAY 3

Security features

Authentication
- Authentication basics
- Multi-factor authentication
- Authentication weaknesses – spoofing
- 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
      - NIST authenticator requirements for memorized secrets
      - Password length
      - Password hardening
      - Using passphrases
      - Password change
      - Forgotten passwords
      - Lab – Password reset weakness
    - 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
- 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
- System information leakage
  - Leaking system information
- Information exposure best practices

Time and state

Race conditions
- File race condition
  - Time of check to time of usage – TOCTTOU
  - Insecure temporary file
- Avoiding race conditions in Python
  - Thread safety and the Global Interpreter Lock (GIL)
  - Case study: TOCTTOU in Calamares
Mutual exclusion and locking
- Deadlocks
Synchronization and thread safety

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

Using vulnerable components

Assessing the environment
Hardening
Malicious packages in Python
Vulnerability management
- Patch management
- Bug bounty programs
- Vulnerability databases
- Vulnerability rating – CVSS
- DevOps, the build process and CI / CD
- Dependency checking in Python
- Lab – Detecting vulnerable components
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)

DAY 4

Cryptography for developers

Cryptography basics
Cryptography in Python
Elementary algorithms
- Random number generation
  - Pseudo random number generators (PRNGs)
  - Cryptographically strong PRNGs
  - Seeding
  - Using virtual random streams
  - Weak and strong PRNGs in Python
  - Using random numbers in Python
  - Case study – Equifax credit account freeze
  - True random number generators (TRNG)
  - Assessing PRNG strength
  - 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
  - Symmetric encryption in Python
  - Lab – Symmetric encryption in Python
  - Asymmetric encryption
    - The RSA algorithm
      - Using RSA – best practices
      - RSA in Python
    - Elliptic Curve Cryptography
      - The ECC algorithm
      - Using ECC – best practices
      - ECC 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)
  - MAC in Python
  - Lab – Calculating MAC in Python
- Digital signature
  - Digital signature with RSA
  - Digital signature with ECC
  - Digital signature in Python
Public Key Infrastructure (PKI)
- Some further key management challenges
- Certificates
  - Chain of trust
  - Certificate management – best practices

Security testing

Security testing methodology
- Security testing – goals and methodologies
- Overview of security testing processes
- Threat modeling
  - SDL threat modeling
  - Mapping STRIDE to DFD
  - DFD example
  - Attack trees
  - Attack tree example
  - Misuse cases
  - Misuse case examples
  - Risk analysis
Security testing techniques and tools
- Code analysis
  - Security aspects of code review
  - Static Application Security Testing (SAST)
  - Lab – Using static analysis tools
  - Lab – Finding vulnerabilities via ML
- Dynamic analysis
  - Security testing at runtime
  - Penetration testing
  - Stress testing
  - Dynamic analysis tools
    - Dynamic Application Security Testing (DAST)
  - Fuzzing
    - Fuzzing techniques
    - Fuzzing – Observing the process
    - ML fuzzing
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
  - Machine learning security resources

Machine Learning Security (MLSEC) – Details

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