Sherlocked Security – AI/ML Model Security & Poisoning Testing

Ensuring Robust and Secure Machine Learning Models: Protecting AI Systems from Attacks and Ensuring Integrity

1. Statement of Work (SOW)

Service Name: AI/ML Model Security & Poisoning Testing
Client Type: Enterprises deploying AI/ML models, AI/ML developers, Data Scientists, and Organizations using machine learning in production.
Service Model: Project-Based Assessment & Retainer Advisory
Compliance Alignment: NIST AI Standards, ISO/IEC 27001, GDPR, and sector-specific regulatory frameworks.

AI/ML Model Security & Poisoning Testing Includes:

Evaluation of machine learning models for vulnerabilities to adversarial attacks
Assessment of model robustness against data poisoning and model poisoning techniques
Identification and mitigation of risks in model training data
Security testing for ML-based APIs and deployment environments
Analysis of data integrity issues in training datasets and model behavior
Testing the resilience of ML models against backdoor attacks
Privacy and confidentiality review in AI/ML models
Penetration testing of AI/ML systems to simulate real-world attack scenarios
Recommendations for improving the security of the ML lifecycle (training, testing, deployment, etc.)

2. Our Approach

[Data Collection & Preparation] → [Model Evaluation] → [Adversarial Testing] → [Poisoning Simulation] → [Robustness Testing] → [Privacy Assessment] → [Security Vulnerabilities Testing] → [Reporting & Recommendations]

3. Methodology

Model Evaluation & Threat Assessment:
- Conduct a thorough evaluation of the machine learning models (e.g., supervised, unsupervised, reinforcement learning) to identify potential vulnerabilities.
- Assess threat vectors such as adversarial attacks, model inversion, and data poisoning.
- Review the model lifecycle, including data collection, preprocessing, and training processes.
Adversarial Testing:
- Apply adversarial machine learning techniques (e.g., FGSM, DeepFool) to test model robustness against perturbations.
- Evaluate model performance degradation under adversarial conditions.
- Simulate real-world attack scenarios where input data is manipulated to deceive models.
Poisoning Testing & Simulation:
- Implement data poisoning attacks where malicious data is injected into the training dataset, aiming to corrupt model performance.
- Simulate backdoor poisoning attacks by introducing malicious patterns into the training process.
- Analyze the impact of poisoned data on model accuracy, stability, and trustworthiness.
Model Robustness Testing:
- Perform stress tests to evaluate how well the model performs in different attack scenarios.
- Test the model’s resilience against concept drift, adversarial input, and other forms of perturbations.
- Assess model behavior in edge cases, such as rare inputs or inputs from adversarial sources.
Privacy & Confidentiality Review:
- Evaluate the risk of data leakage through model inversion or membership inference attacks.
- Analyze the model’s ability to preserve user privacy and confidentiality.
- Test for vulnerabilities where attackers could extract sensitive data from the model’s predictions.
Penetration Testing of AI/ML APIs & Interfaces:
- Conduct penetration testing of AI/ML systems, APIs, and interfaces, checking for vulnerabilities such as unauthorized access or injection attacks.
- Test how the model behaves in a production environment under real-world attack conditions.
Security Vulnerabilities Assessment:
- Evaluate the security of the model deployment pipeline, including code, configurations, and environment settings.
- Identify potential weak points in the deployment, including API keys, model files, and data storage.
- Recommend best practices for securing machine learning workflows, such as using encrypted storage and robust access controls.
Model Update & Defense Strategy:
- Propose methods to continuously retrain and update models to enhance security and robustness.
- Implement defensive mechanisms like adversarial training, anomaly detection, and input validation to protect models from attacks.
- Ensure model integrity during deployment by using techniques like model monitoring and anomaly detection in real-time.

4. Deliverables to the Client

AI/ML Model Security Assessment Report: Detailed report on the security posture of AI/ML models, vulnerabilities identified, and attack simulations.
Adversarial & Poisoning Attack Findings: Documentation of testing and results for adversarial robustness and data poisoning impact.
Privacy & Confidentiality Assessment: Findings related to privacy risks and suggestions for mitigating potential data leakage or privacy breaches.
Penetration Test Results: Penetration testing results for AI/ML systems, APIs, and interfaces, along with recommendations.
Security & Privacy Best Practices: Actionable recommendations for enhancing security and privacy within AI/ML workflows.
Model Update Strategy: A strategy for continuous monitoring, retraining, and defense mechanisms to ensure model integrity.

5. What We Need from You (Client Requirements)

Model Specifications: Provide details of AI/ML models (e.g., architecture, algorithms used, training data).
Access to Training Data: Access to training datasets (either synthetic or real, depending on the scenario).
Model Deployment Information: Information regarding the deployment environment (cloud, on-prem, etc.).
API Access: Access to any exposed APIs or model-serving interfaces.
Regulatory & Privacy Requirements: Documentation of any specific privacy or regulatory frameworks (e.g., GDPR, HIPAA) the model must comply with.
Incident History: Historical security events or data breaches related to AI/ML systems.

6. Tools & Technology Stack

Adversarial Machine Learning Tools:
- IBM Adversarial Robustness 360, CleverHans for adversarial attack simulation.
- OpenAI Gym, PyTorch, TensorFlow for testing AI models against adversarial conditions.
Data Poisoning Testing:
- Poisoning Attack Frameworks: Custom scripts and libraries for testing model poisoning attacks.
- Scikit-learn, Keras, TensorFlow for poisoning simulations on models.
Privacy Testing Tools:
- Google’s Privacy Evaluation Toolkit, IBM Privacy and AI Toolkit for model inversion and membership inference testing.
- PySyft, PyTorch, for privacy-preserving machine learning model testing.
Penetration Testing & Vulnerability Scanning:
- OWASP ZAP, Burp Suite for testing APIs and interfaces.
- Metasploit, Wireshark for network and infrastructure security testing.
ML Model Security Monitoring:
- Alteryx, Seldon, for monitoring and logging model predictions and behavior in production environments.
- Prometheus, Grafana for performance and anomaly detection in deployed models.

7. Engagement Lifecycle

Kickoff & Scoping: Understand client’s AI/ML use cases, model details, deployment architecture, and security needs.
Model Evaluation & Threat Assessment: Identify potential vulnerabilities in models, including adversarial, poisoning, and privacy-related risks.
Adversarial & Poisoning Testing: Conduct adversarial testing and simulate data/model poisoning attacks.
Privacy & Confidentiality Review: Test for privacy risks such as model inversion and data leakage during predictions.
Penetration Testing: Test exposed APIs, model-serving environments, and interfaces for security vulnerabilities.
Security & Privacy Best Practices: Provide a set of actionable recommendations for securing the AI/ML lifecycle, model training, and deployment.
Model Defense Strategy: Propose methods to enhance model resilience, including adversarial training and anomaly detection.
Reporting & Recommendations: Provide a detailed report on the security, robustness, and privacy of AI/ML models, along with actionable improvements.

8. Why Sherlocked Security?

Feature	Sherlocked Advantage
Comprehensive AI/ML Security Review	End-to-end assessment of AI/ML model security, from training to deployment
Adversarial Attack Testing	Identify vulnerabilities through simulated adversarial and poisoning attacks
Privacy & Confidentiality Assessment	Ensure compliance with privacy regulations and prevent data leakage
Robustness & Resilience Testing	Stress-test models to identify weaknesses against perturbations and adversarial inputs
Penetration Testing for APIs & Interfaces	Simulate real-world attacks on AI/ML APIs and interfaces, ensuring integrity
Model Monitoring & Defense Strategies	Ongoing defense mechanisms to protect against evolving threats to ML models

9. Real-World Case Studies

Adversarial Attack Testing for Autonomous Vehicles

Client: A company developing AI models for autonomous vehicle navigation.
Challenge: Protect the vehicle’s AI model from adversarial attacks that could deceive the vehicle’s sensors or decision-making algorithms.
Solution: Conducted adversarial testing using FGSM and DeepFool techniques, identifying vulnerabilities in object detection and navigation models. Enhanced the models with adversarial training to improve robustness.
Outcome: Improved security of the autonomous vehicle’s AI system against adversarial inputs, ensuring safer deployment in real-world scenarios.

Poisoning Attack on Fraud Detection System

Client: A financial institution using machine learning for fraud detection.
Challenge: Prevent malicious actors from injecting poisoned data into the training set to influence fraud detection algorithms.
Solution: Simulated data poisoning attacks and analyzed the impact on model performance. Introduced data validation and anomaly detection measures to identify and mitigate poisoning attempts.
Outcome: Enhanced fraud detection model resilience against poisoning attacks, ensuring more accurate identification of fraudulent transactions.

10. SOP – Standard Operating Procedure

Initial Engagement: Gather client requirements and access details about the AI/ML models, training data, and deployment environments.
Model Security Assessment: Evaluate the model for adversarial vulnerabilities, poisoning risks, and privacy concerns.
Adversarial & Poisoning Testing: Simulate adversarial attacks and data poisoning to assess model robustness.
Privacy & Confidentiality Review: Test for privacy risks such as data leakage and model inversion.
Penetration Testing: Assess APIs and model-serving interfaces for vulnerabilities.
Security Best Practices: Provide recommendations for securing the AI/ML model lifecycle, including model training, deployment, and monitoring.
Model Defense Strategy: Recommend continuous monitoring and defensive techniques like adversarial training and anomaly detection.
Reporting & Recommendations: Provide a comprehensive report on the AI/ML model’s security, privacy, and robustness.

11. AI/ML Model Security & Poisoning Testing Readiness Checklist

1. Pre-Engagement Preparation

[ ] Access to AI/ML model architecture and training details
[ ] Dataset(s) used for training (either real or synthetic)
[ ] Model deployment information (cloud, on-prem, etc.)
[ ] API access for testing model interfaces
[ ] Documentation on privacy or regulatory requirements

2. During Engagement

[ ] Conduct adversarial attack testing on model inputs
[ ] Simulate data poisoning and backdoor attacks in training datasets
[ ] Test the model for vulnerabilities in API and model-serving environments
[ ] Evaluate privacy risks and model inversion scenarios
[ ] Perform penetration testing on exposed interfaces and APIs

3. Post-Review Actions

[ ] Provide findings and recommendations for improving model robustness and security
[ ] Enhance model resilience against adversarial and poisoning attacks
[ ] Implement privacy-preserving mechanisms to safeguard against data leakage
[ ] Update deployment practices to ensure secure model serving and API access

4. Continuous Improvement

[ ] Regularly retrain models to address new security threats
[ ] Monitor model performance and behavior for unusual activities or attacks
[ ] Implement ongoing testing for adversarial and poisoning risks
[ ] Update security protocols and best practices in the AI/ML development lifecycle
[ ] Continuously improve defensive strategies based on emerging threats

AI_ML Model Security & Poisoning Testing