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Hacking AI Systems

Test Your Skills

Multiple-Choice Questions

  1. What is the goal of defense evasion techniques used by adversaries?

    1. To enhance the performance of machine learning models

    2. To gain unauthorized access to machine learning systems

    3. To avoid detection by AI/ML-enabled security software

    4. To improve the accuracy of anomaly detection algorithms

  2. Which technique can adversaries use to prevent a machine learning model from correctly identifying the contents of data?

    1. Model replication

    2. Model extraction

    3. Craft adversarial data

    4. Inference API access

  3. What is the purpose of ML attack staging techniques?

    1. To gather information about the target system

    2. To manipulate business and operational processes

    3. To prepare for an attack on a machine learning model

    4. To exfiltrate sensitive information

  4. An adversary could create a network packet that looks like a normal packet to a machine learning model, but that contains malicious code. This packet could then be used to exploit a vulnerability on a target system. What is the technique used by the adversary?

    1. Reconnaissance.

    2. Evading an ML model.

    3. Exfiltration.

    4. None of these answers are correct.

  5. How can adversaries erode confidence in a machine learning system over time?

    1. By training proxy models

    2. By manipulating AI/ML artifacts

    3. By introducing backdoors into the model

    4. By degrading the model’s performance with adversarial data inputs

  6. What is the primary purpose of exfiltrating AI/ML artifacts?

    1. To gain access to AI/ML-enabled security software

    2. To manipulate the behavior of machine learning models

    3. To steal intellectual property and cause economic harm

    4. To enhance the performance of machine learning algorithms

  7. What is the potential privacy concern related to inferring the membership of a data sample in its training set?

    1. Disclosure of personally identifiable information

    2. Leakage of sensitive business operations

    3. Exposure of the machine learning model’s architecture

    4. Violation of data integrity within the ML system

  8. How can adversaries verify the efficacy of their attack on a machine learning model?

    1. By manipulating the training process of the model

    2. By training proxy models using the victim’s inference API

    3. By exfiltrating the model’s training data

    4. By exploiting vulnerabilities in the ML-enabled security software

  9. What is the purpose of adversarial data in the context of machine learning?

    1. To improve the interpretability of machine learning models

    2. To enhance the generalization capabilities of models

    3. To evaluate the robustness of machine learning algorithms

    4. To cause the model to produce incorrect or misleading results

  10. How can adversaries cause disruption or damage to machine learning systems?

    1. By training proxy models for performance improvement

    2. By manipulating ML artifacts for better accuracy

    3. By flooding the system with excessive requests

    4. By using ML artifacts to enhance the system’s capabilities

  11. What is the potential impact of adversarial data inputs on a machine learning system?

    1. Improved accuracy and reliability of the system

    2. Increased resilience against cyberattacks

    3. Decreased efficiency and degraded performance

    4. Enhanced interpretability of the model’s decisions

  12. How can adversaries use AI/ML model inference API access for exfiltration?

    1. By collecting inferences from the target model and using them as labels for training a separate model

    2. By manipulating the inputs to the inference API to extract private information embedded in the training data

    3. By stealing the model itself through the inference API

    4. By flooding the inference API with requests to disrupt the system

  13. What is the primary purpose of exfiltrating AI/ML artifacts via traditional cyberattack techniques?

    1. To improve the performance of the machine learning system

    2. To enhance the accuracy of anomaly detection algorithms

    3. To gain unauthorized access to AI/ML-enabled security software

    4. To steal valuable intellectual property and sensitive information using common practices

  14. What is the potential impact of flooding a machine learning system with useless queries or computationally expensive inputs?

    1. Improved accuracy and faster response time of the system

    2. Enhanced interpretability of the machine learning model

    3. Increased operational costs and resource exhaustion

    4. Reduced false positives in the system’s outputs

  15. What is the potential impact of eroding confidence in a machine learning system over time?

    1. Increased interpretability of the model’s decisions

    2. Enhanced accuracy and generalization capabilities

    3. Decreased trust and reliance on the system’s outputs

    4. Improved resilience against adversarial attacks

Exercise 5-1: Understanding the MITRE ATT&CK Framework

Objective: Research and explore MITRE ATT&CK Framework


  • Step 1. Visit the official MITRE ATT&CK website (attack.mitre.org).

  • Step 2. Familiarize yourself with the different tactics and techniques listed in the framework.

  • Step 3. Choose one specific technique from any tactic that interests you.

  • Step 4. Conduct further research on the chosen technique to understand its details, real-world examples, and potential mitigation strategies.

  • Step 5. Write a brief summary of your findings, including the technique’s description, its potential impact, and any recommended defensive measures.

Exercise 5-2: Exploring the MITRE ATLAS Framework

Objective: Explore the MITRE ATLAS Knowledge Base


  • Step 1. Visit the official MITRE ATLAS website (atlas.mitre.org).

  • Step 2. Explore the ATLAS knowledge base and its resources, including tactics, techniques, and case studies for machine learning systems.

  • Step 3. Select one specific technique or case study related to machine learning security that captures your interest.

  • Step 4. Research further on the chosen technique or case study to gain a deeper understanding of its context, implementation, and implications.

  • Step 5. Create a short presentation or a blog post summarizing the technique or case study, including its purpose, potential risks, and possible countermeasures.

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Last Update: November 17, 2020