AAISM AI Risk Management — Timed 10 Questions

Question 1

An organization is using an LLM-based agent. What is the primary risk associated with 'indirect prompt injection' in this environment?

Accepted Answer

External data retrieved by the agent contains instructions that override system prompts.. Indirect prompt injection occurs when malicious data is embedded in a retrieved document that instructs the LLM to perform unintended actions.

Answer

The model produces hallucinatory facts.

Answer

The API key for the LLM is exposed in the code.

Answer

The training data includes PII of users.

Question 2

Which TWO of the following are primary risks associated with 'membership inference attacks' against an AI model?

Accepted Answer

Exposure of sensitive information about training data subjects.. Membership inference attacks aim to determine if a specific data point was used in the training set, risking privacy leakage.

Answer

Unauthorized modification of model weights.

Answer

Unauthorized access to the model training algorithm.

Answer

Complete system shutdown via resource exhaustion.

Question 3

When utilizing a third-party AI vendor, which contractual requirement is most critical for addressing 'shadow AI' risks within an enterprise?

Accepted Answer

A clause requiring the vendor to disclose all sub-processors.. Standardized procurement processes and inventory tracking (shadow AI) require clear vendor auditing and accountability clauses.

Answer

A mandate to use the vendor's cloud region exclusively.

Answer

A request for a free trial period.

Answer

A requirement for 99.99% uptime SLA.

Question 4

When establishing a risk management framework for GenAI, which THREE factors should be prioritized to satisfy the NIST AI Risk Management Framework requirements?

Accepted Answer

Continuous monitoring of model performance metrics.. NIST AI RMF prioritizes trustworthiness, safety, and reliability through continuous monitoring and governance.

Answer

Requiring all models to be open-source.

Answer

Implementing a strict 'no external API' policy for all models.

Question 5

A machine learning model is showing signs of 'data drift'. What is the most effective initial step in the risk assessment process?

Accepted Answer

Analyze the statistical distribution of input production data versus training data.. Data drift monitoring involves comparing current production data distributions against training data distributions to detect performance decay.

Answer

Retrain the model immediately with all available data.

Answer

Shut down the application to prevent incorrect predictions.

Answer

Change the model architecture to a more complex design.

Question 6

An organization is performing threat modeling for a RAG (Retrieval-Augmented Generation) pipeline. Which specific vulnerability is unique to the retrieval component?

Accepted Answer

Database poisoning within the retrieved document index.. In RAG, if the search or retrieval index is manipulated, the LLM will generate answers based on attacker-supplied context.

Answer

Overfitting of the LLM on the training set.

Answer

Gradient-based evasion attacks on the LLM.

Answer

Prompt injection targeting the LLM model weights.

Question 7

During an adversarial threat modeling exercise for a facial recognition system, you identify a risk of pixel perturbation attacks. Which countermeasure is the most appropriate technical control to implement?

Accepted Answer

Conduct adversarial training by injecting perturbed samples into the training set.. Adversarial training involves training the model on examples containing perturbations, making it robust against those specific attacks.

Answer

Apply data masking to the input images.

Answer

Implement differential privacy during data preprocessing.

Answer

Increase the model epoch count to improve generalization.

Question 8

You are assessing a supply chain risk where an AI model uses a pre-trained base model from a third-party hub. Which action most effectively mitigates the risk of model poisoning during the integration phase?

Accepted Answer

Validate the model hash against the vendor's provided checksum manifest.. Verifying the cryptographic hash of the model weights against the provider's manifest ensures integrity before deployment.

Answer

Implement role-based access control for the training pipeline.

Answer

Run a sensitivity analysis on the model output.

Answer

Enable MFA for the model registry access.

Question 9

A developer is implementing 'Differential Privacy' on a training dataset. What is the primary trade-off they are managing?

Accepted Answer

Privacy budget (epsilon) vs. model utility/accuracy.. Differential privacy adds noise to training data to preserve privacy, which inherently reduces the utility or accuracy of the model.

Answer

Model training speed vs. model cost.

Answer

Security of the training pipeline vs. developer productivity.

Answer

Deployment latency vs. model scalability.

Question 10

What is the primary objective of a 'Bias Audit' in an AI risk management program?

Accepted Answer

To identify disparate impacts on protected populations.. Bias audits ensure that AI models do not produce discriminatory outcomes based on protected classes.

Answer

To verify the model meets legal data storage requirements.

Answer

To reduce the cost of model deployment.

Answer

To ensure the model is computationally efficient.