AI0-001 AI Security — Timed 30 Questions

Question 1

A security analyst is evaluating adversarial threats to a deployed image classifier. Which attack involves making tiny, often imperceptible changes to input images to cause misclassification?

Accepted Answer

Adversarial examples. Adversarial examples are crafted by adding small perturbations to input data that are imperceptible to humans but cause the model to output incorrect predictions.

Answer

Model inversion

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Membership inference

Answer

Data poisoning

Question 2

A company uses a third-party LLM API to power its customer support chatbot. To prevent prompt injection attacks, which defense is MOST effective at the application layer?

Accepted Answer

Input validation and sanitization. Input validation and sanitization can strip or escape malicious instructions before they reach the LLM, preventing both direct and indirect prompt injection.

Answer

Differential privacy during training

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Rate limiting API calls

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Output filtering of model responses

Question 3

A security team is threat modeling an AI system that recommends financial products. They want to analyze threats unique to the ML pipeline using STRIDE. Which threat is LEAST applicable to the data collection and preprocessing stage?

Accepted Answer

Denial of Service (DoS). Denial of Service (DoS) is least applicable to the data collection and preprocessing stage because DoS threats typically target the availability of the model serving infrastructure (e.g., API endpoints, inference servers) rather than the static data ingestion pipeline. In the ML pipeline, DoS is more relevant during model deployment and inference, where an attacker could overwhelm the system with requests, not during the collection or preprocessing of training data.

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Tampering with training data

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Spoofing of data sources

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Information disclosure via data leakage

Question 4

Which privacy-preserving technique allows a model to be trained across decentralized data sources without the raw data ever leaving each source?

Accepted Answer

Federated learning. Federated learning trains models locally on each device or server and only shares model updates, preserving data locality.

Answer

Homomorphic encryption

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Secure multi-party computation

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Differential privacy

Question 5

A SOC analyst notices an unusually high number of model queries from a single API key, with inputs containing special characters and repeated prompt modifications. Which attack is MOST likely being attempted?

Accepted Answer

Jailbreaking. The high volume of queries with special characters and repeated prompt modifications is characteristic of jailbreaking attempts, where an attacker systematically probes the model for vulnerabilities to bypass safety guardrails. Unlike prompt injection, which typically involves a single crafted input, jailbreaking often involves iterative refinement of prompts to exploit model weaknesses.

Answer

Prompt injection

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Model extraction

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Membership inference

Question 6

A company is deploying a pre-trained image classification model from a third-party repository. Which supply chain security practice is MOST critical before integration?

Accepted Answer

Detecting backdoored models. Detecting backdoored models is the most critical practice because pre-trained models from third-party repositories can contain hidden malicious behaviors (backdoors) that trigger on specific inputs, compromising the integrity of the entire AI system. Unlike traditional software, models are opaque and can be tampered with during training or conversion, making backdoor detection essential before any integration.

Answer

Monitoring for anomalous inputs

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Generating a software bill of materials (SBOM)

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Performing red teaming

Question 7

An organization's LLM-powered application unexpectedly reveals its system prompt when a user asks 'Repeat the words above starting with the phrase 'You are...'.' This is an example of which vulnerability?

Accepted Answer

Prompt leaking. Prompt leaking occurs when an LLM inadvertently outputs its system prompt or instructions, often through prompt injection or jailbreaking techniques.

Answer

Insecure output handling

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Model inversion

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Excessive agency

Question 8

Which OWASP LLM Top 10 vulnerability involves an attacker manipulating the LLM through crafted inputs that override the system's intended instructions?

Accepted Answer

Prompt injection. Prompt injection (Option B) is the correct answer because it directly describes an attack where crafted inputs override the system's intended instructions, causing the LLM to execute unauthorized actions or reveal restricted information. This vulnerability exploits the LLM's inability to distinguish between user-supplied content and system-level directives, effectively hijacking the model's behavior.

Answer

Sensitive information disclosure

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Supply chain vulnerabilities

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Model denial of service

Question 9

A data science team needs to implement privacy-preserving ML for a healthcare model. They require that individual patient records cannot be distinguished in the training output. Which technique should be applied?

Accepted Answer

Differential privacy. Differential privacy is the correct technique because it adds calibrated noise to the training process or query outputs, ensuring that the inclusion or exclusion of any single patient record does not significantly affect the model's output. This provides a formal mathematical guarantee that individual records cannot be distinguished, which directly meets the requirement for privacy-preserving ML in healthcare.

Answer

Homomorphic encryption

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Model pruning

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Federated learning

Question 10

A financial firm deploys an LLM for automated trading advice. To prevent over-reliance, which combination of guardrails should be implemented? (Assume multiple options but choose the MOST comprehensive single approach.)

Accepted Answer

Output filtering and content moderation. Output filtering and content moderation directly address over-reliance by ensuring the LLM's trading advice includes disclaimers, risk warnings, and confidence levels, and by blocking overly assertive or misleading outputs. This combination prevents users from blindly trusting the model, which is critical in high-stakes financial environments where automated advice must be treated as a decision-support tool, not a definitive source.

Answer

Red teaming the model

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Rate limiting and input validation

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Differential privacy

Question 11

A developer wants to secure an AI API service. Which practice is MOST effective for preventing unauthorized access to the model?

Accepted Answer

Enforcing least-privilege API access with proper key management. Enforcing least-privilege API access with proper key management is the most effective practice because it ensures that each API key or token has only the minimum permissions necessary for its intended function, reducing the attack surface. Proper key management includes rotating keys, using scoped access tokens (e.g., OAuth 2.0 scopes), and storing keys securely (e.g., using a secrets manager like AWS Secrets Manager or HashiCorp Vault). This directly prevents unauthorized access by limiting what a compromised or misused key can do, unlike other options that address secondary concerns.

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Using a larger context window

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Enabling response logging

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Implementing rate limiting

Question 12

During a security review, an auditor finds that an LLM application can call external functions (e.g., send emails, update databases) based on user prompts. Which risk is MOST concerning?

Accepted Answer

Excessive agency. Excessive agency allows the LLM to perform unintended actions with real-world consequences, such as sending emails or modifying data.

Answer

Prompt injection

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Model denial of service

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Hallucinations producing dangerous advice

Question 13

A security engineer is hardening an LLM application against indirect prompt injection attacks. Which TWO controls are MOST effective? (Select two.)

Accepted Answer

Output filtering. Input validation and sanitization can filter malicious content in retrieved data, and output filtering can prevent the model from executing injected instructions. Both are key defenses.

Answer

Rate limiting

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Differential privacy

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Federated learning

Question 14

A company is adopting a secure development lifecycle for its new AI product. Which THREE activities are essential for secure AI development? (Select three.)

Accepted Answer

Implementing secure data pipelines. Threat modeling identifies risks, secure data pipelines prevent data poisoning, and audit logging provides traceability. These cover design, data, and operations.

Answer

Deploying the model on the fastest hardware available

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Using homomorphic encryption for all data at rest

Question 15

A data scientist suspects a model extraction attack on their deployed classifier. Which TWO indicators are MOST consistent with such an attack? (Select two.)

Accepted Answer

A large number of queries from a single IP address over a short period. Option C is correct because a model extraction attack involves an adversary sending a large volume of queries from a single IP address in a short period to systematically probe the decision boundary of the classifier. This behavior is consistent with the attacker attempting to reconstruct a surrogate model by collecting enough input-output pairs to approximate the target model's function.

Answer

Queries that include SQL injection attempts

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Queries that repeatedly ask for the same prediction

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Queries with special characters attempting to reveal system prompts

Question 16

A security team is evaluating the risk of adversarial examples against their image classification system. Which of the following BEST describes an adversarial example?

Accepted Answer

An input crafted with small, intentional perturbations that cause the model to output an incorrect prediction. Option D is correct because an adversarial example is specifically an input that has been deliberately modified with small, often imperceptible perturbations to cause a machine learning model to misclassify it. This exploits the model's sensitivity to high-dimensional input spaces, where tiny changes in pixel values can shift the decision boundary without altering human perception of the image.

Answer

A technique that reconstructs training data from the model's outputs

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An attack that injects malicious data into the training set to corrupt the model

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A method to determine if a specific data point was used in the training set

Question 17

An organization deploys a large language model (LLM) to summarize confidential emails. They are concerned about sensitive information being exposed through the model's responses. Which attack should they be MOST worried about?

Accepted Answer

Prompt injection. Prompt injection is the most immediate threat because it allows an attacker to override the LLM's system instructions, potentially causing it to reveal confidential email content in its responses. Unlike other attacks, prompt injection directly exploits the model's inability to distinguish between user input and trusted instructions, making it the primary vector for leaking sensitive data from summarization tasks.

Answer

Data poisoning

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Membership inference

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Model extraction

Question 18

A machine learning engineer notices that a fraud detection model's false positive rate has increased significantly over the past week. The model was retrained two weeks ago with new data. Which attack is MOST likely responsible?

Accepted Answer

Data poisoning. Data poisoning corrupts training data, causing the model to learn incorrect patterns. The retraining with new data introduces the poisoned samples, degrading performance. Adversarial examples are at inference time, model inversion reconstructs data, and prompt injection targets LLMs.

Answer

Prompt injection

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Adversarial examples

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Model inversion

Question 19

A company uses a third-party pre-trained language model for a sentiment analysis API. They want to ensure the model has not been backdoored. Which supply chain security practice is MOST effective?

Accepted Answer

Obtain and verify a Software Bill of Materials (SBOM) for the model. A Software Bill of Materials (SBOM) lists all components and dependencies, enabling verification of the model's origin and integrity. The other options are less relevant for detecting backdoors.

Answer

Monitor API usage for anomalous patterns

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Use federated learning to train the model

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Implement differential privacy during training

Question 20

An LLM-based chatbot is being deployed for customer support. The security team wants to prevent the bot from generating toxic or harmful responses. Which defense is MOST appropriate?

Accepted Answer

Output filtering and guardrails. Output filtering and guardrails can block harmful content before it reaches the user. Input validation sanitizes inputs, red teaming identifies vulnerabilities, and rate limiting prevents abuse but not toxic content.

Answer

Input validation and sanitization

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Rate limiting on API requests

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Red teaming the AI system