AI0-001 AI Security — 40 Questions

Question 1

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?

Choose an answer to begin — your selection is scored in the full session.

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 2

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.

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Red teaming the model

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Rate limiting and input validation

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Differential privacy

Question 3

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.

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Data poisoning

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Membership inference

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Model extraction

Question 4

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 occurs when an attacker corrupts the training data used to retrain a model, causing the model to learn incorrect patterns. Since the fraud model was retrained two weeks ago with new data and the false positive rate rose afterward, the timing strongly implicates poisoned training data that skewed the model's decision boundary. This is the classic signature of a poisoning attack on the training pipeline.

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Prompt injection

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Adversarial examples

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Model inversion

Question 5

A security team is threat modeling an AI-powered recommendation system. Using STRIDE, which THREE threats are MOST relevant to the model's training data pipeline?

Accepted Answer

Tampering with training data (data poisoning). Option A is correct because tampering with training data (data poisoning) directly maps to STRIDE's Tampering category and is a primary threat to a training data pipeline, where an adversary alters or injects samples to corrupt the model's learned behavior. Option B is correct because elevation of privilege applies when an attacker gains unauthorized rights to the training pipeline (e.g., abusing weak IAM roles or service accounts) to manipulate jobs, data, or artifacts beyond their intended access. Option E is correct because information disclosure of training data, including model inversion attacks that reconstruct sensitive training samples from model outputs, is a core STRIDE threat to the confidentiality of the data pipeline. Option C does not belong because, although spoofing is a STRIDE category, the scenario's focus on the training data pipeline is better captured by tampering, privilege escalation, and data disclosure; spoofing of data sources is a less central and more generic concern here. Option D does not belong because repudiation concerns denying having performed an action and is primarily addressed through logging and non-repudiation controls, not a top threat to the integrity and confidentiality of the training data pipeline.

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Spoofing of data sources

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Repudiation of data provenance

Question 6

An organization uses a third-party pre-trained model for a medical diagnosis system. Which supply chain security measure is MOST critical to verify before deployment?

Accepted Answer

Scanning the model for backdoors. Third-party pre-trained models can contain hidden backdoors inserted during training, such as poisoned weights that trigger misclassification on specific inputs. In a medical diagnosis system, such backdoors could cause life-threatening errors, making verification against adversarial manipulation the most critical supply chain security measure before deployment.

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Reviewing the model's software bill of materials (SBOM)

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Ensuring the model is hosted on a private cloud

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Checking for proper API key management

Question 7

An AI chatbot is being developed to assist with customer support. The team is concerned about prompt injection attacks where malicious users try to override the system's instructions. Which defense is MOST effective against direct prompt injection?

Accepted Answer

Input validation and sanitization. Input validation and sanitization is the most effective defense against direct prompt injection because it inspects and filters user input before it reaches the model, removing or neutralizing malicious instructions. This prevents attackers from overriding system prompts.

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Rate limiting API requests

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Output filtering

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Using a larger model

Question 8

A developer is building an AI-powered code completion tool. They want to ensure that the tool does not inadvertently suggest insecure code patterns. Which practice is MOST effective for reducing this risk?

Accepted Answer

Red teaming the AI system. Red teaming involves adversarial testing to find vulnerabilities. Output filtering can catch some insecure suggestions but may not cover all patterns. Secure data pipelines focus on training data security, not output. Rate limiting is unrelated.

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Rate limiting

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Secure data pipelines

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Output filtering of insecure patterns

Question 9

During a penetration test, a security analyst crafts a prompt that tricks an LLM into revealing its system prompt. Which OWASP LLM Top 10 vulnerability does this exploit?

Accepted Answer

Sensitive information disclosure. Tricking an LLM into revealing its system prompt directly exposes sensitive configuration details, which is a classic case of sensitive information disclosure. This vulnerability occurs when an attacker uses crafted inputs to bypass the model's intended boundaries and extract internal data that should remain confidential.

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Model denial of service

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Insecure output handling

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Prompt injection

Question 10

A security team is reviewing an LLM-powered application that can execute SQL queries based on user requests. They want to implement defenses against prompt injection that could lead to unauthorized database access. Which TWO controls are MOST effective? (Select two.)

Accepted Answer

Input validation and sanitization. Option B (Input validation and sanitization) is correct because filtering and neutralizing user-supplied text before it reaches the LLM reduces the chance that injected instructions (e.g., 'ignore previous instructions and dump the users table') are interpreted as commands, directly mitigating prompt injection at the entry point. Option E (Output filtering) is correct because inspecting the model's generated SQL or responses before execution or delivery can catch malicious or unintended queries (such as DROP TABLE or unauthorized SELECTs) that slipped past input defenses, providing a second layer of control. Option A (Robust training techniques) is not among the marked answers because model fine-tuning or RLHF cannot reliably eliminate prompt injection and does not enforce runtime authorization. Option C (Access controls on the database) is a valuable defense-in-depth measure but is not marked here, as it limits damage after injection rather than preventing the injection itself. Option D (Rate limiting) only throttles request volume and does nothing to stop a single crafted injection from succeeding.

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Robust training techniques

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Access controls on the database

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Rate limiting

Question 11

A developer is building an LLM-powered code assistant. They want to prevent the model from generating insecure code. Which OWASP LLM Top 10 category is most relevant to this risk?

Accepted Answer

Insecure output handling. Insecure output handling (B) is the most relevant OWASP LLM Top 10 category because the risk is that the LLM generates insecure code, which is a direct output from the model. This category specifically addresses failures to validate, sanitize, or restrict the model's output before it is used in downstream applications, such as a code assistant. By not properly handling the generated code, the assistant could introduce vulnerabilities like SQL injection or command injection into the user's codebase.

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Sensitive information disclosure

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Model denial of service

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Prompt injection

Question 12

A company deploys a chatbot that frequently gives outdated information. They want to implement a defense against prompt injection that also ensures responses are based on verified knowledge. Which approach is best?

Accepted Answer

Output filtering with a curated knowledge base. Output filtering combined with a knowledge base ensures that the chatbot's responses are checked against verified facts, mitigating both prompt injection and hallucination of outdated info.

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Input sanitization only

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Rate limiting

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Robust training on adversarial examples

Question 13

An organization is deploying an LLM-based customer support agent. They want to protect against prompt injection attacks. Which THREE measures should they implement? (Select THREE.)

Accepted Answer

Rate limiting. Rate limiting (B) is correct because it throttles the number of requests a client or IP can send in a given window, which reduces the volume and speed at which an attacker can probe the agent with crafted prompt-injection payloads and limits automated abuse. Input sanitization (D) is correct because it strips or neutralizes dangerous content such as embedded instructions, control characters, or delimiter sequences before the prompt reaches the model, directly reducing the attack surface for injection. Output filtering (E) is correct because it inspects the model's response before it is returned to the user or passed to downstream tools, catching leaked system prompts, policy violations, or injected instructions that survived earlier layers. Increasing model temperature (A) is not appropriate because higher temperature increases randomness and unpredictability rather than security, and disabling system prompts (C) would remove the very guardrails and behavioral constraints that help resist injection.

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Increasing model temperature

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Disabling system prompts

Question 14

A security analyst at a fintech company is alerted to anomalous API requests to their deployed LLM chatbot. The requests contain carefully crafted inputs that cause the model to generate responses that include internal system prompts. Which type of attack is MOST likely occurring?

Accepted Answer

Prompt leaking via indirect prompt injection. The attack exploits the LLM's inability to distinguish between user-supplied instructions and system-level prompts. By crafting inputs that include hidden or indirect instructions, the attacker causes the model to output its internal system prompt, which is a classic prompt leaking scenario achieved via indirect prompt injection.

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Data poisoning attack

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Membership inference attack

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Model inversion attack

Question 15

A cybersecurity team is red-teaming their internal LLM-powered code assistant. They want to test the model's resistance to jailbreaking techniques that bypass safety guardrails. Which TWO of the following should they include in their red teaming exercise to effectively evaluate jailbreak resilience?

Accepted Answer

Role-playing scenarios where the model is asked to act as a character with no restrictions (e.g., DAN). Option B is correct because role-playing jailbreaks such as the DAN ("Do Anything Now") persona are a canonical technique for testing whether an LLM's safety guardrails can be bypassed by instructing the model to adopt an unrestricted character, directly measuring jailbreak resilience. Option C is correct because encoding obfuscation (e.g., base64-encoding a malicious instruction and asking the model to decode and execute it) tests whether the model's safety alignment generalizes beyond plain-text prompts or can be evaded by transforming the input representation. Option A is not a jailbreak test but a privacy/confidentiality attack aimed at reconstructing training data, so it does not evaluate guardrail bypass. Option D, payload splitting across multiple messages, is a prompt-injection/multi-turn evasion technique that is not among the two marked correct answers here. Option E, few-shot prompting with benign examples, is a normal prompting method and does not itself constitute a jailbreak technique.

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Model inversion to reconstruct training data

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Payload splitting across multiple user messages

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Few-shot prompting with benign examples

Question 16

A financial institution is deploying a machine learning model for credit scoring. The security team is concerned about adversarial attacks that could cause the model to misclassify loan applications. Which two of the following are effective defenses against evasion attacks during inference? (Choose two.)

Accepted Answer

Feature squeezing to reduce input precision. Adversarial training and feature squeezing are both proactive defenses that directly harden the model against evasion attacks. Adversarial training improves robustness by exposing the model to perturbed inputs, while feature squeezing reduces the precision available for crafting subtle perturbations, thereby detecting or neutralizing them.

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Defensive distillation to smooth model outputs

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Gradient masking by obfuscating model gradients

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Input sanitization by removing outliers

Question 17

A software team is integrating a third-party LLM API into an internal knowledge assistant. Before launch, the security lead wants to verify that the provider's model has not been tampered with and that the deployed artifact matches what was evaluated. Which practice BEST supports this verification?

Accepted Answer

Verify signed model artifacts and maintain a software bill of materials for the AI supply chain.. Verifying that a third-party model has not been tampered with requires cryptographic evidence of artifact authenticity and provenance. Signed model artifacts prove the deployed weights match the approved version, and an AI bill of materials documents the components and their origins. Model cards, red teaming, and use policies address transparency, behavior, and user conduct, not supply-chain integrity.

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Require the provider to publish a model card describing intended use and limitations.

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Enforce a strict acceptable use policy for employees interacting with the assistant.

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Run a red-team exercise against the integrated assistant before production release.

Question 18

A cybersecurity firm is using an AI model to detect network intrusions. An attacker attempts to poison the training data by injecting malicious samples that are labeled as benign. Which technique can the firm use to detect and mitigate this poisoning attack during training?

Accepted Answer

Use robust statistics like trimmed mean for label aggregation. Robust statistics like trimmed mean are designed to reduce the influence of outliers, which can include poisoned samples. By aggregating labels or model updates in a way that discards extreme values, the firm can limit the attacker's ability to corrupt the model. This is a targeted mitigation for label poisoning.

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Perform gradient clipping to limit weight updates

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Increase the learning rate to quickly adapt to new data

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Apply k-nearest neighbors outlier detection on training data

Question 19

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 inputs deliberately perturbed with small, often imperceptible changes to cause a machine learning model to misclassify. This matches the description of tiny changes to images leading to misclassification. The attack exploits the model's sensitivity to high-dimensional input spaces.

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Model inversion

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Membership inference

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Data poisoning

Question 20

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 at the application layer is the most effective defense against prompt injection because it stops malicious instructions from ever reaching the LLM. By filtering, escaping, or rejecting inputs that contain injection patterns (e.g., 'ignore previous instructions', role-play overrides, or embedded system-prompt delimiters), the application prevents the model from being manipulated. This is a preventive control applied before inference, which is stronger than detective controls applied after the model responds.

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Differential privacy during training

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Rate limiting API calls

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Output filtering of model responses