An AI practitioner performed continued pre-training on a foundation model (FM).

The verified answer is B. Data leakage. The model exposes sensitive company information because that information was inadvertently included in training data. AWS guidance warns that using sensitive data to train models can cause unintended disclosure of data from model output, including data leakage during inference. This is exactly the scenario: sensitive information entered the training process and later appeared in model output after deployment.

AWS security reference guidance for generative AI model customization identifies sensitive information disclosure as a threat where a model trained on datasets containing PII leaks sensitive information during inference. Although the question says company information rather than PII specifically, the same security principle applies: protected data included in training or continued pre-training can be memorized or reproduced by the model later.

Jailbreaking is incorrect because jailbreaking refers to attempts to bypass model safety controls through adversarial prompts. The problem here is not that a user bypassed safety controls; the sensitive information was already embedded through training data.

Contextual grounding is incorrect because grounding is a technique for improving answer accuracy by connecting model responses to trusted context, such as RAG sources. It is not a security risk.

Prompt injection is incorrect because prompt injection involves malicious input that attempts to override or manipulate model behavior at inference time. The question does not describe a malicious prompt. It describes accidental exposure from training data.

Therefore, the correct security risk is data leakage.