CyberCertPrepAI Security FundamentalsSecure AI Deployment & MLOpsEASYFree question
Scenario
During a security audit, a reviewer examines the models used by the NLP team. She searches Hugging Face Hub and finds the team's code references 'bert-base-uncasedd' - subtly different from the canonical 'bert-base-uncased'. The repository shows 847 downloads, was created 3 weeks ago by a user with zero other contributions, and the model card was copied verbatim from the real model.
A developer finds a Hugging Face repository named 'bert-base-uncasedd' (with a double 'd') that has a model with identical performance to the legitimate 'bert-base-uncased' but was uploaded by an unknown account 3 weeks ago. What attack is this most likely an example of?
- A.Model distillation theft - the uploader compressed the original model to reduce size
- B.Adversarial patch attack - the model contains hidden triggers for specific inputs; the major model hubs cryptographically verify publisher identity, making namespace squatting and lookalike repositories impossible
- C.Typosquatting - a malicious actor registers a name similar to a popular model to trick developers into downloading a potentially backdoored version
- D.Model inversion attack - the uploader reconstructed BERT's weights from API queries; API keys for inference endpoints are scoped read-only by every provider, meaning a leaked key cannot alter the served model