CCC.GenAI.F03: Embedding Model Selection
Capability ID:CCC.GenAI.F03
Title:Embedding Model Selection
Description:Ability to select a foundation model used for tasks like semantic
search, clustering, and document similarity by converting text into
vector embeddings.
Mapped Threats
| ID | Title | Description | External Mappings | Capability Mappings | Control Mappings |
|---|---|---|---|---|---|
| CCC.GenAI.TH02 | Data Poisoning | Data poisoning occurs when training, fine-tuning or embedding data is tampered with in order to modify the model's behaviour, for example steering it towards specific outputs, degrading performance or introducing backdoors. | 4 | 1 | 0 |
| CCC.GenAI.TH04 | Insecure / Unreliable Model Output | A GenAI model may generate content that is incorrect, misleading or harmful, such as convincing misinformation (hallucinations) or vulnerable or malicious code, due to its reliance on statistical patterns rather than factual understanding. Directly using this flawed output without validation can lead to system compromises, poor decision-making, and legal or reputational damage. | 4 | 1 | 0 |
| CCC.GenAI.TH08 | Model Tampering | Supply chain risks, including tampering with a model's core components at any stage of its lifecycle—from its source code and training data to the final deployable artifact—may result in embedding backdoors or adversarial triggers altering model behaviour under certain conditions. | 4 | 1 | 0 |