Configuration
Access Through Human Identity
Access Through Non-Human Identity
Autonomous Action Control
Connected Tools and Functions
Enterprise Retrieval Access
External Communication Access
Model and Build Provenance
Model Objective Alignment
Orchestrated AI System
Persistent Memory Access
Standing Instruction Stack
Vendor-Embedded AI
- ID: CF007
- Created: 26th August 2026
- Updated: 26th August 2026
- Contributors: Nimer Kees, James Weston,
Model and Build Provenance
Model and build provenance is the configuration that determines which model, fine-tune, system prompt, instruction data, build artifact, and distribution pipeline produce the synthetic subject. It includes whether those components are verified, signed, versioned, reproducible, and traceable to an approved source.
This configuration creates an elevated exposure condition because the synthetic subject’s behavior may be shaped before deployment. Malicious or unsafe behavior may be introduced through model weights, fine-tuning data, system prompts, training data, extension builds, dependency updates, or continuous integration and continuous delivery (CI/CD) pipelines.
The primary risk is latent or supply-chain-introduced behavior. A model, fine-tune, or build artifact may behave normally under most conditions, but change behavior when a trigger, date, keyword, context, or task appears. A compromised build or update pipeline may also inject instructions, code, or configuration that changes the synthetic subject’s behavior after review.
Investigators should review model provenance, fine-tune records, training and instruction data, system prompt versions, build artifacts, signatures, software bills of materials, CI/CD logs, deployment history, and runtime version records. Particular attention should be given to unapproved model changes, unsigned artifacts, unexplained behavior shifts, injected prompts, over-scoped CI/CD tokens, and actions that cannot be tied to a known model or prompt version.
Investigative Relevance
Model and build provenance is relevant because configuration begins before runtime. The synthetic subject may appear to follow its deployed directive, while the effective behavior was shaped by an earlier model, data, prompt, or supply-chain change.
This section is especially relevant where synthetic subjects rely on fine-tuned models, third-party model weights, vendor extensions, local models, custom system prompts, model updates, CI/CD-built agents, or distributed application artifacts.