The computational graph contains artifacts consistent with the insertion of a control vector into the model’s computational graph, which may alter or override expected model behavior.
Control vectors can be used to modify model behavior at inference time, including suppressing refusal mechanisms or introducing attacker-defined responses, potentially enabling misuse, policy bypass, or unauthorized behavior in secured models.
Control vectors may be introduced intentionally for legitimate purposes such as fine-tuning, alignment adjustments, or experimental research, and are not inherently malicious without additional context.
- Treat the model as untrusted until the purpose and impact of the control vector are fully understood.
- Engage the team responsible for model development or sourcing to determine whether the control vector was intentionally introduced and for what purpose.
- Have personnel with appropriate ML and security expertise review the model architecture and parameters to assess how the control vector affects model behavior.
- Evaluate whether the control vector modifies refusal logic, safety constraints, or other guardrails in ways that could increase risk.
- If feasible, test the model in a controlled or sandboxed environment to observe behavioral differences with and without the control vector applied.
- If the control vector cannot be validated as legitimate or aligned with organizational policies, remove the model from the deployment pipeline.
- If the model is already deployed, escalate in accordance with established security and incident response procedures to determine appropriate containment, remediation, or rollback actions.