During a security review, a Linux DevOps team discovers that a VS Code plug-in sends their Kubernetes manifests to a public large language model (LLM) for advice on hardening. Some of the manifests still contain base64-encoded Secrets that hold private container-registry credentials. The team wants to keep using the plug-in but must prevent accidental credential exposure. Which action best mitigates this specific risk?
Add an automated pre-submission filter that masks or removes any values matching credential or secret patterns before the manifest is sent to the LLM.
Insert a comment in each manifest instructing the LLM not to reveal or retain embedded secrets.
Require the plug-in to use TLS with certificate pinning when calling the LLM's API endpoint.
Allow the plug-in only on a non-production Git branch that mirrors the manifests.
Sanitizing or redacting secret material before it is transmitted ensures the LLM never receives the data, eliminating the possibility that the model could store, leak, or later regenerate the credentials. Transport encryption (TLS) only protects data in transit, not after it reaches the provider. Embedding "do not disclose" comments relies on the model's voluntary compliance and does not stop the data from entering its context window. Restricting the plug-in to a non-production branch still exposes any secrets that appear in those files.
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What is base64 encoding and how does it relate to secrets in Kubernetes manifests?
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How does an automated pre-submission filter work to protect sensitive data?
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CompTIA Linux+ XK0-006 (V8)
Automation, Orchestration, and Scripting
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