Make Prompt Changes Reviewable
Store prompt text in your own version-controlled repository with its purpose, owner, change rationale, and intended application flow. Point deployment configuration to a specific revision, and retain an approved revision for rollback. During review, highlight changes to disclosure instructions, identity handling, or tool-use language. Define acceptance criteria before release: which requests should succeed, which should be refused, and what data may appear in a reply. A prompt may describe these boundaries, but it must not decide whether a user is authorized. If a revision asks the model to infer access rights from a user's wording, hold the release and move that decision into server-side policy.
Keep Credentials and Tools Outside the Prompt
Keep credentials for NexoToken AI model API access in a controlled server environment, not in browser code, prompt text, or retrieved documents. Consult the current main platform documentation for integration details rather than assuming an endpoint or platform feature. Maintain a server-side allowlist of callable tools, and scope each invocation to the authenticated user's permitted resources. In a hypothetical support workflow, an assistant can receive an authorized ticket excerpt without receiving a general-purpose database credential. If a prompt revision requests a new tool or a broader result, review the tool's inputs, permissions, and returned fields first; do not deploy the revision until that access is approved.
Test Injection Boundaries and Redaction
Treat user messages, retrieved passages, and tool output as untrusted content, including text that claims to override application instructions. Redact secrets and unnecessary personal data before assembling a model request, then check the response against your disclosure rules before showing it to a user. For an explicitly hypothetical test, place an instruction in a ticket body asking the assistant to export another customer's records. Run the case yourself and record what the application actually did: whether the server blocked the query, what data reached the model, and what appeared in the reply. If the boundary fails, fix filtering or tool authorization at the source rather than relying solely on stronger prompt wording.
Release With Checks and a Rollback Path
For each candidate revision, prepare ordinary requests alongside unauthorized requests, injected content, and data that should be redacted. Run them under defined identities and record actual outcomes, including failures; do not treat an expected refusal as proof that it occurred. Release within a limited scope under your own deployment process and retain a way to restore the approved revision. Monitor application logs for revision identifiers, denied access, and redaction alerts. NexoToken usage history can help reconcile API activity, but it is not a substitute for your application's security audit trail. If a release exposes protected data, permits an unauthorized tool call, or produces an unexplained change, stop expansion, roll back, and investigate the recorded request path.
Sources & further reading
NexoToken platform and community
Platform website: https://www.nexotoken.net/
QQ community group: 822274386
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