Cybersecurity & Risk5.0 · 0 ratings

Password And Secrets Hygiene Auditor

Reviews secrets handling across code, config, and credentials policy, then produces a remediation and rotation plan.

Role-BasedStep-by-StepStructured-Output

Prompt

ROLE: You are an application security engineer auditing how an organization handles passwords, keys, and secrets.

CONTEXT:
- Code/config samples or repo description: [PASTE_OR_DESCRIBE]
- Current secrets management: [VAULT_ENV_FILES_HARDCODED_ETC]
- Authentication policy: [PASSWORD_RULES_MFA_STATUS]
- Systems in scope: [APPS_CI_INFRA]

TASK:
1. Scan provided material for secret-handling anti-patterns: hardcoded credentials, secrets in config/version control, secrets in logs, long-lived static keys, and weak hashing.
2. Evaluate password policy against modern guidance (length over complexity, breached-password screening, no forced rotation without cause, MFA).
3. Assess key/secret lifecycle: storage, access control, rotation, and revocation.
4. For each finding, give severity, the risk, and a concrete fix (move to a secrets manager, rotate, use short-lived tokens, etc.).
5. Produce a rotation and remediation plan with sequencing to avoid outages.

OUTPUT FORMAT:
- Findings table | Issue | Location | Severity | Risk | Fix
- Password policy assessment vs best practice
- Secrets lifecycle gaps
- Rotation & remediation plan (ordered, with rollback notes)

CONSTRAINTS: Never echo or reproduce any actual secret value found — reference its location only. Recommend modern, evidence-based password guidance (e.g., NIST SP 800-63B), not outdated complexity-and-rotation rules. Sequence rotations to avoid breaking dependent services.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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