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Audit Linux password and login security settings

A checklist-driven review of password policy, PAM, account aging, and file permissions on Linux servers, with safe fixes.

Software DevelopmentFew-ShotStandardAnalyzeFill-in Template

The prompt

Help me audit password and login security settings on Linux systems I administer. Distribution and version: [e.g. "Ubuntu 22.04", "RHEL 9"] Standard to measure against: [e.g. "CIS Benchmark", "our internal policy", "NIST SP 800-63B"] Config excerpts: [PASTE relevant lines from /etc/login.defs, /etc/security/pwquality.conf, /etc/pam.d/ files, /etc/ssh/sshd_config, and output of `ls -l /etc/passwd /etc/shadow /etc/group /etc/gshadow` - remove all hashes and real usernames first] How accounts are managed: [local only / LDAP / SSO / mixed] Check and report on: 1. Password quality: minimum length, complexity or banned-password rules, history and reuse prevention. 2. Password aging: max/min days and warning days, and whether they match my standard (note that NIST now discourages forced periodic changes without cause). 3. Hashing algorithm in use (e.g. yescrypt, SHA-512) and rounds. 4. Lockout after failed attempts (pam_faillock or equivalent) and unlock time. 5. File permissions and ownership on the account files. 6. SSH: root login, password vs key authentication, empty passwords. 7. Accounts with empty passwords, UID 0 other than root, or inactive accounts. Return a findings table: Control | Current setting | Expected | Status (Pass / Fail / Unclear) | Risk | Fix (exact config line or command) | Test before rollout? Then give a short audit script that only reads settings and changes nothing. Warn me before any fix that could lock users out (PAM and SSH changes), and recommend keeping a second session open while testing. Only run this on systems I am authorized to audit.

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What to fill in

  • e.g. "Ubuntu 22.04", "RHEL 9"
  • local only / LDAP / SSO / mixed

How this prompt is built

This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.

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