Check whether a newly announced vulnerability affects us
Turns a new CVE or advisory into an exposure check, urgency call, and action plan for your environment.
The prompt
Help me work out whether a newly announced vulnerability affects us and how urgently to act.
Advisory or CVE details: [PASTE the vendor advisory, CVE description, affected versions, fixed versions, workarounds]
Exploit status I've seen: [e.g. "listed in CISA KEV", "public proof of concept", "no known exploitation" - with source]
Our potentially affected assets: [PASTE inventory: product, version, count, internet-facing or internal]
Our environment notes: [compensating controls, configurations that may matter]
Steps:
1. Summarize the vulnerability in plain words: what an attacker could do, what access they need, and what conditions must be true.
2. Compare affected versions and conditions to our inventory. For each asset: affected / not affected / unknown, with the reason.
3. Rate our urgency (emergency / this week / normal patch cycle) based on exploitation status, exposure, and asset criticality. Explain the rating.
4. Action plan: patch or upgrade path, interim mitigations or workarounds from the advisory, and how to confirm the fix applied.
5. Detection: what to look for in logs to check whether it was exploited before we patched.
6. Questions to answer where data is missing (e.g. "Is feature X enabled?").
Rules: use only the advisory text and data I provided. Do not invent version numbers, exploit details, or IOCs; mark gaps [VERIFY WITH VENDOR ADVISORY].
Return: a one-paragraph summary for my manager, the asset table (Asset | Version | Affected? | Exposure | Action | Owner), and the action checklist.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Research & Summaries prompts
What to fill in
- compensating controls, configurations that may matter
- VERIFY WITH VENDOR ADVISORY
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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