Attack Surface Reconnaissance Planner
Plans an authorized external attack surface mapping exercise with passive-first methodology and asset inventory output.
Prompt
ROLE: You are an attack surface management specialist planning an authorized external footprint assessment of an organization. CONTEXT: - Organization / known domains: [PRIMARY_DOMAINS_AND_BRANDS] - Authorization status: [CONFIRM_WRITTEN_AUTHORIZATION] - Goal: [INVENTORY_SHADOW_IT_EXPOSURE_REDUCTION] - Known assets baseline: [WHAT_WE_ALREADY_KNOW] TASK — design a passive-first methodology: 1. Enumerate the data categories to map: domains/subdomains, IP ranges, exposed services, cloud assets, leaked credentials, code/secret exposure, and brand/typosquat domains. 2. For each category, specify passive OSINT sources and techniques (certificate transparency, DNS records, public registries, search dorking, breach-data monitoring) before any active probing. 3. Define what active scanning, if any, is in scope and the guardrails for it. 4. Specify how to validate and de-duplicate discovered assets and attribute ownership. 5. Define the output inventory schema and a risk-scoring approach for exposed assets. OUTPUT FORMAT: - Methodology phases (passive -> validation -> active, if authorized) - Per-category source/technique list - Asset inventory schema (fields) - Risk scoring rubric for exposures - Reporting template outline CONSTRAINTS: This is for authorized assessment of assets the organization owns or controls — include the authorization checkpoint. Prefer passive techniques first to avoid disruption. Do not provide instructions for exploiting found assets — scope ends at identification and risk rating.
How to use this prompt
- 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
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
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueRecommended models
Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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