How to Perform LLM API Reconnaissance – Mapping the AI Attack Surface Before You Test | Day 20

🤖 AI/LLM HACKING COURSE FREE Part of the AI/LLM Hacking Course — 90 Days Day 20 of 90 · 22.2% complete ⚠️ Authorised Targets Only: LLM API reconnaissance — including directory brute-forcing and JavaScript analysis — must only be performed against applications within your authorised scope. Passive traffic analysis and JavaScript review are always within scope; active brute-forcing requires explicit confirmation that it's permitted in the engagement rules. On an application security assessment last year, the brief listed one AI…

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LLM-Powered OSINT 2026 — Using AI to Automate Open Source Intelligence Gathering

Three hours of manual OSINT compressed into twenty minutes. That's the productivity difference I measure when I run LLMs in my professional reconnaissance workflow. Not because the AI does magic — it doesn't know anything your tools don't — but because it orchestrates, summarises, and chains tools together faster than any human analyst. It turns raw theHarvester output into structured intelligence. It cross-references Shodan results against the company's LinkedIn headcount. It spots the subdomain pattern that should have a staging…

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AI-Assisted Recon and Attack Surface Mapping 2026 — How hackers use LLMs to map attack surfaces faster

A senior penetration tester I know used to spend three hours on the recon phase of an assessment: running Amass, processing the subdomain list, checking Shodan for the scope's IP ranges, correlating the results, identifying the five or six most interesting targets before starting active testing. Now it takes forty minutes. The data collection phase takes the same time. The analysis and prioritisation — what used to take two hours — is thirty minutes of structured AI prompting and verification…

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