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Introduction:
Manual IP reconnaissance often forces security professionals to juggle multiple tools—DNS resolvers, port scanners, web probes, and vulnerability scanners—leading to fragmented data and wasted time. By leveraging large language models like Gemini CLI with custom instruction files, you can orchestrate a complete recon pipeline from domain resolution to vulnerability detection using a single natural language command. This article dissects a battle-tested automation chain that transforms “do iprecon on target.com” into a fully logged, actionable attack surface map.
Learning Objectives:
- Automate end‑to‑end IP reconnaissance using Gemini CLI and a custom `GEMINI.md` configuration.
- Integrate Rustscan, Nmap, httpx, and Nuclei into a unified, repeatable workflow.
- Systematically save all outputs for follow‑up analysis, reporting, or bug bounty submissions.
You Should Know:
1. Configuring Gemini CLI for Custom Recon Automation
The core of this automation is the `~/.gemini/GEMINI.md` file, which teaches Gemini how to execute your multi‑tool chain. Create or edit this file and append the following instruction block:
When user says "do iprecon on [bash]": 1. Resolve target to all associated IPs 2. Run rustscan/nmap on discovered IPs 3. Run httpx on IP:Port → find interesting URLs 4. Run nuclei on all IPs + services 5. Save all outputs with [bash] prefix → ready for follow-up
Step‑by‑step setup (Linux/macOS):
- Install Gemini CLI: `npm install -g @google/gemini-cli` (or follow official docs).
2. Create the config directory: `mkdir -p ~/.gemini`
- Open `~/.gemini/GEMINI.md` in a text editor and paste the above block.
- Save and exit. Now, running `gemini –yolo “do iprecon on example.com”` triggers the entire chain.
For Windows (WSL2 recommended): install Ubuntu WSL, then follow the Linux steps inside the WSL environment.
- Understanding the Recon Chain – Tools & Commands
Each step in the chain can be executed manually to verify or fine‑tune the process.
- Step 1 – Domain Resolution
Use `dig` or `nslookup` to find all A/AAAA records.dig +short example.com nslookup example.com
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Step 2 – Port Scanning with Rustscan/Nmap
Rustscan is extremely fast; Nmap provides deep service detection.rustscan -a 192.168.1.1 -- -sV -sC -oA rustscan_output Equivalent raw nmap: nmap -p- -sV -sC 192.168.1.1 -oA nmap_full
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Step 3 – Web Service Probing with httpx
Extract live web servers from IP:port lists.
cat ports.txt | httpx -silent -status-code -title -tech-detect -o live_urls.txt
- Step 4 – Vulnerability Scanning with Nuclei
Run Nuclei against discovered IPs and services.
nuclei -list targets.txt -severity low,medium,high,critical -o nuclei_results.txt
3. Windows / PowerShell Alternative Workflow
While many security tools are Linux‑native, you can achieve similar results on Windows using WSL or native ports.
Using WSL (recommended): Install Ubuntu from Microsoft Store, then inside WSL follow the Linux commands.
Native PowerShell approach (limited but possible for basic steps):
Resolve domain
Resolve-DnsName example.com | Select-Object IPAddress
Port scan with Test-NetConnection (very slow)
1..1024 | ForEach-Object { Test-NetConnection example.com -Port $_ -InformationLevel Quiet }
For full recon, use WSL or install nmap for Windows (from nmap.org)
To streamline, configure Gemini CLI inside WSL and call it from PowerShell using wsl gemini --yolo "do iprecon on target.com".
- Extending the Workflow for API Security & Cloud Hardening
Modern bug bounty programs demand API and cloud asset discovery. Add these steps to yourGEMINI.md:
5a. Run `ffuf` or `dirsearch` on discovered web roots to find API endpoints. 5b. Check for common cloud metadata endpoints (169.254.169.254). 5c. Enumerate open S3 buckets with `bucket_finder` or <code>s3scanner</code>.
Example commands:
API endpoint brute‑forcing ffuf -u https://example.com/FUZZ -w /usr/share/wordlists/api_common.txt -o api_endpoints.txt AWS metadata check curl -s http://169.254.169.254/latest/meta-data/iam/security-credentials/ S3 bucket enumeration s3scanner -bucket example -output bucket_results.txt
Cloud hardening tip: To protect your own cloud assets from such recon, disable unused metadata endpoints, use strict IAM roles, and implement S3 bucket policies that deny public listing.
5. Mitigation Strategies – Defending Against Automated Recon
Blue teams can detect and block Gemini‑driven recon chains by:
- Rate limiting source IPs that trigger rapid DNS queries or port scans.
- Deploying a WAF with rules that flag sequential scanning patterns (e.g., more than 100 unique ports in 10 seconds).
- Using IDS/IPS signatures for tools like Nuclei (many templates have known user‑agents or request patterns).
- Honeypots: Create fake open ports or web endpoints that, when probed, automatically blacklist the attacker.
Sample iptables rate limit for incoming scans:
iptables -A INPUT -p tcp --dport 1:65535 -m recent --update --seconds 60 --hitcount 10 -j DROP
For cloud environments (AWS Security Group / Azure NSG), implement burst‑based limits via automation (e.g., Lambda that adds temporary deny rules).
- Building Battle‑Tested Workflows – Training and Continuous Learning
The original post references a live training course (limited‑time offer: 4000 INR) that promises “50+ live classes, real targets, real bounties” and “No class ends without reporting bugs.” While this article provides the automation foundation, advanced techniques like:
- Chaining Gemini with custom nuclei templates
- Post‑processing outputs with `jq` and `gron` for structured data
- Integrating Slack/Telegram notifications for critical findings
are best learned through hands‑on mentorship. The course link is: https://lnkd.in/duGK-7pe. Such training accelerates mastery of the “battle‑tested workflows” mentioned by the author.
To independently improve, study the tool documentation:
7. Complete One‑Command Example & Output Management
After setting up `GEMINI.md`, execute:
gemini --yolo "do iprecon on tesla.com"
Gemini will sequentially run the tools and save outputs with the prefix tesla.com_:
tesla.com_ips.txt tesla.com_ports.txt tesla.com_httpx_urls.txt tesla.com_nuclei_results.txt
Pro tip: Add a sixth step to `GEMINI.md` that automatically runs `grep -r “critical” .txt` and sends a desktop notification using `notify-send` (Linux) or PowerShell’s `BurntToast` module (Windows).
What Undercode Say:
- Key Takeaway 1: AI‑driven orchestration (Gemini CLI + instruction files) transforms fragmented recon into a single natural language command, drastically reducing time from target to vulnerability.
- Key Takeaway 2: The modular chain (DNS → port scan → web probe → vuln scan) mirrors professional bug bounty workflows and can be extended with API discovery and cloud hardening checks.
Analysis: This approach democratizes advanced recon—even junior testers can run sophisticated pipelines without memorizing every tool flag. However, defenders must evolve: traditional static defenses fail against coordinated automation. Expect to see AI‑augmented red teams and blue teams emerging, where custom instruction files become as critical as shell scripts. The training course referenced fills a genuine gap: knowing how to chain tools is one thing; knowing how to interpret results, avoid false positives, and report professionally is what turns automation into bounties.
Prediction:
Within 18 months, most bug bounty hunters will rely on AI‑powered orchestration layers for first‑pass reconnaissance. This shift will force platforms (HackerOne, Bugcrowd) to implement AI‑resistant rate limiting and dynamic test‑case generation. Simultaneously, defensive AI will emerge that mimics human interaction patterns to waste attacker resources. The arms race will move from “which tool to use” to “how to write smarter instruction files and anti‑recon logic.” Ultimately, AI‑driven recon will become a standard checkbox in every security assessment, lowering the barrier to entry while raising the bar for sophisticated evasion.
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Reported By: Vaidikpandya Bugbounty – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
