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Introduction:
In the rapidly evolving landscape of cybersecurity, the lines between artificial intelligence and open-source intelligence (OSINT) are blurring to create powerful new tools for ethical hackers. Claude-OSINT is a groundbreaking project that transforms the Claude AI model into an elite external reconnaissance operator, providing bug bounty hunters and red teamers with over 90 structured recon modules to systematically map attack surfaces and identify vulnerabilities.
Learning Objectives:
- Master AI-driven reconnaissance workflows for authorized bug bounty engagements
- Learn to combine OSINT techniques with automated secret detection and dorking
- Implement structured attack surface mapping methodologies used by professional penetration testers
You Should Know:
1. Understanding Claude-OSINT and Its Core Architecture
Claude-OSINT is not just another OSINT tool—it’s a paired skills system designed for the Claude skills ecosystem. It consists of two primary `SKILL.md` files that transform Claude into a methodical recon specialist. The first, `osint-methodology` (1,694 lines), focuses on strategic thinking: asset-graph discipline, severity assessment, time budgeting, and identity-fabric mapping. The second, `offensive-osint` (4,168 lines), provides the tactical arsenal: probe paths, regex patterns, curl one-liners, and tool URLs.
The GitHub repository includes 90+ capabilities across 12 domains, 48 secret-regex patterns, 80+ Google dorks, 9 read-only credential validators, and 27 attack-path templates. With ~5,500 lines of structured tradecraft, it achieves a 96.9% PASS rate on a 32-prompt self-evaluation, covering approximately 85–90% of the recon phase for authorized engagements.
Step‑by‑Step Guide to Installing and Configuring Claude-OSINT
To leverage Claude-OSINT for your bug bounty operations, follow these steps:
1. Clone the repository to your local machine:
git clone https://github.com/elementalsouls/Claude-OSINT.git cd Claude-OSINT
2. Install the skills into your Claude environment:
mkdir -p ~/.claude/skills/ cp -r skills/ ~/.claude/skills/
3. Verify the installation:
ls ~/.claude/skills/ Expected output: offensive-osint/ osint-methodology/
- Load the skills in a Claude session by simply asking questions related to OSINT or reconnaissance; Claude will auto-detect relevant phrases and load the appropriate skill contexts.
2. Mastering GitHub Dorks and Secret Discovery
GitHub dorks are crafted search queries that leverage GitHub’s advanced search operators to locate exposed API keys, credentials, and misconfigured data in public repositories. Tools like GitHub Dorking Tool automate this process, using 25+ powerful dorks to scan multiple organizations for AWS, Google, Azure, PayPal, and Stripe keys. Similarly, SecretHunter provides regex-based detection for hardcoded secrets in JavaScript files, supporting multi-threaded scanning for speed and efficiency.
Step‑by‑Step Guide to Automating GitHub Dorking for Secret Hunting
1. Install GitHub Dorking Tool:
git clone https://github.com/myselfakash20/Github_Dorker.git cd Github_Dorker pip install requests
2. Set up your GitHub personal access token:
export GITHUB_TOKEN="your_personal_access_token_here"
3. Run the automated dorking script:
python3 github_dorker.py
The script will automatically scan for exposed secrets across public repositories using its built-in dork list.
- For advanced dorking, use GitDorker with a comprehensive dork file:
python3 GitDorker.py -d Dorks/alldorks.txt -tf tf/TOKENSFILE -q "example.com"
This command uses GitHub tokens from the tokens file and searches for content related to the target domain using 239 dorks from various sources.
3. Attack Surface Mapping with SurfaceMap and Claude-OSINT
Attack surface mapping is the process of identifying all external assets, entry points, and potential vulnerabilities associated with an organization. Modern tools like SurfaceMap combine 48+ OSINT data sources with LLM intelligence to build complete maps of an organization’s attack surface. The tool operates in three phases: Phase 0 (LLM brainstorm with web enrichment), Phase 1 (passive recon from 20+ concurrent OSINT sources), and Phase 2 (active probing including HTTP probing, port scanning, and Nuclei vulnerability scanning).
Step‑by‑Step Guide to Performing Comprehensive Attack Surface Mapping
1. Install SurfaceMap with all dependencies:
pip install surfacemap[bash]
- Set your Gemini API key for enhanced LLM analysis (optional but recommended):
surfacemap set-key GEMINI_API_KEY your-key-here
-
Run a full discovery scan on your target domain:
surfacemap discover example.com --json --mindmap
This command will generate an interactive HTML mindmap and export results in JSON format for further analysis.
4. For passive-only reconnaissance (faster, no active probing):
surfacemap discover example.com --passive-only --json
5. Start continuous monitoring for your target:
surfacemap monitor example.com
This will perform scheduled scans and send diff alerts via Slack when changes are detected.
4. Implementing Effective Secret Detection Patterns
Secret detection relies on regex patterns to identify API keys, tokens, credentials, and other sensitive data in code repositories and web applications. Modern secret detection engines use patterns like `AKIA[0-9A-Z]{16}` for AWS Access Keys, `sk-[A-Za-z0-9]{48}` for OpenAI API keys, and entropy-based detection for generic secrets.
Step‑by‑Step Guide to Building Your Own Secret Detection Engine
1. Create a patterns.json file with your detection rules:
[
{
"name": "AWS Access Key ID",
"regex": "AKIA[0-9A-Z]{16}",
"confidence": "high"
},
{
"name": "Generic API Key",
"regex": "(api[_-]?key|token|auth|secret)[\"'\s:=>]{1,10}[^\"'\s]+",
"confidence": "medium"
}
]
2. Use SecretHunter to scan target JavaScript files:
python secret-hunter.py -i urls.txt -p patterns.json -t 50
This command scans URLs from the input file using 50 threads, significantly speeding up the discovery process.
- For scanning local repositories, use a Python script with regex compilation:
import re import os</li> </ol> patterns = { "AWS_Key": re.compile(r"AKIA[0-9A-Z]{16}"), "OpenAI_Key": re.compile(r"sk-[A-Za-z0-9]{48}") } def scan_file(filepath): with open(filepath, 'r', errors='ignore') as f: content = f.read() for name, pattern in patterns.items(): if pattern.search(content): print(f"[!] Found {name} in {filepath}")5. Advanced Reconnaissance Methodology for Bug Bounty Hunting
Professional bug bounty hunting follows a structured methodology that begins with comprehensive reconnaissance to identify all cloud resources, subdomains, and endpoints associated with a target. The process typically includes passive recon using certificate transparency logs (e.g., crt.sh), active probing for live hosts, and automated testing for common vulnerabilities.
Step‑by‑Step Guide to Implementing a Professional Recon Workflow
- Begin with passive OSINT using certificate transparency logs:
curl "https://crt.sh/?q=%.example.com&output=json" | jq -r '.[].name_value' | sort -u > subdomains.txt
-
Use Claude-OSINT’s subdomain enumeration capabilities which include a 7-source fallback chain when primary sources fail.
-
Perform common-prefix subdomain sweeps using the provided PowerShell or bash scripts:
Bash example for common prefix sweep for prefix in $(cat common-prefixes.txt); do host "$prefix.example.com" 2>/dev/null | grep "has address" | cut -d " " -f 1 done
-
Enrich your findings with WHOIS, RDAP, and historical WHOIS data:
whois example.com | grep -E "Name Server|Registrar|Creation Date"
-
Map bulk IP addresses to ASNs using Cymru or RIPEstat:
for ip in $(cat ips.txt); do whois -h whois.cymru.com " $ip" | tail -1 done
What Undercode Say:
- Claude-OSINT bridges the critical gap between manual OSINT and automated reconnaissance, providing structured tradecraft that transforms AI into a true reconnaissance expert. This is a game-changer for bug bounty hunters who need to scale their operations without sacrificing methodology quality.
- The integration of 90+ recon modules, secret detection patterns, and attack-path templates creates a comprehensive toolkit that would take years for individual practitioners to develop independently. By making this openly available, the cybersecurity community gains access to enterprise-grade reconnaissance capabilities.
Prediction:
The emergence of AI-powered OSINT tools like Claude-OSINT signals a fundamental shift in how reconnaissance will be conducted over the next 2-3 years. We can expect to see widespread adoption of AI-driven recon workflows across both offensive and defensive security teams, with automated attack surface mapping becoming a standard prerequisite for all security assessments. As these tools mature, they will likely incorporate real-time threat intelligence feeds, automated exploitation chains, and predictive vulnerability identification—potentially reducing reconnaissance time by 70-80% while simultaneously improving coverage depth and accuracy. However, this democratization of advanced recon capabilities also raises important ethical considerations, as the same tools could be weaponized by malicious actors, necessitating robust access controls and responsible disclosure frameworks.
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- Begin with passive OSINT using certificate transparency logs:
