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Introduction:
The digital reconnaissance phase of bug bounty hunting and penetration testing just received a massive upgrade with QueryGen, a powerful Go-based tool that transforms Nuclei vulnerability templates into precision search engine queries. This revolutionary approach automates target discovery across six major search platforms, enabling security researchers to identify potentially vulnerable assets with unprecedented efficiency and scale.
Learning Objectives:
- Master QueryGen installation, configuration, and multi-engine query generation
- Learn to integrate QueryGen into automated bug bounty pipelines and workflows
- Understand how to validate QueryGen results with Nuclei scanning and manual exploitation
You Should Know:
1. QueryGen Installation and Basic Operation
Install QueryGen from GitHub git clone https://github.com/querygen/querygen cd querygen go build -o querygen main.go sudo mv querygen /usr/local/bin/ Verify installation querygen --version Basic syntax structure querygen -engine [shodan|google|censys|fofa|hunter|zoomeye] -severity [critical|high|medium|low|info|all] [bash]
QueryGen compiles directly from source using Go, ensuring compatibility across platforms. The installation process involves cloning the repository, building the binary, and moving it to your system PATH. Verification confirms proper installation, while the syntax structure demonstrates the tool’s consistent command-line interface across all supported search engines.
2. Multi-Engine Query Generation Techniques
Generate critical severity queries for Shodan querygen -engine shodan -severity critical Combine multiple severity levels for Google dorking querygen -engine google -severity critical,high Output all severity queries for Censys in silent mode querygen -engine censys -severity all --silent Save queries to file for later use querygen -engine fofa -severity medium,low > fofa_queries.txt
This approach allows researchers to tailor their reconnaissance to specific risk levels and search platforms. The severity filtering ensures focus on high-impact vulnerabilities, while silent mode enables seamless integration into automated pipelines. Saving outputs to files facilitates batch processing and documentation of hunting methodologies.
3. Advanced Query Pipeline Integration
Pipe QueryGen output directly to search tools
querygen -engine shodan -severity critical | xargs -I {} shodan search {} --limit 10
Generate and execute ZoomEye queries automatically
querygen -engine zoomeye -severity high | while read query; do
zoomeye search "$query" --num 20 >> results.json
done
Create automated scanning workflow
querygen -engine hunter -severity all --silent | tee hunter_queries.lst
cat hunter_queries.lst | parallel -j 4 'nuclei -u {} -t /nuclei-templates/'
These advanced pipelines demonstrate QueryGen’s true power in automated workflows. By combining query generation with parallel execution and immediate validation scanning, researchers can create end-to-end reconnaissance systems that dramatically reduce manual effort while increasing coverage and discovery rates.
4. Shodan-Specific Query Applications
Example Shodan query output from QueryGen ssl:krazeplanet.com http.title:"Dashboard [bash]" Manual Shodan search with QueryGen output shodan search 'ssl:krazeplanet.com http.title:"Dashboard [bash]"' --fields ip_str,port,org --separator , Bulk Shodan query processing querygen -engine shodan -severity critical,high | shodan download --limit 1000 results shodan parse --fields ip_str,port results.json.gz > targets.txt
Shodan queries generated by QueryGen focus on specific SSL certificates and service banners that indicate vulnerable applications. The bulk processing capabilities allow researchers to download extensive result sets, parse relevant target information, and prepare for subsequent vulnerability validation phases.
5. Google Dorking Automation
QueryGen Google dork examples inurl:/api/v1/users admin intitle:"Apache Tomcat" "Manager GUI" filetype:env "DB_PASSWORD" Automated Google searching with lynx querygen -engine google -severity critical | while read dork; do lynx -dump "https://www.google.com/search?q=$dork" | grep -oP 'https?://[^&]+' | grep -v google done Site-specific dork generation querygen -engine google -severity high | sed 's/$/ site:target.com/' | tee google_target_dorks.txt
QueryGen revolutionizes traditional Google dorking by automatically generating sophisticated search queries from vulnerability templates. The automation scripts demonstrate how to programmatically execute these dorks while filtering out irrelevant results, enabling scalable discovery of exposed sensitive files and administrative interfaces.
6. Cross-Platform Query Translation
Convert between search engine syntaxes automatically
querygen -engine shodan -severity medium | querygen --convert-to fofa
Compare query results across platforms
for engine in shodan fofa censys; do
querygen -engine $engine -severity critical | head -5 > ${engine}_comparison.txt
done
Unified multi-engine search approach
engines=("shodan" "fofa" "censys")
for engine in "${engines[@]}"; do
querygen -engine $engine -severity high | search_tool_$engine
done
QueryGen’s intelligent query conversion capabilities allow researchers to leverage the unique strengths of each search platform without manual syntax translation. This cross-platform approach ensures comprehensive coverage and helps identify discrepancies in indexing between different search engines.
7. Nuclei Validation Integration
Direct pipeline from QueryGen to Nuclei scanning
querygen -engine shodan -severity critical | shodan search --fields ip_str,port - | awk '{print $1":"$2}' | nuclei -t /nuclei-templates/ -l -
Targeted template validation
querygen -engine fofa -severity high | fofa search --fields ip,port | while read target; do
nuclei -u $target -t /nuclei-templates/exposures/ -o nuclei_results.json
done
Continuous monitoring setup
while true; do
querygen -engine zoomeye -severity critical,high | zoomeye search --fields ip,port | nuclei -t /nuclei-templates/cves/ -rl 10
sleep 3600
done
The ultimate validation of QueryGen’s effectiveness comes through Nuclei scanning. These pipelines demonstrate how to automatically transition from target discovery to vulnerability confirmation, creating a seamless workflow from reconnaissance to validated findings ready for bug bounty reporting or remediation.
What Undercode Say:
- QueryGen represents a paradigm shift in automated reconnaissance, reducing hours of manual query crafting to seconds of automated generation
- The tool’s multi-engine approach eliminates platform-specific knowledge barriers, democratizing advanced search techniques
- Integration with existing Nuclei ecosystems creates an end-to-end vulnerability discovery pipeline previously unavailable to most researchers
QueryGen fundamentally changes the economics of bug bounty hunting by dramatically reducing the time investment required for comprehensive reconnaissance. The tool’s ability to automatically generate platform-specific queries from standardized vulnerability templates represents a significant advancement in security automation. By bridging the gap between vulnerability definitions and active discovery, QueryGen enables researchers to scale their efforts across multiple search platforms simultaneously, potentially increasing finding rates while maintaining methodological consistency. The seamless integration with validation tools like Nuclei creates a closed-loop system that could accelerate vulnerability discovery timelines across the entire security community.
Prediction:
QueryGen’s methodology will catalyze a new generation of intelligent reconnaissance tools that further abstract the technical complexities of multi-platform searching. Within two years, we anticipate integrated AI-assisted query refinement, real-time result validation, and automated exploit suggestion becoming standard features in the bug bounty toolkit. This evolution will likely force organizations to enhance their external attack surface management strategies as automated discovery tools become more accessible and effective, potentially leading to a short-term increase in reported vulnerabilities followed by long-term improvements in overall security posture as exposure reduction becomes more systematic.
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IT/Security Reporter URL:
Reported By: Rix4uni Bugbounty – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
