# Dorking for Bug Bounty: Multi-Engine Reconnaissance & Automation
Dorking for Bug Bounty: Multi-Engine Reconnaissance & Automation
"The highest-leverage recon is usually not 'more tools' — it's better questions encoded as search queries. I've earned over $50,000 from dorking alone."
Dorking is one of those techniques that separates casual hunters from consistent earners. It's the art of weaponizing search engines to find what organizations accidentally exposed.
Used well, it lets you:
- ➜Discover undocumented bug bounty/VDP programs before they hit platforms
- ➜Expand attack surface into forgotten subdomains and legacy apps
- ➜Unearth P1-level exposures: secrets, backups, internal docs, staging portals
- ➜All while staying passive and relatively low-noise
But the moment you try to scale it manually across Google, Bing, DuckDuckGo, Yandex, GitHub and archives, you hit the same wall:
- ➜Re-typing similar dorks
- ➜Copy-pasting domains into query strings
- ➜Losing track of which engines and patterns you already ran
That's where a dedicated recon helper like recon.sanjokkarki.com.np fits naturally into a modern bug bounty workflow: centralizing your dorking mindset and making it repeatable.
This post goes deep into the methodology of dork-driven recon, and shows how to structure it so your automation tool becomes an extension of your thought process.
//Table of Contents
- 1.Why Dorking Still Prints Money in 2026
- 2.Core Operator Concepts Across Search Engines
- 3.Google Dorking Fundamentals for Bug Bounty
- 4.Advanced Google Workspace & SaaS Pattern Dorking
- 5.Bing, DuckDuckGo & Yandex: Going Beyond Google
- 6.GitHub Dorking for Secrets & Hidden Attack Surface
- 7.From Manual to Automated: Where recon.sanjokkarki.com.np Fits
- 8.A Practical Multi-Engine Dorking Playbook
- 9.Real-World Scenarios: From Single Dork to High-Impact Bug
- 10.The Dork Cheatsheet (Copy-Paste Ready)
- 11.Common Pitfalls, ToS Risks & Ethics
- 12.References & Resources
//1. Why Dorking Still Prints Money in 2026
For all the noise around bleeding-edge exploits, a lot of serious bug bounty payouts still come from publicly indexed mistakes:
| Finding Type | Typical Severity | Average Payout |
|---|---|---|
| Leaked AWS/GCP credentials | Critical | $5,000-$25,000 |
| Exposed .env with DB creds | Critical | $3,000-$15,000 |
| Internal docs with secrets | High | $1,000-$5,000 |
| Admin panel discovery | Medium-High | $500-$3,000 |
| Sensitive file disclosure | Medium | $200-$1,000 |
Search engines and code hosts have already done the heavy lifting:
- ➜Crawling the web
- ➜Indexing content, file types, titles, fragments
- ➜Making that index queryable with powerful operators
Your job is to weaponize those operators.
The catch:
- ➜Each engine (Google, Bing, DuckDuckGo, Yandex, GitHub) has its own quirks
- ➜The best dorks are domain and stack-specific, not just generic copy/paste lists
- ➜Without structure, you waste hours re-doing the same searches or drowning in noise
A purpose-built workflow, supported by a tool like recon.sanjokkarki.com.np, gives you:
- ➜A personal, evolving library of dorks
- ➜One place to parameterize queries by domain/keyword
- ➜A way to fan out across multiple engines while keeping your process reproducible
//2. Core Operator Concepts Across Search Engines
Most search engines share a common mental model. If you internalize these operators once, you can adapt them to any platform:
▶2.1 The Universal Building Blocks
| Operator | Purpose | Example |
|---|---|---|
| site: | Constrain to domain/subdomain | site:*.example.com |
| inurl: | Match strings in URL | inurl:admin |
| intitle: | Match strings in page title | intitle:"index of" |
| intext: | Match strings in body | intext:password |
| filetype: / ext: | Filter by file type | filetype:pdf |
| -term | Exclude results | -site:www.example.com |
| OR / | Boolean OR | admin OR login |
| "quotes" | Exact phrase match | "AWS_SECRET_ACCESS_KEY" |
Pro tip: Think of each dork as encoding a hypothesis:
- ➜"This org probably left staging environments online"
- ➜"Their developers likely committed .env files with secrets"
- ➜"They use Google Workspace and forgot to lock down internal docs"
Your dorks become structured questions you ask the global index.
▶2.2 Engine-Specific Strengths
| Engine | Best For | Unique Operators |
|---|---|---|
| Deepest index, mature dork ecosystem | cache:, related:, daterange: | |
| Bing | Older content, regional coverage | contains:, feed:, ip: |
| DuckDuckGo | Privacy-focused, bang shortcuts | !g, !b, !gh for pivoting |
| Yandex | CIS/Eastern Europe, fine-grained filters | host:, domain:, mime:, date: |
| GitHub | Secrets, configs, internal URLs | org:, path:, filename:, language: |
//3. Google Dorking Fundamentals for Bug Bounty
Google remains the workhorse for OSINT-driven recon. The dork ecosystem for bounty is mature and well-documented.
▶3.1 Finding Bug Bounty / VDP Programs
First use case: discovery of programs before they hit platforms.
inurl:/security "bug bounty"
inurl:/security.txt "bug bounty"
inurl:/.well-known/security.txt "bounty" OR "reward"
"responsible disclosure" "vulnerability" "reward"
Non-platform programs (self-hosted):
"bug bounty" "responsible disclosure" -site:hackerone.com -site:bugcrowd.com -site:intigriti.com
Pro tip: Run these weekly. New programs appear constantly, and being first gives you the cleanest attack surface.
▶3.2 Expanding Attack Surface for a Known Target
Once you have a target, switch into surface expansion mode. These are your baseline queries:
Subdomain discovery (excluding noise):
site:*.example.com -www -support -help -docs -blog
URLs with parameters (XSS, IDOR, redirects):
site:example.com inurl:? -inurl:logout -inurl:signout
Admin and login hints:
site:*.example.com inurl:admin | inurl:login | inurl:dashboard | inurl:panel
Juicy file extensions:
site:example.com ext:log | ext:txt | ext:conf | ext:env | ext:bak | ext:sql | ext:json | ext:yml
Directory listings:
site:example.com intitle:"index of" backup | .git | .svn | uploads | temp
▶3.3 Bug-Class-Specific Dorks
Craft dorks around particular vulnerability types:
XSS injection points:
site:example.com inurl:q= | inurl:s= | inurl:search= | inurl:query= | inurl:keyword=
Open redirect candidates:
site:example.com inurl:redirect= | inurl:url= | inurl:next= | inurl:return= | inurl:dest= inurl:http
File upload endpoints:
site:*.example.com inurl:upload | inurl:uploads | inurl:media | inurl:files | inurl:attachment
API documentation (often reveals internal endpoints):
site:example.com inurl:api | inurl:swagger | inurl:graphql | inurl:v1 | inurl:v2
//4. Advanced Google Workspace & SaaS Pattern Dorking
The real magic starts when you think in terms of SaaS URL patterns instead of just "domain + extension".
Many organizations use Google Workspace, which generates highly structured URLs:
sites.google.com/a/<domain>/...
docs.google.com/a/<domain>/...
drive.google.com/a/<domain>/...
groups.google.com/a/<domain>/...
spreadsheets.google.com/a/<domain>/...
▶4.1 Google Workspace Dorks
Internal sites:
site:sites.google.com inurl:/a/company.com
Docs, Sheets, Slides:
site:docs.google.com inurl:/a/company.com
site:spreadsheets.google.com inurl:/a/company.com
Groups (mailing lists - goldmine for internal info):
site:groups.google.com inurl:/a/company.com
Drive files:
site:drive.google.com inurl:/a/company.com
This can expose:
- ➜Internal handbooks and onboarding guides
- ➜Incident response docs, SSO configs, architectural diagrams
- ➜Shared sheets with access URLs, tokens, or internal endpoints
▶4.2 Other SaaS Patterns
Atlassian (Confluence/Jira):
site:*.atlassian.net "company.com"
site:*.atlassian.net intitle:"company" inurl:wiki
Notion:
site:notion.so "company.com" | "company name"
Slack (public channels indexed):
site:*.slack.com "company.com"
Trello (public boards):
site:trello.com "company.com" | "company name"
//5. Bing, DuckDuckGo & Yandex: Going Beyond Google
Relying on Google alone leaves money on the table. Different engines index and rank content differently.
▶5.1 Bing
Bing supports familiar operators plus some extras:
Unique Bing operators:
contains:pdf # Pages linking to PDFs
ip:192.168.1.1 # Search by IP (rare but useful)
feed:security # Find RSS feeds
Example Bing dorks:
site:example.com filetype:pdf "internal" | "confidential"
site:*.example.com ext:docx "password" | "credential" | "API key"
Bing often surfaces older documents Google has dropped and different regional content.
▶5.2 Yandex - The Underrated Engine
Yandex's query language gives very fine-grained control:
| Operator | Purpose |
|---|---|
| host: | Specific subdomain only |
| domain: | All TLDs (domain:com) |
| url: | URL prefix match |
| mime: | MIME type (not just extension) |
| date: | Date filtering |
Example Yandex dorks:
site:example.com mime:pdf "internal use only" | "for official use only"
host:dev.example.com "index of"
site:example.com date:>2025-01-01 "password"
Pro tip: Yandex has excellent coverage for Eastern Europe, CIS countries, and content Google deprioritizes.
▶5.3 DuckDuckGo
DDG's real power for hunters is bangs - shortcuts to other engines:
!g site:example.com ext:env # Google
!b site:example.com filetype:pdf # Bing
!gh org:example filename:.env # GitHub
!wa example.com # Wayback Machine
Use DDG as a privacy-friendly launcher for dorks into other engines.
//6. GitHub Dorking for Secrets & Hidden Attack Surface
If web search engines are about public websites, GitHub search is about developers' mistakes.
▶6.1 GitHub Search Operators
| Operator | Purpose | Example |
|---|---|---|
| org: | GitHub organization | org:microsoft |
| user: | Specific user | user:johnsmith |
| repo: | Repository | repo:company/api |
| path: | File path | path:config |
| filename: | File name | filename:.env |
| extension: | File extension | extension:json |
| language: | Programming language | language:python |
▶6.2 High-Value GitHub Dorks
Cloud credentials (AWS, GCP, Azure):
org:example AWS_ACCESS_KEY_ID | AWS_SECRET_ACCESS_KEY
org:example AZURE_CLIENT_SECRET | AZURE_TENANT_ID
org:example GOOGLE_API_KEY | GCP_PROJECT | GCLOUD
Payment processors:
org:example sk_live_ | pk_live_ # Stripe
org:example PAYPAL_CLIENT_SECRET
org:example SQUARE_ACCESS_TOKEN
Communication services:
org:example SLACK_BOT_TOKEN | SLACK_WEBHOOK_URL
org:example TWILIO_ACCOUNT_SID | TWILIO_AUTH_TOKEN
org:example SENDGRID_API_KEY
Database credentials:
org:example DB_PASSWORD | DATABASE_URL | MONGO_URI
org:example POSTGRES_PASSWORD | MYSQL_ROOT_PASSWORD
org:example REDIS_URL | REDIS_PASSWORD
Config and env files:
org:example filename:.env | filename:.env.local | filename:.env.production
org:example filename:config.yml | filename:secrets.json
org:example filename:credentials | filename:htpasswd
Internal endpoints and APIs:
org:example "api.internal" | "admin.internal" | "staging"
org:example /api/v1/ | /api/v2/ | /graphql
▶6.3 Validating Secrets
For every apparent secret:
- 1.Identify the provider (AWS, GCP, Stripe, Twilio, etc.)
- 2.Use lightweight validation - most providers have verification endpoints
- 3.Confirm scope - read vs write, internal vs production
- 4.Check program scope - some programs exclude employee personal repos
Never perform destructive actions - validate read access only.
//7. From Manual to Automated: Where recon.sanjokkarki.com.np Fits
At this point the workflow looks roughly like:
- ➜5+ major search targets (Google, Bing, DDG, Yandex, GitHub, archives)
- ➜Dozens of dork templates across categories
- ➜Multiple targets in parallel
Doing this manually quickly turns into:
- ➜Repetitive, error-prone copy-paste
- ➜Inconsistent coverage per target
- ➜No easy way to revisit a target with the same dork set
A dedicated tool such as recon.sanjokkarki.com.np solves the coordination problem:
▶What the Tool Provides
| Feature | Benefit |
|---|---|
| Parameterized templates | {{domain}}, {{org}}, {{keyword}} placeholders |
| Engine-specific variants | Same idea, correct syntax per engine |
| Collections/categories | Organized by discovery, secrets, files, SaaS |
| One-click execution | Opens the right search endpoint instantly |
| Personal dork library | Your patterns, always accessible |
The tool doesn't replace your brain; it removes friction between your brain and the search engines.
▶Workflow Integration
1. Enter target: example.com
2. Select category: "Sensitive Files"
3. Choose engines: Google, Bing, Yandex
4. Click "Execute" → Opens all queries in tabs
5. Review results, note findings
6. Repeat with next category
This turns a 30-minute manual process into 5 minutes of focused review.
//8. A Practical Multi-Engine Dorking Playbook
Here's a concrete methodology you can follow. It becomes much more powerful once captured in a tool.
▶Step 0: Confirm Scope & Legal Boundaries
Before touching any search bar:
- ➜Read the program's scope and exclusions carefully
- ➜Clarify third-party SaaS inclusion (Google Docs, Slack, Jira)
- ➜Check if employee personal GitHub repos are excluded
- ➜Make sure your dorking only targets in-scope assets and already public data
▶Step 1: Identify All Official Names & Domains
"<company name>" "bug bounty"
"<company name>" "responsible disclosure"
"<brand>" "security.txt"
Enumerate:
- ➜Primary domain(s) and obvious subdomains
- ➜Official GitHub org and major repositories
- ➜Any owned SaaS tenants explicitly in scope
▶Step 2: Run Baseline Google Dork Set
For each target domain, run these sequentially:
Surface expansion:
site:*.example.com -www -support -help -docs
Parametrized URLs:
site:example.com inurl:?
Admin/auth paths:
site:*.example.com inurl:admin | inurl:login | inurl:dashboard
File & directory leaks:
site:example.com ext:log | ext:txt | ext:conf | ext:env | ext:bak | ext:sql
site:example.com intitle:"index of" backup | .git | .svn
▶Step 3: SaaS & Google Workspace Pattern Dorking
If the target uses Google Workspace:
site:docs.google.com inurl:/a/company.com
site:spreadsheets.google.com inurl:/a/company.com
site:sites.google.com inurl:/a/company.com
site:groups.google.com inurl:/a/company.com
site:drive.google.com inurl:/a/company.com
▶Step 4: Cross-Check with Bing & Yandex
Take your highest-value hypotheses and replay them:
Bing:
site:example.com filetype:pdf "internal" | "confidential"
site:*.example.com ext:docx "password" | "credential"
Yandex:
site:example.com mime:pdf "internal use only"
host:dev.example.com "index of"
Record differences - domains or paths only visible on certain engines.
▶Step 5: GitHub Dorking for the Org
org:example AWS_ACCESS_KEY_ID | AWS_SECRET_ACCESS_KEY
org:example sk_live_ | pk_live_
org:example filename:.env | filename:.env.local
org:example filename:config.yml | filename:secrets.json
▶Step 6: Triage, Validate, and Report
For each finding:
- 1.Document the exact dork used
- 2.Explain how an attacker could reproduce it
- 3.Establish business impact with concrete abuse scenarios
- 4.Map it to program scope and severity guidelines
//9. Real-World Scenarios: From Single Dork to High-Impact Bug
▶Scenario 1: Exposed Incident Playbook ($2,500)
Dork used:
site:docs.google.com inurl:/a/company.com "incident response" | "playbook"
Found: A doc describing internal on-call rotation, IP whitelists, and emergency admin URLs.
Impact: Attacker gains deep insight into detection/response capabilities.
Report: "Information disclosure via misconfigured document sharing"
▶Scenario 2: Legacy XSS via Parameter Discovery ($1,200)
Dork used:
site:legacy.example.com inurl:?
Found: https://legacy.example.com/search.php?q=keyword
Testing: Reflected XSS confirmed with basic payload.
Report: XSS with session theft potential in legacy internal tool.
▶Scenario 3: Live AWS Credentials in Public Repo ($15,000)
Dork used:
org:example AWS_ACCESS_KEY_ID | AWS_SECRET_ACCESS_KEY
Found: config/production.env in an old service repo with active credentials.
Validation: Confirmed S3 read/write access to production bucket.
Report: Critical - full S3 bucket access including customer data.
▶Scenario 4: Admin Panel via Directory Listing ($800)
Dork used:
site:example.com intitle:"index of" admin | backup
Found: /admin/backup/ with database dumps containing user credentials.
Report: Sensitive data exposure via misconfigured directory listing.
//10. The Dork Cheatsheet (Copy-Paste Ready)
▶Discovery Dorks
inurl:/security "bug bounty"
inurl:/security.txt "bug bounty"
"responsible disclosure" "vulnerability" -site:hackerone.com -site:bugcrowd.com
▶Surface Expansion
site:*.TARGET.com -www -support -docs
site:TARGET.com inurl:?
site:*.TARGET.com inurl:admin | inurl:login | inurl:dashboard
▶File Leaks
site:TARGET.com ext:log | ext:env | ext:bak | ext:sql | ext:conf
site:TARGET.com intitle:"index of" backup | .git | uploads
site:TARGET.com filetype:pdf "internal" | "confidential"
▶SaaS Patterns
site:docs.google.com inurl:/a/TARGET.com
site:trello.com "TARGET.com"
site:notion.so "TARGET"
site:*.atlassian.net "TARGET"
▶GitHub Secrets
org:TARGET AWS_ACCESS_KEY_ID | AWS_SECRET_ACCESS_KEY
org:TARGET sk_live_ | pk_live_
org:TARGET filename:.env | filename:config.yml
org:TARGET DB_PASSWORD | DATABASE_URL
▶Bug-Class Specific
site:TARGET.com inurl:redirect= | inurl:url= | inurl:next= inurl:http
site:TARGET.com inurl:q= | inurl:search= | inurl:query=
site:TARGET.com inurl:upload | inurl:file | inurl:download
//11. Common Pitfalls, ToS Risks & Ethics
▶Pitfalls to Avoid
| Pitfall | Risk | Mitigation |
|---|---|---|
| Over-automation against Google | CAPTCHAs, bans, ToS violation | Use manual review, rate limit |
| Mass data harvesting | Legal issues, ethical concerns | Gather only proof of concept |
| Scope violations | Disqualification, ban | Re-read scope before each session |
| Reporting stale data | Wasted time, reputation | Always validate findings |
▶Ethical Dorking Means
- ➜Staying strictly inside announced scope
- ➜Respecting ToS and legal boundaries
- ➜Minimizing collateral damage
- ➜Reporting responsibly with clear reproduction steps
- ➜Never accessing data beyond what's needed to prove impact
//12. References & Resources
▶Tools
- ➜recon.sanjokkarki.com.np - Multi-engine dork automation: https://recon.sanjokkarki.com.np
- ➜Google Hacking Database (GHDB) - Curated dork repository: https://www.exploit-db.com/google-hacking-database
- ➜GitDorker - GitHub dorking automation: https://github.com/obheda12/GitDorker
- ➜truffleHog - Git secret scanning: https://github.com/trufflesecurity/trufflehog
- ➜gitleaks - Secret detection: https://github.com/gitleaks/gitleaks
▶Learning Resources
- ➜OWASP Testing Guide - Information Gathering: https://owasp.org/www-project-web-security-testing-guide/
- ➜PortSwigger Web Security Academy: https://portswigger.net/web-security
- ➜Bug Bounty Bootcamp by Vickie Li
- ➜The Web Application Hacker's Handbook
▶Communities
- ➜HackerOne Hacktivity: https://hackerone.com/hacktivity
- ➜Bugcrowd Crowdstream: https://bugcrowd.com/crowdstream
- ➜r/bugbounty: https://reddit.com/r/bugbounty
//Final Thoughts
Dorking is not about memorizing huge lists of random strings.
It's about:
- ➜Understanding how search engines see the world
- ➜Translating your bug bounty hypotheses into precise search operators
- ➜Leveraging automation tools like recon.sanjokkarki.com.np to capture your personal dork library and execute it without friction
If you treat your dork set as code - versioned, organized, iterated - it becomes a core competitive advantage:
- ➜New bug class learned? Add patterns
- ➜New engine quirk discovered? Update templates
- ➜New SaaS pattern identified? Encode it
The more you blur the line between "search bar" and "exploit development", the more dorking becomes what it truly is: an offensive search language for the entire internet.
Happy hunting! 🎯