hunt-idor
Hunt-idor is a reconnaissance and exploitation skill for identifying Insecure Direct Object Reference vulnerabilities in web applications. Use this skill when testing APIs, web services, and multi-tenant platforms for authorization bypasses that expose user data, financial records, or administrative resources through predictable or sequential identifiers in URLs, GraphQL queries, or request parameters.
git clone --depth 1 https://github.com/elementalsouls/Claude-BugHunter /tmp/hunt-idor && cp -r /tmp/hunt-idor/skills/hunt-idor ~/.claude/skills/hunt-idorSKILL.md
## Crown Jewel Targets
**Why IDOR pays big:**
- Direct access to other users' data without authentication bypass — clear, demonstrable impact
- Chains easily with privilege escalation, financial fraud, and account takeover
- Affects virtually every application with user-owned resources
**Highest-value asset types (by payout potential):**
| Asset Type | Why It Pays |
|---|---|
| Financial documents / billing APIs | PII + financial data exposure (Shopify, Uber, PayPal) |
| Private repositories / source code | IP theft, critical data loss (GitHub) |
| User messages / DMs | Privacy violation at scale (Reddit) |
| Account management endpoints | User addition, deletion, privilege escalation (PayPal, Mozilla) |
| Business/org administration | Cross-tenant escalation, employee PII (Uber) |
| Content moderation/admin actions | Operational sabotage (Reddit mod logs) |
**Programs that pay most for IDOR:**
- Platforms with multi-tenancy (SaaS, B2B tools)
- Fintech and payment processors
- Social platforms with private content
- Developer tools with org/repo isolation
---
## Attack Surface Signals
**URL patterns that scream IDOR:**
```
/api/v1/users/{id}/
/api/v*/orders/{order_id}
/invoices/download?id=
/reports/{uuid}/
/messages/{thread_id}
/admin/orgs/{org_id}/members
/migration/{migration_id}/files
/graphql (query params with IDs)
/api/business/{business_id}/
/vouchers/{voucher_id}/policy
```
**Response header signals:**
- `Content-Type: application/json` on endpoints accepting raw IDs
- No `X-Frame-Options` or CORS misconfigs paired with ID params
- `Authorization: Bearer` tokens that are user-scoped but hit org-level resources
**JavaScript source patterns:**
```javascript
// Look for hardcoded or interpolated IDs in JS
fetch(`/api/v1/users/${userId}/profile`)
axios.get('/invoices/' + invoiceId)
graphql query { billingDocument(id: $docId) }
// Redux/state stores exposing foreign IDs
state.currentUser.organizationId
```
**Tech stack signals:**
- GraphQL endpoints (query-based IDORs are often missed)
- REST APIs with sequential integer IDs (most vulnerable)
- UUIDs that are predictable or leaked in other responses
- Multi-tenant SaaS apps with `org_id`, `account_id`, `business_id` params
- Mobile apps (Burp the APK — mobile APIs often skip authorization checks)
---
## Step-by-Step Hunting Methodology
1. **Map all object references in the application**
- Browse every feature authenticated as User A
- Capture all requests in Burp Suite
- Filter for requests containing: `id=`, `_id=`, `uuid=`, `/v1/{noun}/{id}`, query params with numeric/UUID values
2. **Enumerate ID types**
- Sequential integers → enumerate ±1, ±100
- UUIDs → check if they appear in other responses or JS files
- Hashed IDs → check if leaked in public endpoints, metadata, or GraphQL introspection
3. **Create two separate accounts (same privilege level)**
- User A: resource owner
- User B: attacker account
- Log all IDs belonging to User A while authenticated as User A
4. **Replay User A's resource IDs as User B**
- Replace session cookie/token with User B's credentials
- Send identical requests referencing User A's object IDs
- Test ALL HTTP verbs: GET, POST, PUT, PATCH, DELETE on each endpoint
5. **Test cross-tenant/cross-org scenarios**
- Create accounts in separate organizations/businesses
- Test if Org B's session can reference Org A's IDs
- Pay special attention to admin/management endpoints
6. **Test GraphQL specifically**
- Run introspection: `{ __schema { queryType { fields { name } } } }`
- For every query/mutation taking an `id` argument, substitute another user's ID
- Test both queries (read) and mutations (write/delete)
7. **Test write/destructive operations, not just reads**
- Can User B DELETE User A's resources?
- Can User B MODIFY User A's content?
- Can User B ADD themselves to User A's account?
8. **Chain IDORs together**
- Use one IDOR's leaked data (org IDs, user IDs) to fuel the next
- IDOR → leaked ID → second IDOR → privilege escalation
9. **Test state-changing edge cases**
- Expired tokens/invites that can still be accepted
- Race conditions on resource IDs
- Indirect references: `?sort=id` or `?filter[user_id]=`
10. **Document the exact differential**
- Confirm User B has NO legitimate access to User A's resource
- Screenshot/log the 200 OK vs expected 403/404
---
## Payload & Detection Patterns
**Basic IDOR test with curl (swap cookie/token):**
```bash
# Get User A's resource ID while authenticated as A
curl -s -H "Cookie: session=USER_A_SESSION" \
https://target.com/api/v1/invoices/12345
# Replay with User B's session
curl -s -H "Cookie: session=USER_B_SESSION" \
https://target.com/api/v1/invoices/12345
# Success = 200 OK with User A's data
```
**GraphQL IDOR test:**
```bash
curl -s -X POST https://target.com/graphql \
-H "Authorization: Bearer USER_B_TOKEN" \
-H "Content-Type: application/json" \
-d '{"query":"{ billingDocument(id: \"USER_A_DOC_ID\") { id amount pdfUrl } }"}'
```
**Enumerate sequential IDs with ffuf:**
```bash
ffuf -u "https://target.com/api/v1/orders/FUZZ" \
-w ids.txt \
-H "Authorization: Bearer USER_B_TOKEN" \
-mc 200 \
-o idor_results.json
```
**Generate sequential ID wordlist:**
```python
# Generate IDs around a known value
known_id = 48291
with open("ids.txt", "w") as f:
for i in range(known_id - 500, known_id + 500):
f.write(str(i) + "\n")
```
**Burp Intruder payload for IDOR scanning:**
```
GET /api/messages/§12345§ HTTP/1.1
Host: target.com
Authorization: Bearer USER_B_TOKEN
# Mark §12345§ as injection point
# Use numeric sequential payload: 12000-13000
# Filter responses by length difference or status 200
```
**JavaScript scraping for leaked IDs:**
```bash
# Find IDs in JS bundles
curl -s https://target.com/static/app.js | grep -Eo '"id":"[a-f0-9-]{36}"' | sort -u
# Find object references in API responses
curl -s -H "Cookie: sessionEnd-to-end Android APK red-team pipeline — automated APK acquisition (Play Store + apkpure + apkmirror fallback), jadx decompilation, secret/URL/JWT/Firebase grep, pinned-cert extraction, exported-component enumeration, Frida runtime instrumentation templates, intent-injection probes. Built from an authorized external red-team engagement where 7 APKs were pulled manually, 4 download attempts truncated, and a hardcoded JWT + 30 internal API endpoints were recovered from one of the apps. Use when target has a mobile app catalogue (Play Store developer page), when you find an APK URL hosted on a web server, or when post-recon mentions "mobile app" in scope.
Run autonomous hunt loop on a target — scope check → recon → rank surface → hunt → validate → report with configurable checkpoints. Usage: /autopilot target.com [--paranoid|--normal|--yolo]
Local-tooling companion to the bug-bounty orchestrator — carries the SAME complete bug-bounty workflow, but reach for THIS variant when you also need to resolve where tools, wordlists, and clones are installed on the local machine (jhaddix, SecLists, trufflehog, ffuf, dalfox, ghauri); for pure orchestration/routing use the bug-bounty skill. Workflow it covers — 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 when you need the local install path of a tool / wordlist / clone for a hunt, or as the full-workflow variant when operating from this local toolkit; for general routing use the bug-bounty skill. 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
Use at the START of any bug bounty hunting session, when switching targets, or when feeling lost about what to do next. Master orchestrator that combines the 5-phase non-linear hunting workflow with the critical thinking framework (developer psychology, anomaly detection, What-If experiments). Routes to all other skills based on current hunting phase. Also use when asking "what should I do next" or "where am I in the process.
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
Bugcrowd-specific reporting tactics complementing report-writing: VRT category search-and-fallback strategy when no exact match exists, manual severity override when VRT defaults underrate impact, severity-request paragraph as first body section, OOS-clause rebuttal templates (rate limiting on auth-flow endpoints, debug-info framing, user-enumeration with sensitive PII, theoretical-issue counter), chained-finding cross-reference patterns, target selection for QA-vs-prod programs, researcher-side hygiene (Bugcrowdninja email alias, account state restoration, friendly-tester posture). Use when filing a Bugcrowd submission, when VRT default seems wrong, when triager closes as OOS or downgrades severity, when chaining linked submissions, or when scope distinguishes production from QA. Pairs with report-writing and triage-validation.
Build an exploit chain — given bug A, finds B and C to combine for higher severity and payout. Knows common chain patterns: IDOR→ATO, SSRF→cloud metadata, XSS→ATO, open redirect→OAuth theft, S3→bundle→secret→OAuth. Usage: /chain
Cloud IAM red-team attack chain across AWS, Azure, GCP — focused on EXTERNAL exploitation paths and post-credential-discovery privilege analysis. Covers IAM enumeration (aws iam, az role, gcloud iam), STS/AssumeRole chaining, Azure Managed Identity abuse (via SSRF/leak), GCP service account JSON abuse, IMDSv1/v2 attacks via SSRF, K8s ServiceAccount token privilege analysis once held (token discovery / cluster exposure is owned by hunt-k8s), role-trust-policy confused-deputy, cross-account assume-role enumeration, IAM privilege escalation patterns (24+ AWS, 8+ Azure, 6+ GCP), and AWS Cognito Identity Pool unauthenticated-role attack chain (GetId → GetCredentialsForIdentity → IAM role abuse). Built for the case where recon yields a credential (key, JSON, token) and you need to know what it grants and how to escalate. Use when an AWS key / Azure secret / GCP service account JSON / K8s SA token surfaces from a code repo, JS bundle, APK, breach corpus, or SSRF chain.