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Introduction:
In the competitive arena of bug bounty hunting, achieving a “triaged” status for a submitted vulnerability is the critical gate between a mere finding and a paid reward. This process, managed by platform security teams, validates the exploit’s legitimacy, severity, and impact. Focusing on Insecure Direct Object Reference (IDOR) vulnerabilities—a common yet high-impact flaw—this guide provides a structured methodology for hunters to consistently discover, exploit, and document these bugs to meet triage criteria.
Learning Objectives:
- Understand the core mechanics of IDOR vulnerabilities and their modern variations in APIs and stateful applications.
- Develop a systematic methodology for testing object references across different authorization contexts.
- Learn to automate discovery processes and craft undeniable proof-of-concept (PoC) demonstrations for triage teams.
You Should Know:
1. Decoding IDOR: Beyond Changing a Simple ID
IDOR occurs when an application provides direct access to objects based on user-supplied input without adequate authorization checks. The classic example is changing a URL parameter like `?user_id=1001` to ?user_id=1002. However, modern applications often use UUIDs, hashes, or complex tokens.
Step‑by‑step guide:
Step 1: Map all object references. Use a proxy tool like Burp Suite or OWASP ZAP to intercept all requests. Catalog every parameter that seems to reference a user, order, document, or transaction (e.g., id, uid, document_key, transaction).
Step 2: Understand the reference format. Is it a sequential number, a UUID (123e4567-e89b-12d3-a456-426614174000), or a base64-encoded string? Decode or analyze them.
Example: Decoding a suspected base64 ID in Linux terminal echo "MTIzNDU2Nzg5MA==" | base64 -d Output may reveal a numeric or string ID.
Step 3: Test in a different context. If you have two test accounts (Account A and Account B), perform an action in A (e.g., load your profile), capture the request, and replay it with your AUTH token from Account B. Does Account B see Account A’s data?
- The Authorization Matrix: Testing Stateful and Stateless Contexts
True IDOR flaws are about authorization, not just parameter tampering. You must test the same reference across different user roles and states.
Step‑by‑step guide:
Step 1: Establish testing matrix. Create accounts for all relevant roles (User, Moderator, Admin) and states (e.g., User X’s documents vs. User Y’s documents).
Step 2: Methodically substitute tokens. Using Burp Suite’s “Repeater” tool, systematically swap session cookies, JWT tokens, and API keys while keeping the object reference constant. Observe if access is incorrectly granted.
Step 3: Check for referer/header-based authorization. Sometimes, checks rely on HTTP headers. Tamper with the `Referer` header or custom headers like X-User-Id.
Example curl command testing with a modified referer header curl -H "Authorization: Bearer <JWT_TOKEN_ACCOUNT_B>" -H "Referer: https://target.com/app/user/1001" https://target.com/api/v1/documents/5001
3. Automating the Discovery with Fuzzing
Manual testing is foundational, but automation scales your efforts. Use targeted fuzzing to find numeric IDORs.
Step‑by‑step guide:
Step 1: Prepare a wordlist. Create or use a list of common ID parameters and potential numeric ranges.
Generate a simple numeric sequence in Linux seq 1000 1050 > num_ids.txt Common ID parameter names wordlist snippet echo -e "id\nuuid\nkey\ndoc\nfile\naccount" > param_names.txt
Step 2: Use ffuf for fuzzing. The tool `ffuf` is excellent for this. Fuzz both parameter names and values.
Fuzz for parameter names ffuf -w param_names.txt -u "https://target.com/api/FUZZ=1001" -H "Authorization: Bearer <VALID_TOKEN>" -fs 0 Fuzz for valid object IDs (brute-force) ffuf -w num_ids.txt -u "https://target.com/api/document?id=FUZZ" -H "Authorization: Bearer <VALID_TOKEN>" -fr "not found"
4. Exploiting Mass Assignment and Batch Operations
Modern APIs often have batch endpoints (e.g., /api/batch/update) that accept arrays of object references. This can escalate a single IDOR into a mass data breach.
Step‑by‑step guide:
Step 1: Find batch endpoints. Look for API routes with terms like batch, bulk, multiple, or endpoints that accept JSON arrays.
Step 2: Craft a malicious batch request. If you can retrieve your own documents with IDs [101, 102], try replacing them with [101, 102, 200, 201].
{
"operation": "get",
"document_ids": [101, 102, 200, 201]
}
Step 3: Analyze the response. Does the API return data for all IDs without checking ownership for each one?
5. Crafting the Irrefutable Proof-of-Concept (PoC) for Triage
A triage team needs clear, concise, and undeniable evidence. Your report must eliminate all ambiguity.
Step‑by‑step guide:
Step 1: Document the flow with evidence. Use screenshots and video. A simple 4-panel image is effective: 1) Logged in as User A, 2) Captured request with User A’s data, 3) Replayed request as User B (show token swap), 4) Received User A’s data in User B’s session.
Step 2: Provide a reproducible script. Include a standalone Python or cURL script that replicates the bug with dummy credentials and target IDs.
import requests
PoC for IDOR in /api/document/{id}
USER_B_TOKEN = "eyJhbGci..."
url = "https://target.com/api/document/5001" Document belonging to User A
headers = {"Authorization": f"Bearer {USER_B_TOKEN}"}
resp = requests.get(url, headers=headers)
if resp.status_code == 200 and "sensitive_data_field" in resp.text:
print("[+] IDOR VULNERABILITY CONFIRMED.")
print(f"[+] User B accessed document: {resp.json()}")
Step 3: Clearly articulate impact. State precisely: “This allows any authenticated user to view/change/delete the [sensitive data] of any other user, leading to a full compromise of data confidentiality/integrity.”
What Undercode Say:
- Methodology Over Luck: Consistent success in bug bounty programs stems from a rigorous, repeatable testing methodology, not just ad-hoc parameter tampering. Building an authorization testing matrix is non-negotiable.
- The Proof is in the PoC: The difference between a “Not Applicable” and a “Triaged” verdict often lies in the clarity and reproducibility of your proof-of-concept. Invest time in making your evidence foolproof for an analyst who has limited context.
Analysis: The original post highlights a universal goal for hunters: getting triaged. The comment speculating “IDOR?” underscores how common this vulnerability is. The journey from finding a potential issue to having it validated requires bridging the gap between a hunter’s understanding and the triage team’s need for unambiguous, impactful evidence. By approaching IDOR hunting with the systematic rigor of a security engineer—documenting processes, automating discovery, and crafting military-grade PoCs—hunters significantly increase their triage rate. This transforms sporadic success into a sustainable practice.
Prediction:
The future of bug bounty triage will increasingly leverage AI-assisted triage bots that perform initial validation of submissions, checking for exploit reproducibility and basic impact. Hunters who structure their reports with machine-readable evidence (clean code, well-labeled data) will see faster triage times and potentially higher rewards. Platforms will evolve towards hybrid AI-human systems where only complex, novel, or high-severity bugs escalate to human analysts, making precision in reporting and technical communication the hunter’s most valuable skill.
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IT/Security Reporter URL:
Reported By: Shari7a0x %D8%A7%D9%84%D8%AD%D9%85%D8%AF – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
