Authentication Bypass in Data Warehouse API

While reviewing a large-scale Data Pipeline platform responsible for processing events and exporting them to cloud data warehouses, I came across a surprisingly dangerous behavior in the way requests were reaching internal services.

At first, I assumed there had to be some hidden security layer protecting the backend. After all, this was infrastructure handling sensitive customer data. However, the deeper I investigated, the more obvious it became that the issue was much simpler - and much more severe - than expected.

💰 Bug Bounty Reward: $1,500

---

Understanding the Architecture​

The platform followed a common microservices design:

• A public Gateway receives external traffic.
• A Reverse Proxy forwards requests internally.
• A Warehouse Master service handles warehouse operations.
• Authentication middleware is supposed to validate requests before they reach internal systems.

In theory, the Gateway should act as the primary security boundary between the internet and the internal infrastructure.

---

The First Red Flag​

While reviewing the Gateway source code, I found a direct reverse proxy configuration:

whURL, _ := url.ParseRequestURI(misc.GetWarehouseURL())
gw.whProxy = httputil.NewSingleHostReverseProxy(whURL)

Nothing unusual there.
The real problem appeared when I inspected how routes were registered.

---

Critical API Endpoints Without Authentication​

Several warehouse-related endpoints were exposed directly through the proxy:

r.Post("/pending-events", gw.whProxy.ServeHTTP)
r.Post("/trigger-upload", gw.whProxy.ServeHTTP)
r.Post("/jobs",           gw.whProxy.ServeHTTP)
r.Get("/fetch-tables",    gw.whProxy.ServeHTTP)
r.Get("/jobs/status",     gw.whProxy.ServeHTTP)

At first glance, these routes looked normal.
However, none of them had any security middleware attached.

There was no:

• Authentication
• Authorization
• API Key Validation
• Ownership Verification
• Access Control
• Rate Limiting

Every request reaching these endpoints was immediately forwarded to the internal Warehouse Master service.
🚨 This effectively bypassed the entire security model.

---

Was the Internal Service Protected?​

To answer that question, I moved on to the Warehouse Master codebase.
The middleware configuration looked like this:

srvMux.Use(
    chiware.StatMiddleware(ctx, a.statsFactory, "warehouse"),
)

That's it.
The service only applied a statistics middleware used for monitoring and metrics collection.
There was no authentication layer, no authorization checks, and no request validation.
The internal service completely trusted that any request reaching it had already been verified by the Gateway.
That assumption turned out to be the root cause of the vulnerability.

---

Why This Was So Dangerous​

The entire security architecture relied on a single belief:

"Only the Gateway can talk to the Warehouse Master."

Unfortunately, the Gateway was forwarding requests without performing any meaningful validation.

As a result, an attacker could interact directly with sensitive backend functionality without providing:

• Access Tokens
• API Keys
• Authorization Headers
• User Credentials
• Session Information

---

Triggering Unauthorized Warehouse Uploads​

One of the exposed endpoints allowed warehouse uploads to be triggered manually.
Example request:

curl -i -X POST 'http://localhost:8088/v1/warehouse/trigger-upload' \
  -d '{"source_id":"xxxyyyzz...WYZLUyk"}'

Response:

HTTP/1.1 200 OK

The only validation performed was whether the provided source ID resolved to an existing warehouse.

Internally, the code simply scheduled upload jobs:

for _, warehouse := range wh {
    a.triggerStore.Store(warehouse.Identifier, struct{}{})
}

No authentication. No ownership verification.

Potential Impact​

⚠️ Force expensive warehouse uploads.
⚠️ Trigger unnecessary cloud compute operations.
⚠️ Increase customer billing costs.
⚠️ Exhaust provider quotas and rate limits.
⚠️ Disrupt legitimate data synchronization jobs.

This becomes especially costly when customers use services such as:

• Snowflake
• BigQuery
• Redshift

where billing is heavily tied to query execution and compute usage.

---

The Most Dangerous Endpoint: Data Deletion​

The most severe issue involved the jobs endpoint.
An attacker could submit the following request:

curl -i -X POST 'http://localhost:8088/v1/warehouse/jobs' \
  -d '{
    "source_id":"...",
    "destination_id":"...",
    "job_run_id":"victim-job-run-id",
    "task_run_id":"victim-task-run-id",
    "async_job_type":"deletebyjobrunid"
  }'

Internally, the request was processed as follows:

jobType, _ := model.FromSourceJobType(payload.JobType)

tableUploads, _ := m.tableUploadsRepo.GetByJobRunTaskRun(
    ctx,
    payload.SourceID,
    payload.DestinationID,
    payload.JobRunID,
    payload.TaskRunID,
)

Once accepted, the Warehouse Processor executed deletion operations against records associated with the specified Job Run.
🚨 This resulted in real customer data being deleted from cloud warehouses.

---

Why Exploitation Was Easier Than Expected​

To perform the attack, an attacker only needed four values:

• source_id
• destination_id
• job_run_id
• task_run_id

The problem?
These identifiers were not actually secret.

They could often be discovered through:

• CI/CD logs
• Error messages
• Monitoring dashboards
• Debug output
• Internal documentation
• Shared log files

A single leaked log entry could provide enough information to perform destructive operations against customer data.

---

Information Disclosure Through Warehouse Enumeration​

The exposure didn't stop with data deletion.
Additional endpoints allowed attackers to gather intelligence about customer environments.

Fetch Tables Endpoint​

The fetch-tables endpoint revealed:

• Database table names
• Column names
• Schema structures
• Warehouse metadata

This information can significantly simplify targeted attacks against sensitive datasets.

Pending Events Endpoint​

The pending-events endpoint exposed:

• Pipeline status information
• Processing activity
• Event backlogs
• Source-specific operational details

An attacker could monitor internal workflows and time attacks against active data processing operations.

---

Security Impact​

The vulnerability introduced multiple high-severity risks:

🔴 Authentication Bypass​

🔴 Unauthorized Data Deletion​

🔴 Cross-Tenant Exposure​

🔴 Cloud Cost Abuse​

🔴 Information Disclosure​

🔴 Warehouse Enumeration​

🔴 Lack of Accountability​

🔴 Complete Trust Boundary Failure​

In some scenarios, this could lead to customer data loss, financial impact, and exposure of sensitive business information.

---

Lessons Learned​

This vulnerability highlights a common security mistake in modern distributed systems.
Never assume internal services are automatically trusted.
Every sensitive service should enforce its own security controls, including:

✅ Authentication​

✅ Authorization​

✅ Ownership Validation​

✅ Audit Logging​

✅ Rate Limiting​

✅ Zero Trust Principles​

Security boundaries should be enforced at multiple layers, not delegated entirely to a single Gateway component.

---

Final Thoughts​

What made this vulnerability particularly interesting was its simplicity. There was no advanced exploit chain, no sophisticated bypass technique, and no complex race condition.

Instead, the issue originated from a dangerous architectural assumption: the Gateway was expected to protect internal services, while the internal services blindly trusted anything arriving from the Gateway.

Because that trust relationship was never properly enforced, attackers could access sensitive warehouse functionality, delete customer data, trigger costly operations, enumerate schemas, and gather intelligence about internal pipelines - all without authentication.

This serves as a powerful reminder that trust boundaries should never be assumed, especially when protecting critical infrastructure and customer data. 🔥