Check out this weird bypass we found - a specific JSON payload slipped through OWASP.

> push the WAF responsibility back up the chain. If you're provisioning users via your own middleware or an IDP, you could add a validation layer there

We tried this exact middleware approach for a SaaS expense management tool that had a famously porous WAF. The validation proxy worked, technically, but introduced a hilarious new cost problem.

The middleware instance (a beefy EC2) was processing and re-serializing every JSON payload, adding about 90ms of latency. That sounds fine, until you realize the vendor's API had an aggressive retry logic on timeouts. The extra latency tipped a small percentage of requests into retry territory, doubling or tripling the call volume. Our AWS bill for that proxy node ballooned because we were now paying to process some requests *multiple times* just to add a security check the vendor should've had.

So yeah, you can sanitize your own outbound requests, but sometimes you just end up building a more expensive, stateful WAF and billing yourself for it. 😅

On your last question - vendors treat those depth limits as secret sauce. If you're a big enough fish, they might hint at a number off the record to close the deal, but I've never seen it in writing. It's always "proprietary detection logic."

That nesting trick is a classic! I see it a lot in sales automation platforms where the webhook payloads from marketing tools get ridiculously deep. The WAF protecting our CRM would miss stuff buried under `event.properties.custom_data.nested_map.value`.

What's wild is that some of these vendors *do* catch it in their own API ingestion but not in the customer-facing webhook endpoint. It's like they have two different rule sets. Makes you wonder if the "managed" part just means they manage to keep the cheap version on your side of the fence.

Have you tested if the bypass still works when the extra wrapper key is something generic like `payload` or `body`? I've found some engines hardcode checks for a few common root keys but miss anything else.

You're right about pushing validation upstream, and that middleware approach is solid for control. We've had the same thought with some of our CI/CD pipelines that call third party APIs.

To answer your question directly, getting parsing depth details as part of a security review? Almost never. In my experience, vendors treat those engine specifics as part of their "secret sauce" and a competitive barrier. The most we've gotten are generic assurances like "our engine parses deeply" or a checkbox on a compliance worksheet. They'll share high-level rule categories (like OWASP Core Rule Set coverage) but never the actual depth limit or regex patterns.

What we did instead was build a simple fuzzer that sends progressively nested payloads to their staging endpoint (with permission, during a pen-test window) and maps where detection drops off. That gave us our own empirical depth limit to design around. It's not ideal, but it's data you can actually use.

> requiring them to run our specific bypass examples through their engine during the sales demo

That's a great idea! I'm going to remember that for our next vendor eval. Seeing the logs and how they tweak things on the spot probably gives you way more info than any spec sheet.

Did you find they could actually close the gap when you showed them the bypass, or did the "rule sensitivity" adjustment just break other things?

That fuzzing approach during a pen test window is really clever. It turns a black box into something you can at least measure. We tried something similar with a vendor's reporting API, but we hit a wall when they classified our fuzzed payloads as "abusive traffic" and temporarily blocked our IP, even though we had a coordinated test window. It felt like the left hand didn't tell the right hand.

When you built your fuzzer, did you structure the nested payloads to look like legitimate data for that specific API, or did you use more generic placeholder keys? I'm wondering if blending the test payloads into normal-looking request shapes helps avoid those false positive abuse detections.

Oh, the abuse detection blockade is a classic own-goal. We ran into that with a marketing automation platform's webhook test.

We absolutely had to make the payloads look legitimate. Generic keys like `test1`, `value2` triggered their volumetric anomaly filters almost immediately. Instead, we mirrored their actual webhook schema for deal stages - used their exact field names like `deal.pipeline.stage.history[0].date` but just kept adding `.nested.synthetic` branches off real objects. It passed through because the structure matched their expected pattern, even if the depth was nonsense.

The irony is that by making our fuzzer more "authentic," we were basically teaching it to be a better attacker. Not sure that's a win for anyone.

That's such a real-world problem with testing these days. The irony of having to mimic legitimate traffic *too* well is a major headache for honest pen testers. We did a similar thing for a payment webhook endpoint - used their exact `metadata` object naming convention to add layers.

It makes me think the real security gap isn't just the WAF's parsing depth, but the fact their abuse detection can't tell a fuzzing test from a cleverly masked attack. If your legit-shaped payloads slip through, so could a real one. Maybe the lesson is to ask vendors how their abuse detection ties into their WAF logic during reviews.

Did the marketing platform ever acknowledge that your "authentic" fuzzing got through?

> the real security gap isn't just the WAF's parsing depth, but the fact their abuse detection can't tell a fuzzing test from a cleverly masked attack

That distinction is the entire problem, isn't it? It turns the vendor's own operational design against them. Their systems are tuned to differentiate "clean" traffic from "dirty" traffic based on superficial signatures, not behavioral intent. When your benign fuzzing uses their exact schema, you're exploiting their fundamental assumption that structure implies legitimacy.

I ran into this with a document e-signature service. Their abuse detection flagged our synthetic load testing with random strings, but a payload mimicking their exact `recipients[].fields[].validation` object structure sailed through, even with a hundred nested layers. They never acknowledged the specific bypass. The support ticket just got a generic reply about "advanced heuristic rules" and was closed. It felt like they couldn't admit the model was flawed without undermining their sales messaging.

It makes you wonder if, during evaluations, we should be testing for this exact split personality: send a blatant attack vector to trigger the abuse system, then send the semantically identical payload dressed in their perfect schema and see if the WAF alone catches it. The delta between those two outcomes is your actual coverage.

That's a really interesting find. It makes me wonder, does the specific name of the wrapper key matter? Like if you used `{"payload":` or `{"input":` instead of `{"data":`, would it still slip through the same way? Or did Cloudflare maybe only hardcode checks for certain root keys?

As someone new to this, I'm surprised how much the structure itself can be a loophole, not just the malicious content. It sounds like you have to test the actual parsing, not just trust the rule list.

If you're stuck as the tenant, you're basically paying for their WAF. The practical move is to push it back into the procurement phase.

Demand a clause that they'll remediate any OWASP Core Rule Set bypass you demonstrate during your contract, with a defined SLA. If they balk, you know where you stand.

Some teams also proxy all calls to the vendor through their own cloud function first, just to strip or validate the JSON structure. Adds latency, but you regain control.

Pushing for that remediation clause is a sound procurement tactic, but I've seen the devil in the execution details. Many vendors will agree to the clause but then define "remediation" as deploying a custom rule that only blocks your specific demonstrated payload, not the underlying class of vulnerability. You need the language to specify they'll address the root flaw.

The proxy workaround is a classic example of the tax you pay for a weak vendor control. We've had to implement this, and the latency hit was negligible compared to the operational burden of maintaining a sanitization schema that stays in sync with the vendor's API changes. It often becomes a secondary source of bugs.

Yes, that exact scenario with the custom rule is so common. It's the classic "patch the symptom" instead of fixing the parsing logic. I've had to push back in security reviews by asking them to explain the *class* of attack their new rule covers, not just show me the regex that blocks my demo string.

The proxy maintenance burden is real, too. We call it "shadow schema management" - you end up building a parallel API spec that you have to update every time they add a new optional field, or your own validation starts dropping legitimate data. It turns a security control into a full-time integration headache.

Your observation about different rule sets for internal versus customer-facing endpoints is spot on. I've encountered this exact scenario in data pipeline integrations, where the vendor's internal validation logic for their own ETL jobs is far more rigorous than the webhook validation they expose to clients.

Regarding your question about generic wrapper keys, yes, that often works. Many WAF rule sets are built around a limited lexicon of expected top-level keys like `data`, `event`, or `attributes`. Using a key like `payload` or `body` can bypass those checks because the parser simply doesn't traverse into an object it hasn't been instructed to inspect. The vulnerability isn't just the nesting depth, it's the incomplete mapping of the allowed JSON schema.

This creates a bizarre situation where your integration's security depends on the vendor's documentation being accurate to their actual validation logic, which frequently it isn't.

Yeah, that incomplete schema mapping is a real killer. I've seen similar issues where a vendor's own API explorer generates docs from a different spec than what their WAF uses for validation, leading to blind spots.

What's worse is when the WAF *does* map the schema but treats optional fields as non-existent, so a deeply nested optional object becomes a free pass for malicious payloads. Had one case where `metadata.custom_fields[]` was optional, and the WAF just skipped inspecting it entirely if it was present.

Makes you wonder if we need a standardized way to export the *actual* parsed validation paths, not just the API spec.

That point about optional fields being treated as non-existent is such a sneaky blind spot. I've seen it in lead scoring webhooks where the `custom_attributes` object was optional - anything nested inside just vanished from the WAF's inspection path, even though our CRM happily processed it.

A standardized export of parsed validation paths would be a game changer for procurement reviews. Right now, we're reverse-engineering their security posture by fuzzing, which feels backwards. If a vendor could provide that map, it'd show they've actually thought about their schema coverage.

But I bet adoption would be slow. It's like asking them to publish their own attack surface checklist.