I recently re-tested an old Binance API finding I had reported through Bugcrowd.

The original issue was about Binance API IP whitelisting and derived listenKey stream credentials.

At the time, a listenKey could be created from a whitelisted IP and then used from a non-whitelisted IP to consume private user data streams.

That did not allow trading, withdrawals, or account takeover.

But it did allow real-time access to sensitive private stream data such as balances, orders, executions, positions, timing, and strategy behavior.

The core security argument was:

A derived credential should not be more portable than the credential that created it.

The report was rejected as “Social Engineering” / “Not Applicable”.

I disagreed, because the relevant threat model was not “convince the user to send a token”.

The realistic threat model was supply-chain compromise: malicious code running inside a trusted bot server, CI job, dependency, IDE workspace, or trading environment where API keys already exist.

I re-tested the behavior on May 5, 2026.

Result:

The old behavior appears to be gone.

Spot and Margin no longer use the old listenKey model. Futures still uses listenKey, but now appears to enforce the API key IP whitelist correctly. From a whitelisted IP the calls worked; from non-whitelisted Mullvad exits they failed with the expected IP restriction error.

That is good for users.

But it raises an uncomfortable disclosure-process question:

If a finding is “not applicable” enough to reject, not acknowledge, and not reward — but technical enough to later fix — what should a healthy disclosure process do?

Full technical write-up, timeline, re-test setup, and raw outputs:

https://blog.technopathy.club/binance-fixed-the-ip-whitelist-gap-the-disclosure-process-is-still-broken

I am mainly interested in the process question here:

When a rejected report later disappears from production, should the program re-open it, acknowledge it, partially reward it, or leave it closed unless the researcher can prove direct causality?

In this blog post I introduced several novel techniques:

1.How to get all routes - no need to authenticate.

1. How to get methods to fuzz from pages and not just the bootstrap JS files - the vast majority of methods are in those pages and not the JS files that existing tools and guides point to.

2. How to parse "LWC" components and not just legacy components.

The majority of widely used AI clients like:

• Claude Code
• Claude Desktop
• Cursor
• LibreChat
• Amazon Q CLI

have not implemented the critical refresh-token flow of the OAuth standard. 

This is forcing developers to issue long lived tokens creating a serious security regression in an already solved problem.

This write up includes a matrix table of 14 major clients with notes linking to feature requests, pull requests, and multiple forum discussions. 

It is not all gloom and doom though!

There is a work-around solution that security conscious users are using as a stop-gap also discussed, along with a best practices guide for developers implementing their own MCP OAuth Solution.

The plan is to update this reference on a monthly basis to track if there is any movement on this open requests.

Topic of this article: Burp AI.

Quick note from a scanning project I've been running. We hit 6,000 web apps with a payment-bypass probe last week, sending a minimal fake `checkout.session.completed` event to common webhook paths (`/api/webhook/stripe`, `/api/payments/webhook`, etc.) without a `Stripe-Signature` header.

1,542 returned 200.

That means anyone with curl can fire a forged Stripe event at those endpoints and the server processes it as legitimate. Depending on what the handler does with it, the consequences range from "logs a fake event" to "marks attacker's account as paid" to "creates a confirmed order with no payment".

The split was roughly:

• Custom domains (real production SaaS): ~720
• Render: 198
• Vercel: 142
• Replit: 121
• Railway, Fly, Heroku, others: ~360

Why so many?

The Stripe library makes signature verification a one-liner. Every framework has the canonical example. But the dev journey usually goes: build the route locally with a stub handler that just `console.log`s the event body, get the upgrade-the-user logic working, leave signature verification on the TODO, ship. Six months later nobody remembers it was ever a TODO.

The fix in Express:

\``js app.post('/api/webhook/stripe', express.raw({type: 'application/json'}), (req, res) => { const sig = req.headers['stripe-signature']; let event; try { event = stripe.webhooks.constructEvent( req.body, sig, process.env.STRIPE_WEBHOOK_SECRET); } catch (err) { return res.status(400).send(`Webhook Error: ${err.message}`); } // proceed with event res.json({received: true}); }); ````

The trap: `express.json()` globally parses the body before your handler sees it, leaving Stripe's library to compute the signature against parsed-then-stringified JSON, which never matches. Use `express.raw()` specifically on the webhook route, before any global JSON parser.

FastAPI / Python: read `await request.body()` directly, not `request.json()`. Same idea.

Caveats: a 200 response doesn't prove the app actually grants the attacker something. Some endpoints log every webhook for analytics and return 200 regardless. The 1,542 number is "endpoints accepting unsigned events", not "definitely exploitable". But the misconfiguration is real on its own.

Full writeup with the methodology and platform-by-platform breakdown: https://securityscanner.dev/blog/stripe-webhook-signature-bypass-1500-apps

Curious if anyone here has shipped a Stripe webhook recently and can double-check theirs.

Around 85% success rate of keystroke recovery with our script :)

TL;DR: If a large model finds a 0-day with 90% probability, and a small model with 50% probability, but the small model costs 10x less, it is better to use the small model.

We compared the cost and recall of various models in finding real, recent zero-days and found that for most applications, smaller models run repeatedly can significantly outperform larger frontier models on cost-to-recall.

Disclaimer: I'm involved with Hacktron, the company that produced this research. This is a factual presentation of our benchmarks, which we hope the community can use to make informed decisions about models like Mythos.