Methodology over Automation: Deconstructing E-commerce Checkout Logic

Introduction In application security, the most critical vulnerabilities often reside in business logic — flaws that automated scanners completely miss because they lack context. During a recent bug bounty engagement on a major e-commerce platform (target redacted), I completely bypassed automated recon tools. My goal was to map the application's state machine manually and identify where the application places trust in user-controlled input.

This article breaks down my methodology for mapping an e-commerce checkout flow to identify potential vectors for BOLA (Broken Object Level Authorization), Parameter Tampering, and Business Logic abuse.

Phase 1: Traffic Interception and State Mapping Using Burp Suite, I proxied all traffic while performing a standard user journey: browsing products, adding to cart, initiating checkout, and generating a payment prompt. My objective was to observe how state is passed between these discrete steps.

I categorized the attack surface into three distinct API groups:

1. Catalog/Navigation APIs: Endpoints handling page_id and product_id.
2. Cart APIs: Endpoints managing the cart_session.
3. Checkout/Payment APIs: Endpoints processing the final order total, applying offers, and initiating the payment gateway.

Phase 2: Identifying Trust Boundaries & Injection Points By analyzing the JSON payloads, I identified several critical parameters that warrant deep-dive testing. Finding these parameters isn't the vulnerability, the vulnerability is if the server fails to validate them.

• BOLA / IDOR Candidates: In the checkout API, I observed explicit user_id and address_id parameters being passed in the JSON body.
• The Test: Can I swap my user_id with another user's ID to view their cart or force an order onto their account? Does the server validate that the authenticated session token matches the user_id in the payload?
• Parameter Tampering & Price Manipulation: The payload to the payment gateway included a customer_amount key (e.g., "customer_amount": 3514).
• The Test: If this value is modified to 1 or a negative integer, does the server recalculate the cart total server-side, or does it blindly pass the user-supplied amount to the payment gateway?
• State Manipulation: I noted interesting boolean flags such as is_headless_enabled and enable_price_unbundling: false.
• The Test: Toggling these states to true. Can unbundling the price bypass minimum order thresholds or alter the discount logic?

Phase 3: Next Steps Manual mapping creates a roadmap. The next phase of this engagement involves systematically sending these requests to Burp Repeater and testing the server's response to manipulated logic.

Conclusion Building custom automation and scaling recon (like the Python VAPT toolkit I am currently developing) is highly effective, but it must be built on top of a solid understanding of application logic. You cannot automate the detection of a business logic flaw if you don't know how to find the trust boundaries manually.