Offensive Business Logic Testing Methodology
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About this skill
Problem Solved
Business logic flaws hide between the developer's intended specification and what the system actually accepts. Scanners mostly match known patterns, so they miss assumptions about who can call a transition, from which state, with what inputs, and how many times. This skill provides an offensive testing method for breaking multi-step business flows into verifiable states and transitions, then finding flaws that chain into high-impact abuse.
How It Works
The core workflow is:
- Model each multi-step flow as a state machine, listing states, allowed transitions, and side effects
- For every transition, ask about caller identity, current state, input constraints, and call frequency
- Probe assumptions along the state, identity, input, and frequency axes
- Combine single-axis flaws into chains, since isolated issues are often low severity while chained abuse is critical
- Quantify financial impact per finding, for example loss-per-attack × scale
Boundaries: this is not an automated vulnerability scanner and does not replace code review or authorization-model analysis. It fits applications with payments, approvals, inventory, accounts, multi-step APIs, or other state-dependent flows. Use it when you can access the target endpoints, understand the business process, and prepare reproduction steps and impact evidence.
Use Cases
- Audit payment or approval APIs by modeling multi-step flows as state machines and finding privilege or replay flaws
- Review state-dependent flows such as account upgrades and inventory deduction to list identity, input, and frequency assumptions
- During bug bounty testing, chain single logic flaws and quantify impact as loss per attack multiplied by scale
- Write penetration test reports organized around state, identity, input, and frequency reproduction evidence
Best For
- Web/API security engineers who need to decompose business flows into state machines and locate high-risk chains
- Bug bounty hunters who need to combine flaws in payment, approval, or account flows into quantifiable impact
- Application security engineers who need to audit identity, input, and frequency assumptions with reproducible evidence
- Penetration test leads who need to organize single logic flaws into impact chains in reports
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