Find the bugs that come from your application's own rules: IDOR, broken auth flows, race conditions, mass-assignment escalation. The kind static rules can't predict.
Traditional SAST tests for patterns. We test for behavior. An LLM reads your codebase end-to-end, derives your application's security-critical invariants, then walks the code looking for where they break.
Six categories of business logic flaws that signature-based tools cannot detect, because each one depends on context only your codebase carries.
Invalid state transitions or missing state checks.
Flawed permission models, privilege escalation paths.
Timing-dependent logic errors.
Business rule bypasses through unexpected input sequences.
Out-of-order operations the application permits but shouldn't.
Broken assumptions about data consistency.
From invariant discovery to working exploit, every finding is confirmed and reproducible. Runs as the final phase of the SAST pipeline, with results merged into the same SARIF stream.
Reads your code end-to-end and defines business invariants: security-critical rules unique to your application, such as multi-tenant data isolation, checkout totals that must never go negative, role-based access boundaries, rate-limiting enforcement, and state machine transitions that shouldn't be skippable.
Systematically walks the codebase looking for places where each invariant breaks. Missing authorization checks on admin endpoints, tenant ID filters that can be bypassed via parameter tampering, payment flows where amount validation happens client-side only.
Each candidate violation is confirmed against the actual code to rule out false positives.
Every confirmed finding gets a full proof-of-concept exploit with step-by-step reproduction instructions: the exact HTTP requests, parameter values, and sequencing needed to demonstrate the vulnerability in a running environment.
Because Business Logic Testing runs inside the SAST pipeline and emits findings in standard SARIF, results merge with other SAST findings and flow through the canonical findings deduplication system.
Business invariants are not generic patterns. They describe what your application is supposed to do, the rules that hold for your data, your tenants, your workflows. These are context-specific rules that generic scanners miss.
A banking app requires strict multi-tenant isolation. Account balances, transaction logs, and user records must never leak across tenants.
A social media app intentionally exposes certain information platform-wide, but has invariants about what should be private to a user, visible only to followers, or fully public.
Same vulnerability class, opposite correct behavior. Pattern-based scanners cannot tell the difference. Keygraph learns yours.
Schedule a demo and watch Business Logic Testing run against your staging environment.