Engineer builds deterministic 4-node AI pipeline to eliminate false positives in security testing
A application security engineer developed an autonomous penetration testing system called Okwute after standard LLM-based tools proved unreliable in production environments. The core problem with conversational AI tools was threefold: context loss between sessions, hallucinated vulnerabilities, and constant need for manual prompting between steps. To solve this, the engineer designed a four-node directed graph pipeline — Mapper, Generator, Executor, and Validator — running on a self-hosted, headless harness that stores all state and findings in structured filesystem artifacts rather than in-memory context. The system uses a three-tier memory architecture separating organisation-wide security baselines, product-family patterns, and per-session scratchpads, allowing knowledge to persist and scale across multiple targets. The goal is to reduce repetitive manual testing work for security engineers who are often responsible for dozens of microservices and APIs simultaneously.
