OpenHack

Engineering teams typically run security scans with high false-positive rates and only pentest once a year or once a quarter, leaving vulnerabilities undiscovered for long stretches. Security findings often lack business context, so teams struggle to know whether a flagged issue is an exploitable, revenue-impacting risk or a low-priority non-issue, like a leaked test credential that is not reachable in production. Logic-based vulnerabilities such as broken access control, race conditions, timing attacks, and IDORs are frequently missed by traditional scanning tools because they require reasoning across multiple findings rather than isolated checks. The page states this costs teams 100+ hours that could be saved through better false-positive handling, and leaves critical bugs like authentication bypasses live in production.

OpenHack positions itself as an AI security engineer that scans codebases, runs AI-driven pentests, and inspects dependencies and secrets, then verifies each finding by building a working proof of concept and reproducing it in a sandbox or browser before reporting it. It prioritizes vulnerabilities using business context, such as whether an asset is internet-facing, part of a payment workflow, or in production, ranking issues like a broken access control bug as critical while deprioritizing unreachable leaked test credentials. The product integrates with team tools like Slack and Linear, letting engineers ask '@OpenHack what should we fix first' and get an assigned fix delivered back as a pull request. It also ships an open-source CLI that runs locally and lets users bring any AI model, alongside a managed platform that adds continuous scanning, team controls, and zero-data-retention inference, with code and findings stored locally and only inference requests leaving the user's environment or region.