A red team that only reports what it proved
A pentest engagement attacks one of your repositories in an isolated sandbox and reports only what it proved. Every pentest finding arrives with a working proof of concept and the evidence captured while it ran, so nobody spends a morning disproving a guess.
Mapped from the code,
proven in a sandbox
An engagement maps your attack surface from the code and the graph, fans out one agent per attack class, then validates the promising leads by building a non-destructive proof in a sandbox. A candidate that cannot be exploited is dropped. You can watch it move through Enumerating, Attacking, Validating and Reporting.
4
23
$3.14
How it works
Twelve attack classes,
chosen per repository
From SQL and NoSQL injection and command injection through broken access control, server-side request forgery and cross-site scripting to authentication weaknesses. All twelve are on by default. A severity floor, from Critical down to Info, decides how much of what is proven gets reported.
A sandbox that
reaches nothing
Every engagement runs in a sandbox created for that run and destroyed after it, holding a clone of the repository and nothing else of yours. Each command is screened before it runs: requests to outside hosts, raw sockets, disk-destructive commands, anything piped into a shell and git push are refused.
Your own prompts,
attacked too
AI red-team is a separate switch. It finds the LLM prompts in the repository and attacks those instead, covering prompt injection, jailbreaks, prompt leaking and tool abuse, with a live transcript while it runs.
Depth, schedule
and a cost ceiling
Light leans on static analysis. Tiered works in stages from reconnaissance to validation and is the usual choice. Deep fans out wider and validates everything it finds. Run on demand, daily, weekly or monthly, with a maximum cost per run so a long engagement stops instead of continuing.
Included
Related
Security
Advisories assessed for reachability in your code, malware alerts within minutes, remediation pull requests and an AI bill of materials per repository.
Read moreAI bill of materials
Every AI SDK, model identifier, MCP server and prompt in each repository, with the file and line, rebuilt on each scan and exported as CycloneDX.
Read moreSandbox tests
Dependencies installed, the suite run, tests written for uncovered code, and the terminal trace kept, in a sandbox built for each pull request.
Read more
Put it on one repository
Connect GitHub or GitLab, enable a repo, and the next pull request opened gets a review.