Open_Ultra_SAST/docs/index.md

OpenUltraSAST

OpenUltraSAST is an experimental static security analyser meant to work as a safety net on repositories it has never seen: it scans a checkout, or the commits a git push would publish, and separates what it can prove from what it merely suspects. A scan that could not look at something records a degradation instead of reporting a clean result.

State as of 2026-10-02 (v2.0.0): the pre-push safety net is NO-GO for rollout. No hook capability is enabled, and no independent population has passed the qualification gates (see Evaluation).

Three layers

Layer	What it is	Where
Scanners	ousast scan (quick, standard, deep) and ousast pre-push: language-scoped pattern rules, a Joern code property graph engine, verification and scoring. Deterministic in quick; no model is needed for a scan.	Scanning, Architecture
The agentic plane on ax	Model-driven work over whole repositories, run as a DAG of tasks on google/ax, one isolated actor per task. What the runs learn is kept in a memory store. Maintainer surface: ousast plane.	The plane on ax, Memory
The decision engine	A learned classifier that weighs every instrument's signals against similar labelled cases from memory. In development, not adopted: no scan, pre-push check or report uses it. Maintainer surface: ousast learn.	Memory and detection, Decision engine

How they connect

flowchart LR
    repo["Repository or push delta"] --> scan["Scanners: quick rules, Joern engine, verify, score"]
    scan --> report["Report: findings with evidence, SARIF, score"]
    scan -- "alerts on vulnerable and fixed pins" --> plane["Plane on ax: repo-facts, verify a/b/c, agree, features"]
    plane -- "remember" --> mem[("Memory store: rows, facts, examples, blobs")]
    mem -- "improve --memory: rule-status proposals" --> gate{"Validator and detection gate"}
    gate -- "accepted round" --> ledger["Ruleset ledger: rule status"]
    ledger --> scan
    labels["Labels from ground truth"] --> mem
    mem -- "retrieval of similar labelled cases" --> engine["Decision engine: compiled AI classifier"]
    engine -. "intended, not adopted: BLOCK / ADVISORY" .-> report

• The scanners produce findings and, inside plane Runs, the alerts that become memory rows.
• The plane runs model work (verify passes, roles) and model-free bookkeeping (facts, agreement, feature records) per case; its remember task turns a case's artifacts into rows of the memory store.
• Memory feeds two consumers. ousast improve --memory turns stored rows into rule-status proposals that pass through the same validator and gate as every other edit. The decision engine retrieves labelled examples from the same store.
• The decision engine's output (dotted line) is the intended path. Nothing user-facing reads it today.

Where to go next

• Run a scan: Examples, and the commands in Scanning.
• Understand a finding's evidence and score: Architecture.
• The program analysis under a scan, what each representation can see and what it misses: Detection techniques.
• Operate the plane and its store: ax on this host and RustFS setup.
• Where the plane runs today and what a separate Kubernetes cluster would take (planned, not done): Deployment.
• Where the model spend goes, per Task at runtime and in the maintainers' own coding sessions, and what is reused instead of paid again: Token ergonomics.
• Trust boundaries and hardening: Threat model.

Every number in these pages is quoted from a committed record whose path is given next to it.