Open_Ultra_SAST/README.md

# OpenUltraSAST

**v1.2.0-alpha.1:** experimental pre-push safety net. Integration and census repairs are
verified; useful hook coverage, latency and independent capability qualification remain
**NO-GO**. No real hook capability is enabled. See [release notes](RELEASE_NOTES.md) and
[spec status](.kiro/specs/pre-push-safety-net/status.md).

OpenUltraSAST is a command-line security analysis harness built around one
goal: eliminating false positives by turning a suspicion into an
evidence-backed finding, and learning from the claims that were rejected. A
scan combines static rules, a semantic overlay, optional model-driven hunting
and Docker-isolated proof, and every claim carries the evidence that earned its
place.

The project began as an editor-agent skill and is now a CLI first. Agent
integrations remain, but nothing requires one: `ousast` is the interface, and
the model-driven parts are optional extras that degrade visibly when absent.

> **Maturity legend:** ✅ implemented and tested · 🟡 primitive exists, not yet
> auto-wired into the scan loop · 🧭 designed, on the roadmap. The current
> verified gate is `usable_harness_mvp`.

## Install and quickstart

```bash
uv sync --group dev

# Scan a local repository (quick mode = deterministic, no model calls)
uv run ousast scan /path/to/repo --mode quick

# Build an embedding-ready chunk index
uv run ousast index /path/to/repo

# Score the detector against a benchmark manifest
uv run ousast benchmark benchmarks/manifests/python-vulnerable.toml --mode quick

# Differential pair eval: fire on vuln, stay silent on the fix (local + real GitHub VFCs)
uv run ousast pairs
uv run ousast pairs --slice sast   # OWASP Benchmark Java/Python + Juliet
uv run ousast pairs --slice vibe-py --pointers   # nightly: also harvest non-vendored pointer pairs into ~/.cache
uv run ousast mechanisms export --slice vibe-py   # seed mechanism candidates from trusted pairs (offline); `ousast improve` admits them under the holdout gate
uv run ousast pairs --slice vibe-py --loo        # leave-one-out recall of the corpus as a teacher
uv run python -m openultrasast.pair_gate

# Run the bounded self-improvement loop over the ruleset (benchmark-driven)
uv run ousast improve benchmarks/manifests/python-vulnerable.toml --dry-run
```

Every run writes auditable artifacts under `.openultrasast/runs/<scan-id>/`
(`manifest.json`, `findings.json`, `verification.json`, `report.md`,
`report.sarif`, `trace/events.jsonl`, ranking/preprocess/mapping JSON).
`--fail-on findings|verified` gives CI-friendly exit codes.

## How a scan works

The CLI is thin; the `HarnessRuntime` owns the lifecycle and emits an event
trace for every stage (`task_start … stage_start/stage_end … task_end`).

```
repo ──▶ preprocess (language, LOC, tags, fuzz entry points)
     ──▶ static mapping (Semgrep/CodeQL SARIF ingest, normalized hints)
     ──▶ entry-point + reachability mapping (routes, CLI, parsers, contracts)
     ──▶ rank (surface·0.5 + influence·0.2 + reachability·0.3)
     ──▶ findings  ─ quick: language-scoped pattern rules
     │             └ standard: tiered hunter pool (retrieval + skill snippets)
     ──▶ independent verifier (evidence ladder enforced)
     ──▶ report.md + report.sarif + manifest.json (shared finding IDs)
```

- **quick** ✅: deterministic, language-scoped pattern rules + reachability +
  verification. No model calls, fully reproducible.
- **standard** ✅ scheduling / 🧭 LLM hunters: runs the tiered hunter pool
  (A/B/C budgets, retrieval context, selected Trail of Bits skill snippets,
  JSONL trajectories) and verification; language-aware LLM hunters are the next
  upgrade (today standard reuses the deterministic findings per target).
- **deep** 🧭: sandboxed build/fuzz/dynamic reproduction (not yet implemented).

## Detection coverage

Language-scoped rule families (rules only run against matching languages, which
removes cross-language false positives):

| Language | Classes (CWE) |
| --- | --- |
| C / C++ | buffer overflow (120), format string (134), command injection (78) |
| Python | command injection (78), code injection (95), deserialization (502), SQLi (89), path traversal (22), SSRF (918), weak hash (327), SSTI (94), insecure default (489) |
| JavaScript / TS | command injection (78), code injection (95), reflected/DOM XSS (79), SQLi (89), path traversal (22), SSRF (918), weak hash (327), deserialization (502) |
| Java + Groovy templates | command injection (78), SQLi via concatenation (89), weak hash (327), deserialization (502), unescaped template XSS (79) |

Cheat-sheet fixtures live under `benchmarks/fixtures/` and feed the 90/10 smoke
gate. **Efficiency** is measured separately on isolated vuln-vs-fixed pairs
(`ousast pairs`, `python -m openultrasast.pair_gate`): the harness must fire on
the vulnerable snapshot and stay silent after the patch. Local fixture
counterparts are a CI gate; vendored GitHub VFCs (SVEN, CWE-Bench-Java, NVD
patterns) are the honesty dashboard and the miss/fp signals for `ousast improve`.
Dataset pointers (CVEfixes, DiverseVul, PrimeVul, MegaVul, MoreFixes, Vul4J,
GitHub Advisory Database, OSV) are in `benchmarks/pairs/datasets.toml`.

## Project scores and central CWE policy

Severity used to be whatever string a rule set on itself. It is now governed centrally: one CWE, one severity, decided by policy — not by the rule that fired.

**Central CWE policy** ✅ (`policy/verycode.py`): a vendored `CWE_Score.tsv` (verycode-policies, ~167 CWEs) is the single source of truth. Each CWE carries a flaw category, a `severity` (0-5), and `static` / `dynamic` scope flags. `load_policy()` reads it positionally (the upstream file ships CRLF and a trailing space in the `Flaw Severity ` header), and `resolve_severity()` resolves a finding's severity *exclusively* from policy, keyed on CWE — any legacy rule-local severity is discarded. CWEs that are not `static` resolve to `0` and are report-only, never scored.

**Fail-loud startup** ✅: `assert_rules_resolve(PATTERN_RULES, policy)` runs as the `policy_check` stage right after `policy_load`. If any *enabled* rule names a CWE the policy does not govern, it raises `PolicyError` and the scan aborts before doing work — you cannot ship a rule whose severity nobody decided.

**0-100 project score** ✅ (`scoring/project_score.py`): each finding's penalty is `severity weight × reachability multiplier`, and the score is an exponential decay of the total (`100 · e^(−total/k)`, `k=60`).

| Severity | Weight (`SEV_WEIGHT`) | | Reachability | Multiplier (`REACH_MULT`) |
| --- | --- | --- | --- | --- |
| 5 | 50 | | `reachable` | 1.0 |
| 4 | 25 | | `inferred-file-surface` | 0.6 |
| 3 | 10 | | `unknown` | 0.4 |
| 2 / 1 / 0 | 2 / 1 / 0 | | | |

No penalty → 100; one reachable severity-5 finding → ~43. The **reachability multiplier is the false-positive calibration knob**: a confirmed FP lowers a finding's effective reachability instead of deleting the rule, so the score moves without losing the detection.

**Two-condition gate** ✅: a finding that is both severity-5 *and* `reachable` **always** fails the gate. The `score < min_score` threshold (default `min_score=80`) only fails when `blocking` is enabled — so scoring is **advisory-first by default** and turns into a CI gate when you opt in.

**Artifacts** ✅: the `score` stage writes `score.json` (project score, `max_severity`, `penalty_total`, `by_category`, `out_of_scope_dynamic_only`, `unmapped_cwe`, gate verdict) and merges the same block into `manifest.json`. Scoring is zero-dependency (stdlib only). One mapping detail: verycode has no CWE-120 (generic buffer copy), so the C/C++ memory-unsafe rules listed under [Detection coverage](#detection-coverage) carry **CWE-121** (Stack-Based Buffer Overflow, severity 5), which the policy does govern.

🧭 This is the first implemented slice of the self-improving ruleset work: a central, policy-governed severity model and a project score. Rules-as-data and the full self-improvement loop over rulesets are later phases.

## Running the benchmarks

Benchmarks wrap the same scan pipeline and score it against ground-truth
manifests in `benchmarks/manifests/`. Each manifest lists the *known*
vulnerabilities (CWE, file, line, sink, evidence) plus optional external
baselines; fixtures live in `benchmarks/fixtures/` and include safe-API files so
precision is measured honestly.

```bash
# One language
uv run ousast benchmark benchmarks/manifests/cpp-damn-vulnerable.toml --mode quick

# All in-scope corpora
for m in c-cpp-smoke cpp-damn-vulnerable \
         python-web-smoke python-vulnerable \
         javascript-node-web-smoke javascript-vulnerable \
         java-web-smoke java-spring-boot-vulnerable; do
  uv run ousast benchmark "benchmarks/manifests/$m.toml" --mode quick
done
```

Each run writes `.openultrasast/benchmarks/<id>/`:
`benchmark_result.json` (recall, precision, misses, false positives, runtime),
`calibration_records.json` (every miss as a next-improvement candidate), and
`external_baseline_deltas.json` (tool-vs-tool comparison).

### Latest results (`--mode quick`)

The corpora are modeled on Damn Vulnerable C/C++, PyGoat/DVPWA and NodeGoat,
plus the real [`kiview/damn-vulnerable-spring-boot-app`](https://github.com/kiview/damn-vulnerable-spring-boot-app)
vendored verbatim (MIT). Three vulnerabilities are deliberately left in the
ground truth that pattern matching *cannot* reach (integer overflow, second-order
SQLi, prototype pollution) so recall is not self-fulfilling.

| Language | Recall | False-positive rate |
| --- | --- | --- |
| C / C++ | 93.3% (14/15) | 0.0% |
| Python | 93.3% (14/15) | 0.0% |
| JavaScript | 92.3% (12/13) | 0.0% |
| Java | 100% (4/4) | 0.0% |
| **Overall** | **93.6% (44/47)** | **0.0%** |

The project goal is **≥90% recall and <10% false positives** per language. The
gate lives in `tests/test_detection_benchmarks.py`, so a rule change that drops
recall or raises false positives fails CI.

## Agent integrations, fusion, and ultra workflows

The `ousast` CLI is the interface. An agent that can run shell commands needs
nothing else:

```bash
uv run ousast scan . --mode quick --fail-on verified
```

For [OpenCode](https://opencode.ai) there are optional project skills that
describe the workflows above, so the agent drives the same CLI
(`.opencode/skills/`):

| Skill | Purpose |
| --- | --- |
| `openultrasast-scan` ✅ | run/plan `ousast scan`, indexing, evidence-gated analysis |
| `openultrasast-triage` ✅ | false-positive elimination, ranking calibration, verifier adjudication |
| `openultrasast-fix-audit` ✅ | OpenUltraCode fix lifecycle and adversarial fix review |

### MCP server (narrow surface) ✅

`ousast mcp` runs a zero-dependency MCP server over stdio (newline-delimited
JSON-RPC) exposing only stable, project-level operations — **never** arbitrary
shell, Docker, or internal hunter tools. Point an MCP client (OpenCode, Claude
Desktop, an IDE) at it:

```jsonc
// e.g. an MCP client config entry
{ "command": "uv", "args": ["run", "ousast", "mcp"] }
```

Tools: `openultrasast.scan`, `status`, `findings`, `get_finding`, `evidence`,
`artifacts`, `benchmark`, `explain`, `propose_patch` (degrades visibly — the patch
oracle is a later phase), and `export_report`. The `scan`/`benchmark` tools run the
bounded analysis pipeline; no tool accepts a free-form command. A typical flow:
`scan {path}` → returns a `run_dir` → `findings {run_dir}` → `explain {run_dir, finding_id}`.

**Ultra workflows (OpenUltraCode discipline):** fixes are never a one-shot patch
prompt. `openultrasast-fix-audit` runs the bounded lifecycle
`intake → plan → minimal patch → adversarial review → reconcile → fresh
verification → ready`, and a patch can only reach `patch_validated` after
sandboxed validation passes with no accepted blocking findings.

**Fusion (deepening) ✅:** when a finding needs more reasoning than the normal
ranker → hunter → verifier → mapping loop provides (critical/high severity,
verifier disagreement, conflicting static vs semantic evidence, risky fixes, or
an explicit high-assurance request), fusion runs two independent panels that
steel-man the vulnerability and false-positive cases, vote, and a decider issues
the disposition (accepted / rejected / mitigated / deferred / blocked) with votes,
model IDs, and degradations disclosed. It runs automatically on triggered findings
in `--mode standard`, writing `fusion.json` and a `fusion` block in the manifest.
Deterministic by default (`fusion.py`); set `[fusion] panel_model` to route the
panels through the configured provider:

```toml
[fusion]
enabled        = true       # default; runs on triggered findings in standard mode
panel_model    = "gpt-4o"   # optional: LLM panels via the [harnessx] provider
decider_model  = "gpt-4o"   # optional: disclosed in the decision's model IDs
high_assurance = false      # true forces fusion on every finding
```

> The `kiro-*` skills, `AGENTS.md` and the `.kiro/` directory are the
> maintainer's spec-driven development tooling. They are not part of using
> OpenUltraSAST and can be ignored by users.

## Evidence ladder: how a false positive is eliminated

Evidence level is a state machine, not a label a model may assign. The verifier
runs on independent context (it never sees hunter reasoning) and enforces:

```
suspicion ─▶ static_corroboration ─▶ crash_reproduced ─▶ root_cause_explained
          ─▶ exploit_demonstrated ─▶ patch_validated
```

A finding is rejected or held back unless the *artifact* for the next level
actually exists:

- below `static_corroboration` → `NEEDS_EVIDENCE` (a model/heuristic suspicion is
  never reported as verified);
- `static_corroboration` but **reachability unknown** → `REJECTED` with a
  tie-breaker demanding call-graph / route / CLI / parser / dynamic evidence
  (this is what kills "pattern matched but nothing attacker-controlled reaches
  it" findings);
- `static_corroboration` **and** function-level reachability → `ACCEPTED`.

When a finding is still rejected after review, `openultrasast-triage` records a
**scoped false-positive learning** (`calibration.py`): a reason from a fixed
taxonomy (`unreachable_path`, `missing_attacker_control`, `sanitizer_disproved`,
`static_rule_mismatch`, `incorrect_model_assumption`, `duplicate`,
`insufficient_impact`, `unsupported`, `contradicted`, `unverified`), evidence,
and a **scope**. Scoping keeps the learning narrow: a rejected `syscall_entry`
claim in `auth/` demotes only `auth/…`, never the whole vulnerability class. This
is covered by `tests/test_calibration.py::test_scoped_false_positive_demotion_does_not_suppress_class_globally`.
Demotion (with an audit trail) is preferred over deletion, so an analyst can
always see *why* something was downgraded.

## How a false negative is eliminated

Misses are first-class signals, surfaced by the benchmark layer rather than
hidden. Every expected vulnerability that no finding matched becomes a
`BenchmarkCalibrationRecord` in `calibration_records.json`, e.g. the integer
overflow the regex engine cannot see:

```json
{
  "cwe": "CWE-190",
  "vulnerability_class": "integer overflow",
  "path": "src/buffer.cpp",
  "failed_stage": "benchmark_ground_truth_matching",
  "next_improvement_candidate": "rules_or_language_hunter",
  "reason": "no OpenUltraSAST finding matched the expected benchmark vulnerability"
}
```

The `next_improvement_candidate` routes the gap to the stage that should close
it: a static rule, SARIF source, entry-point mapping, retrieval package, hunter
prompt, dynamic reproducer, or skill route. Once a miss is
addressed, it stays closed because the recall/precision gate runs in CI.
`external_baseline_deltas.json` additionally shows where another tool found a
vulnerability OpenUltraSAST missed (or vice-versa).

## Configuring the HarnessX agentic plane

The LLM hunter pool and the llm-judge verifier are optional. The core install is
zero-dependency and `--mode quick` never calls a model. To enable the agentic
plane, install the extra, name the models, and pick a provider:

```bash
uv sync --extra harnessx          # SHA-pinned optional dependency
export OPENAI_API_KEY=sk-...       # or ANTHROPIC_API_KEY, matching the provider
uv run ousast scan /path/to/code --mode standard --config openultrasast.toml
```

```toml
# openultrasast.toml
[models]
hunter   = "gpt-4o"   # LLM hunter pool (empty -> deterministic hunter)
verifier = "gpt-4o"   # llm-judge verifier (empty -> structural verifier)

[harnessx]
provider        = "openai"   # "anthropic" (default) | "openai" | "litellm"
max_cost_usd    = 2.0        # per-task spend cap
token_threshold = 120000     # per-task token budget
```

The provider selects the HarnessX backend for **both** the hunter and the
verifier; each reads its own standard key env var (`openai` → `OPENAI_API_KEY`,
`anthropic` → `ANTHROPIC_API_KEY`). The HarnessX path activates only when the
extra is installed, a model is configured, and the mode is `standard` — all three
gated through one capability seam (`harness_ext.has_harnessx`). If any condition
fails, the scan transparently falls back to the deterministic hunter / structural
verifier and records a `degradations` entry in `manifest.json`, so a run is never
silently downgraded:

```bash
jq '.degradations' "$run/manifest.json"   # null = HarnessX ran; entries = fell back
```

## The HarnessX self-improving cycle

Each scan is a composed harness of typed processors with read/write **state
contracts**. Strict mode fails the scan on a violation; warn mode records a
degradation. The loop now runs inside the pipeline without an agent: every scan
persists verifier rejections as scoped learnings, and the next scan loads them
and demotes those scopes before findings are produced.

```
        ┌──────────── automatic ledger (no agent in the loop) ───────────┐
        ▼                                                                 │
  scan ──▶ verify ──▶ non-accepted findings ──▶ scoped learnings ─────────┤
        │             (.openultrasast/calibration/                        │  loaded by
        │              false_positive_learnings.json)                     │  the next
  next scan ──▶ rank ──▶ calibrate (demote rejected scopes) ──▶ findings ──┘  scan
```

What runs automatically each scan (✅, `calibration.py` + `cli.py`):

1. **`calibrate` stage** (after `rank`, before findings) loads the persistent
   ledger and demotes the priority of every scope that previously produced a
   rejected finding (`calibrate_rankings`), writing `applied_calibrations.json`.
2. **`record_calibration` stage** (after `verify`) turns this run's non-accepted
   verifier outcomes into scoped `FalsePositiveLearning` records
   (`learnings_from_verifications`) and merges them into the ledger, de-duplicated
   by finding ID so a repeat rejection does not compound without bound.

Demotion is **scoped and reversible**: a rejection in `lib.py` demotes only that
scope; an accepted (reachable) finding in `app.py` is never touched; and because
priority is demoted rather than the finding deleted, the audit trail
(`applied_calibrations.json`, the ledger) shows exactly why a surface lost
attention. Covered by `tests/test_pipeline_calibration.py`.

### Rule-level loop: `ousast improve`

The ranking loop above adjusts *attention*. A second, bounded loop adjusts the
**governed ruleset itself** from benchmark feedback — and writes its result to the
same loop-owned ledger a scan reads, so the loop closes end-to-end:

```bash
# Preview what the loop would change (writes nothing)
uv run ousast improve benchmarks/manifests/java-spring-boot-vulnerable.toml --dry-run

# Apply: accepted rounds land in <target>/.openultrasast/calibration/rule_policy.json,
# which the next `ousast scan`/`benchmark` of that target loads automatically.
uv run ousast improve benchmarks/manifests/java-spring-boot-vulnerable.toml
```

Each round (`improve/evolve.py`): benchmark with the current ledger → per-rule
signals (`rule_signals.json`) → propose **bounded** edits → validate → replay
smoke → re-benchmark → a **hard acceptance gate** → accept or revert
byte-for-byte. The loop may only pull two levers (rule status, score constants);
it can never edit a rule's pattern text or the authoritative 0–5 CWE severity, and
must stage `enabled → shadow` before `disabled`. A round is accepted only if
**recall ≥ 90% AND FP < 10% AND the project score did not regress AND no matched
finding was lost** — otherwise the ledger is never written. Auto-shadowing removes
a precision-dragger's false-positive cost without deleting it (recall is
preserved); persistent misses are *nominated* through the signal file, leaving
pattern authoring to a human pull request. The project score is the optimization
reward; the recall/FP gate is a separate hard constraint that is never folded into
it — and the same gate runs in CI (`python -m openultrasast.gate`), so a loop
result that breached it could never merge. Covered by `tests/test_improve.py` and
`tests/test_cli_improve.py`.

This is the deterministic substrate of the self-improvement loop. When the
optional `openultrasast[harnessx]` extra is present, the HarnessX `MetaAgent`
becomes the richer *proposer* that plugs into this same validated, gated
machinery — the safety contract (bounded levers, replay/novelty/journal gates,
hard acceptance gate, byte-for-byte revert) is identical either way.

Beyond these loops, the `openultrasast-triage` skill lets an agent adjust
prompt constraints, retrieval filters, skill routing and benchmark-miss triage.
Those `RankingCalibration` fields are already produced and will be consumed
directly once the per-family detectors land.

## Security & hardening

OpenUltraSAST treats scanned code as **untrusted input and never executes it** (quick
and standard modes). Scan artifacts are scrubbed of credentials before they are written
(`[hardening] redact_secrets`, on by default), agentic spend and output size are bounded
(`[harnessx] max_cost_usd`/`token_threshold`, `[hardening] max_findings`), provider calls
retry transient failures with backoff, and missing capabilities degrade visibly in the
manifest rather than silently. See [docs/threat-model.md](docs/threat-model.md) for the
full trust boundaries and sandbox limits, and [docs/examples.md](docs/examples.md) for
end-to-end walkthroughs.

## Development

```bash
uv run pytest                 # tests, incl. the 90/10 detection gate
uv run ruff check .           # lint
uv run ruff format --check .  # format
uv run mypy src/openultrasast # types
uv run python -m openultrasast.gate  # standalone detection gate
uv run python -m openultrasast.pair_gate  # local vuln-vs-fix efficiency + GitHub honesty
uv run python dagger/ci.py    # full containerized CI pipeline
```