mirror of
https://github.com/norandom/OpenUltraSAST.git
synced 2026-10-16 03:49:26 +00:00
Marius CiepluchandClaude Opus 5
b9552518b9
Task 3.7. A round proposes one change for one family and then lets the evidence decide whether it stays. The stages run cheapest first: a train minibatch, then the family's held-out pairs, then every other family. Each stage scores several runs per pair, and asking for fewer than three is refused outright, because a single run misses most real changes. The family directory is snapshotted before the change, so every outcome other than acceptance leaves the tree exactly as it was found: a tie, a regression on either side, another family below its floor, a proposal that reaches beyond its directory, or a round that crosses its cost cap. Acceptance bumps only that family's version and records which configuration won which pair. Every outcome is journalled, including having no proposal at all, and the round directory holds the proposal, the scores, the pair identities behind the attribution counts and the detector trajectories, all redacted, so a round can be replayed and its attribution audited rather than taken on trust. A round number is never reused. Recorded while writing the sweep: with per-family directories a change cannot reach another family, so the sweep is a floor check over every other family at the moment this round would land, not a causal claim about this change. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
224 lines
9.5 KiB
Python
224 lines
9.5 KiB
Python
"""learning-harness task 3.7: one evolve round, end to end (offline)."""
|
|
|
|
from __future__ import annotations
|
|
|
|
import json
|
|
from pathlib import Path
|
|
|
|
import pytest
|
|
|
|
from openultrasast.benchmark import ExpectedFinding
|
|
from openultrasast.findings import StaticFinding
|
|
from openultrasast.learning.detectors import FamilyConfig, write_default_configs
|
|
from openultrasast.learning.families import load_families
|
|
from openultrasast.learning.journal import Archive, LearningJournal
|
|
from openultrasast.learning.proposer import Proposal, ScriptedProposer
|
|
from openultrasast.learning.rounds import NoiseFloor
|
|
from openultrasast.pairs import PairCase
|
|
|
|
VULN = "from flask import request\nimport os\n\n\ndef run():\n os.system(request.args.get('cmd'))\n"
|
|
FIXED = "from flask import request\nimport subprocess\n\n\ndef run():\n subprocess.run(['echo', 'x'], check=False)\n"
|
|
|
|
|
|
def _case(tmp_path: Path, name: str, *, split: str, family: str = "injection") -> PairCase:
|
|
(tmp_path / f"{name}-v.py").write_text(VULN)
|
|
(tmp_path / f"{name}-f.py").write_text(FIXED)
|
|
return PairCase(
|
|
name=name,
|
|
slice="vibe-py",
|
|
language="python",
|
|
origin="test",
|
|
vuln_file=tmp_path / f"{name}-v.py",
|
|
fixed_file=tmp_path / f"{name}-f.py",
|
|
relpath="app.py",
|
|
expected=(
|
|
ExpectedFinding(
|
|
cwe="CWE-78", vulnerability_class="x", path="app.py", evidence="", function="run", mechanism="other", family=family
|
|
),
|
|
),
|
|
min_recall=1.0,
|
|
fix_policy="silent",
|
|
split=split,
|
|
review_tier="seeded",
|
|
)
|
|
|
|
|
|
def _finding(family: str) -> StaticFinding:
|
|
return StaticFinding(
|
|
finding_id="detector:app.py:6",
|
|
path="app.py",
|
|
title="t",
|
|
severity="high",
|
|
confidence="low",
|
|
evidence_level="suspicion",
|
|
rationale="r",
|
|
line=6,
|
|
function_name="run",
|
|
reachability_status="unknown",
|
|
reachability_evidence=[],
|
|
reachability_conditions=[],
|
|
tags=[f"family:{family}", "detector:x@0"],
|
|
ranking_priority=0.0,
|
|
)
|
|
|
|
|
|
def _factory(finds: dict[str, bool]): # type: ignore[no-untyped-def]
|
|
"""A detector whose behaviour depends on the configuration version it was built from."""
|
|
|
|
def build(config: FamilyConfig): # type: ignore[no-untyped-def]
|
|
def scan(root: Path) -> list[StaticFinding]:
|
|
if root.name != "vuln":
|
|
return []
|
|
return [_finding(config.family)] if finds.get(config.checklist.strip(), False) else []
|
|
|
|
return scan
|
|
|
|
return build
|
|
|
|
|
|
def _proposal(text: str) -> Proposal:
|
|
return Proposal(hypothesis="ask about quoting", lever="checklist", change={"checklist.md": text}, predicted_affected=("injection",))
|
|
|
|
|
|
@pytest.fixture
|
|
def world(tmp_path: Path): # type: ignore[no-untyped-def]
|
|
configs = tmp_path / "configs"
|
|
write_default_configs(configs, load_families(), prompt="p", version="0")
|
|
(configs / "injection" / "checklist.md").write_text("- old\n")
|
|
cases = [_case(tmp_path, "train-a", split="train"), _case(tmp_path, "hold-a", split="holdout")]
|
|
return {
|
|
"configs": configs,
|
|
"cases": cases,
|
|
"journal": LearningJournal(tmp_path / "journal.jsonl"),
|
|
"archive": Archive(tmp_path / "archive.jsonl"),
|
|
"out": tmp_path / "out",
|
|
"floors": {"injection": NoiseFloor(family="injection", k_runs=5, pairs=1, flaky_pairs=0, negative_flip_rate=0.0, budget_flips=0)},
|
|
}
|
|
|
|
|
|
def _run(world, proposal: Proposal | None, finds: dict[str, bool], **kwargs): # type: ignore[no-untyped-def]
|
|
from openultrasast.learning.rounds import run_learning_round
|
|
|
|
return run_learning_round(
|
|
world["cases"],
|
|
family="injection",
|
|
taxonomy=load_families(),
|
|
configs_dir=world["configs"],
|
|
proposer=ScriptedProposer([proposal] if proposal else []),
|
|
scan_factory=_factory(finds),
|
|
model="m",
|
|
journal=world["journal"],
|
|
archive=world["archive"],
|
|
floors=world["floors"],
|
|
out_dir=world["out"],
|
|
**kwargs,
|
|
)
|
|
|
|
|
|
def test_a_change_that_helps_is_accepted_versioned_and_journalled(world) -> None: # type: ignore[no-untyped-def]
|
|
record = _run(world, _proposal("- ask about quoting\n"), {"- ask about quoting": True})
|
|
assert record.outcome == "accepted" and record.family == "injection"
|
|
assert record.target_train_delta > 0 and record.target_holdout_delta > 0
|
|
assert (world["configs"] / "injection" / "checklist.md").read_text() == "- ask about quoting\n"
|
|
assert 'version = "1"' in (world["configs"] / "injection" / "family.toml").read_text()
|
|
assert [item.outcome for item in world["journal"].rounds()] == ["accepted"]
|
|
assert world["archive"].winners("injection")
|
|
|
|
|
|
def test_a_change_that_helps_nothing_is_rejected_and_reverted(world) -> None: # type: ignore[no-untyped-def]
|
|
record = _run(world, _proposal("- useless\n"), {})
|
|
assert record.outcome == "rejected" and record.reason == "tie"
|
|
assert (world["configs"] / "injection" / "checklist.md").read_text() == "- old\n"
|
|
assert 'version = "0"' in (world["configs"] / "injection" / "family.toml").read_text()
|
|
assert world["journal"].rejected_buffer("injection") == ("ask about quoting",)
|
|
|
|
|
|
def test_a_round_is_rejected_when_the_sweep_finds_another_family_below_its_floor(world) -> None: # type: ignore[no-untyped-def]
|
|
"""The sweep is a floor check over every other family, not a causal claim about this change.
|
|
|
|
Per-family directories make a change that reaches another family structurally impossible, so what the
|
|
sweep really guards is a family that is failing for any reason at the moment this round would land.
|
|
"""
|
|
world["cases"].append(_case(world["configs"].parent, "other-hold", split="holdout", family="access_control"))
|
|
world["floors"]["access_control"] = NoiseFloor(
|
|
family="access_control", k_runs=5, pairs=1, flaky_pairs=0, negative_flip_rate=0.0, budget_flips=0
|
|
)
|
|
|
|
def build(config: FamilyConfig): # type: ignore[no-untyped-def]
|
|
def scan(root: Path) -> list[StaticFinding]:
|
|
if root.name != "vuln" or config.family == "access_control":
|
|
return [] # access control finds nothing, so its holdout pair is below its floor
|
|
return [_finding("injection")] if config.checklist.strip() == "- ask about quoting" else []
|
|
|
|
return scan
|
|
|
|
from openultrasast.learning.rounds import run_learning_round
|
|
|
|
record = run_learning_round(
|
|
world["cases"],
|
|
family="injection",
|
|
taxonomy=load_families(),
|
|
configs_dir=world["configs"],
|
|
proposer=ScriptedProposer([_proposal("- ask about quoting\n")]),
|
|
scan_factory=build,
|
|
model="m",
|
|
journal=world["journal"],
|
|
archive=world["archive"],
|
|
floors=world["floors"],
|
|
out_dir=world["out"],
|
|
sweep_families=("access_control",),
|
|
)
|
|
assert record.outcome == "rejected" and record.reason == "collateral_regression"
|
|
assert record.sweep["access_control"] == 1
|
|
assert (world["configs"] / "injection" / "checklist.md").read_text() == "- old\n"
|
|
|
|
|
|
def test_a_round_over_its_cost_cap_is_reverted_and_says_so(world) -> None: # type: ignore[no-untyped-def]
|
|
record = _run(world, _proposal("- ask about quoting\n"), {"- ask about quoting": True}, cost_cap_usd=0.0, spent_usd=lambda: 1.0)
|
|
assert record.outcome == "reverted_cost" and record.cost_usd == 1.0
|
|
assert (world["configs"] / "injection" / "checklist.md").read_text() == "- old\n"
|
|
|
|
|
|
def test_a_proposal_that_reaches_beyond_its_family_never_runs(world) -> None: # type: ignore[no-untyped-def]
|
|
record = _run(world, Proposal(hypothesis="h", lever="checklist", change={"../escape.md": "x"}), {})
|
|
assert record.outcome == "refused_holdout" or record.outcome == "rejected"
|
|
assert "outside" in record.reason
|
|
assert not (world["configs"].parent / "escape.md").exists()
|
|
|
|
|
|
def test_a_round_with_no_proposal_is_recorded_rather_than_skipped(world) -> None: # type: ignore[no-untyped-def]
|
|
record = _run(world, None, {})
|
|
assert record.outcome == "rejected" and record.reason == "no_proposal"
|
|
assert [item.round for item in world["journal"].rounds()] == [1]
|
|
|
|
|
|
def test_the_round_directory_holds_what_a_replay_needs(world) -> None: # type: ignore[no-untyped-def]
|
|
record = _run(
|
|
world,
|
|
_proposal("- ask about quoting\n"),
|
|
{"- ask about quoting": True},
|
|
trajectories=lambda: [{"pair": "train-a", "prompt": "api_key = " + "'" + "B" * 24 + "'"}],
|
|
)
|
|
directory = world["out"] / "rounds" / "1"
|
|
assert (directory / "proposal.json").is_file() and (directory / "scores.json").is_file()
|
|
attribution = json.loads((directory / "attribution.json").read_text())
|
|
assert attribution["flipped_predicted"] == ["hold-a"] or attribution["flipped_predicted"] == ["train-a", "hold-a"]
|
|
assert attribution["flipped_unpredicted"] == []
|
|
trajectories = (directory / "trajectories.jsonl").read_text()
|
|
assert "REDACTED" in trajectories and "B" * 24 not in trajectories
|
|
assert record.attribution.precision == 1.0
|
|
|
|
|
|
def test_a_round_number_is_never_reused(world) -> None: # type: ignore[no-untyped-def]
|
|
from openultrasast.learning.journal import JournalError
|
|
|
|
_run(world, _proposal("- ask about quoting\n"), {"- ask about quoting": True})
|
|
(world["out"] / "rounds" / "2").mkdir(parents=True)
|
|
with pytest.raises(JournalError, match="2"):
|
|
_run(world, _proposal("- another\n"), {})
|
|
|
|
|
|
def test_every_stage_scores_at_least_three_runs_per_pair(world) -> None: # type: ignore[no-untyped-def]
|
|
with pytest.raises(ValueError, match="three"):
|
|
_run(world, _proposal("- x\n"), {}, k_runs=2)
|