mirror of
https://github.com/norandom/OpenUltraSAST.git
synced 2026-10-16 03:49:26 +00:00
129 lines
3.8 KiB
Python
129 lines
3.8 KiB
Python
from __future__ import annotations
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import json
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import os
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import tomllib
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from dataclasses import asdict, dataclass
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from pathlib import Path
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DEFAULT_VECTOR_STORE = "json-local"
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@dataclass(frozen=True)
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class ModelConfig:
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ranker: str | None = None
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hunter: str | None = None
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verifier: str | None = None
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patcher: str | None = None
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@dataclass(frozen=True)
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class EmbeddingConfig:
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model: str | None = None
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store: str = DEFAULT_VECTOR_STORE
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@dataclass(frozen=True)
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class SandboxConfig:
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network: bool = False
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workspace_readonly: bool = True
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memory_mb: int = 2048
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timeout_seconds: int = 300
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pids_limit: int = 512
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@dataclass(frozen=True)
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class DynamicConfig:
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enabled: bool = False
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network_scope: tuple[str, ...] = ()
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@dataclass(frozen=True)
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class EvidenceConfig:
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minimum_report_verified: str = "static_corroboration"
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minimum_exploit: str = "crash_reproduced"
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minimum_patch: str = "root_cause_explained"
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@dataclass(frozen=True)
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class ResolvedConfig:
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models: ModelConfig = ModelConfig()
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embeddings: EmbeddingConfig = EmbeddingConfig()
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sandbox: SandboxConfig = SandboxConfig()
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dynamic: DynamicConfig = DynamicConfig()
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evidence: EvidenceConfig = EvidenceConfig()
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runs_dir: str = ".openultrasast/runs"
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def load_config(config_path: Path | None = None) -> ResolvedConfig:
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data: dict[str, object] = {}
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if config_path is not None and config_path.exists():
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with config_path.open("rb") as handle:
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data = tomllib.load(handle)
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return ResolvedConfig(
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models=_load_models(data.get("models", {})),
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embeddings=_load_embeddings(data.get("embeddings", {})),
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sandbox=_load_sandbox(data.get("sandbox", {})),
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dynamic=_load_dynamic(data.get("dynamic", {})),
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evidence=_load_evidence(data.get("evidence", {})),
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runs_dir=os.environ.get("OPENULTRASAST_RUNS_DIR", ".openultrasast/runs"),
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)
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def write_resolved_config(config: ResolvedConfig, path: Path) -> None:
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path.parent.mkdir(parents=True, exist_ok=True)
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path.write_text(json.dumps(asdict(config), indent=2, sort_keys=True) + "\n")
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def _section(value: object) -> dict[str, object]:
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return value if isinstance(value, dict) else {}
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def _string(value: object) -> str | None:
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return value if isinstance(value, str) and value else None
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def _load_models(value: object) -> ModelConfig:
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data = _section(value)
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return ModelConfig(
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ranker=_string(data.get("ranker")),
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hunter=_string(data.get("hunter")),
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verifier=_string(data.get("verifier")),
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patcher=_string(data.get("patcher")),
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)
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def _load_embeddings(value: object) -> EmbeddingConfig:
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data = _section(value)
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return EmbeddingConfig(model=_string(data.get("model")), store=_string(data.get("store")) or DEFAULT_VECTOR_STORE)
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def _load_sandbox(value: object) -> SandboxConfig:
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data = _section(value)
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return SandboxConfig(
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network=bool(data.get("network", False)),
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workspace_readonly=bool(data.get("workspace_readonly", True)),
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memory_mb=int(data.get("memory_mb", 2048)),
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timeout_seconds=int(data.get("timeout_seconds", 300)),
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pids_limit=int(data.get("pids_limit", 512)),
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)
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def _load_dynamic(value: object) -> DynamicConfig:
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data = _section(value)
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scope = data.get("network_scope", [])
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if not isinstance(scope, list):
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scope = []
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return DynamicConfig(enabled=bool(data.get("enabled", False)), network_scope=tuple(str(item) for item in scope))
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def _load_evidence(value: object) -> EvidenceConfig:
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data = _section(value)
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return EvidenceConfig(
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minimum_report_verified=str(data.get("minimum_report_verified", "static_corroboration")),
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minimum_exploit=str(data.get("minimum_exploit", "crash_reproduced")),
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minimum_patch=str(data.get("minimum_patch", "root_cause_explained")),
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)
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