Justin Ibarra
472 lines
15 KiB
Python
472 lines
15 KiB
Python
# Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
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# or more contributor license agreements. Licensed under the Elastic License
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# 2.0; you may not use this file except in compliance with the Elastic License
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# 2.0.
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"""Rule object."""
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import dataclasses
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import json
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import typing
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from dataclasses import dataclass, field
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from functools import cached_property
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from pathlib import Path
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from typing import Literal, Union, Optional, List, Any
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from uuid import uuid4
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from marshmallow import ValidationError, validates_schema
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from . import utils
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from .mixins import MarshmallowDataclassMixin
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from .rule_formatter import toml_write, nested_normalize
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from .schemas import definitions, SCHEMA_DIR
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from .schemas import downgrade
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from .utils import cached
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_META_SCHEMA_REQ_DEFAULTS = {}
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@dataclass(frozen=True)
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class RuleMeta(MarshmallowDataclassMixin):
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"""Data stored in a rule's [metadata] section of TOML."""
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creation_date: definitions.Date
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updated_date: definitions.Date
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deprecation_date: Optional[definitions.Date]
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# Optional fields
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beats_version: Optional[definitions.BranchVer]
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ecs_versions: Optional[List[definitions.BranchVer]]
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comments: Optional[str]
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maturity: Optional[definitions.Maturity]
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os_type_list: Optional[List[definitions.OSType]]
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query_schema_validation: Optional[bool]
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related_endpoint_rules: Optional[List[str]]
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@dataclass(frozen=True)
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class BaseThreatEntry:
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id: str
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name: str
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reference: str
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@dataclass(frozen=True)
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class SubTechnique(BaseThreatEntry):
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"""Mapping to threat subtechnique."""
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reference: definitions.SubTechniqueURL
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@dataclass(frozen=True)
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class Technique(BaseThreatEntry):
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"""Mapping to threat subtechnique."""
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# subtechniques are stored at threat[].technique.subtechnique[]
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reference: definitions.TechniqueURL
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subtechnique: Optional[List[SubTechnique]]
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@dataclass(frozen=True)
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class Tactic(BaseThreatEntry):
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"""Mapping to a threat tactic."""
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reference: definitions.TacticURL
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@dataclass(frozen=True)
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class ThreatMapping(MarshmallowDataclassMixin):
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"""Mapping to a threat framework."""
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framework: Literal["MITRE ATT&CK"]
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tactic: Tactic
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technique: Optional[List[Technique]]
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@staticmethod
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def flatten(threat_mappings: Optional[List]) -> 'FlatThreatMapping':
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"""Get flat lists of tactic and technique info."""
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tactic_names = []
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tactic_ids = []
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technique_ids = set()
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technique_names = set()
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sub_technique_ids = set()
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sub_technique_names = set()
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for entry in (threat_mappings or []):
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tactic_names.append(entry.tactic.name)
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tactic_ids.append(entry.tactic.id)
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for technique in (entry.technique or []):
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technique_names.add(technique.name)
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technique_ids.add(technique.id)
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for subtechnique in (technique.subtechnique or []):
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sub_technique_ids.update(subtechnique.id)
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sub_technique_names.update(subtechnique.name)
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return FlatThreatMapping(
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tactic_names=sorted(tactic_names),
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tactic_ids=sorted(tactic_ids),
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technique_names=sorted(technique_names),
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technique_ids=sorted(technique_ids),
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sub_technique_names=sorted(sub_technique_names),
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sub_technique_ids=sorted(sub_technique_ids)
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)
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@dataclass(frozen=True)
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class RiskScoreMapping(MarshmallowDataclassMixin):
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field: str
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operator: Optional[definitions.Operator]
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value: Optional[str]
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@dataclass(frozen=True)
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class SeverityMapping(MarshmallowDataclassMixin):
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field: str
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operator: Optional[definitions.Operator]
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value: Optional[str]
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severity: Optional[str]
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@dataclass(frozen=True)
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class FlatThreatMapping(MarshmallowDataclassMixin):
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tactic_names: List[str]
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tactic_ids: List[str]
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technique_names: List[str]
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technique_ids: List[str]
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sub_technique_names: List[str]
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sub_technique_ids: List[str]
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@dataclass(frozen=True)
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class BaseRuleData(MarshmallowDataclassMixin):
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actions: Optional[list]
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author: List[str]
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building_block_type: Optional[str]
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description: str
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enabled: Optional[bool]
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exceptions_list: Optional[list]
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license: Optional[str]
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false_positives: Optional[List[str]]
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filters: Optional[List[dict]]
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# trailing `_` required since `from` is a reserved word in python
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from_: Optional[str] = field(metadata=dict(data_key="from"))
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interval: Optional[definitions.Interval]
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max_signals: Optional[definitions.MaxSignals]
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meta: Optional[dict]
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name: str
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note: Optional[definitions.Markdown]
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# can we remove this comment?
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# explicitly NOT allowed!
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# output_index: Optional[str]
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references: Optional[List[str]]
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risk_score: definitions.RiskScore
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risk_score_mapping: Optional[List[RiskScoreMapping]]
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rule_id: definitions.UUIDString
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rule_name_override: Optional[str]
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severity_mapping: Optional[List[SeverityMapping]]
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severity: definitions.Severity
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tags: Optional[List[str]]
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throttle: Optional[str]
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timeline_id: Optional[definitions.TimelineTemplateId]
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timeline_title: Optional[definitions.TimelineTemplateTitle]
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timestamp_override: Optional[str]
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to: Optional[str]
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type: definitions.RuleType
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threat: Optional[List[ThreatMapping]]
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@classmethod
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def save_schema(cls):
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"""Save the schema as a jsonschema."""
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fields: List[dataclasses.Field] = dataclasses.fields(cls)
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type_field = next(field for field in fields if field.name == "type")
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rule_type = typing.get_args(type_field.type)[0] if cls != BaseRuleData else "base"
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schema = cls.jsonschema()
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version_dir = SCHEMA_DIR / "master"
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version_dir.mkdir(exist_ok=True, parents=True)
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# expand out the jsonschema definitions
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with (version_dir / f"master.{rule_type}.json").open("w") as f:
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json.dump(schema, f, indent=2, sort_keys=True)
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def validate_query(self, meta: RuleMeta) -> None:
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pass
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@dataclass
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class QueryValidator:
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query: str
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@property
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def ast(self) -> Any:
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raise NotImplementedError
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def validate(self, data: 'QueryRuleData', meta: RuleMeta) -> None:
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raise NotImplementedError()
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@dataclass(frozen=True)
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class QueryRuleData(BaseRuleData):
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"""Specific fields for query event types."""
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type: Literal["query"]
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index: Optional[List[str]]
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query: str
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language: definitions.FilterLanguages
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@cached_property
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def validator(self) -> Optional[QueryValidator]:
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if self.language == "kuery":
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return KQLValidator(self.query)
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elif self.language == "eql":
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return EQLValidator(self.query)
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def validate_query(self, meta: RuleMeta) -> None:
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validator = self.validator
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if validator is not None:
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return validator.validate(self, meta)
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@cached_property
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def ast(self):
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validator = self.validator
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if validator is not None:
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return validator.ast
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@dataclass(frozen=True)
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class MachineLearningRuleData(BaseRuleData):
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type: Literal["machine_learning"]
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anomaly_threshold: int
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machine_learning_job_id: Union[str, List[str]]
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@dataclass(frozen=True)
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class ThresholdQueryRuleData(QueryRuleData):
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"""Specific fields for query event types."""
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@dataclass(frozen=True)
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class ThresholdMapping(MarshmallowDataclassMixin):
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@dataclass(frozen=True)
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class ThresholdCardinality:
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field: str
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value: definitions.ThresholdValue
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field: List[definitions.NonEmptyStr]
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value: definitions.ThresholdValue
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cardinality: Optional[ThresholdCardinality]
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type: Literal["threshold"]
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threshold: ThresholdMapping
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@dataclass(frozen=True)
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class EQLRuleData(QueryRuleData):
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"""EQL rules are a special case of query rules."""
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type: Literal["eql"]
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language: Literal["eql"]
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@dataclass(frozen=True)
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class ThreatMatchRuleData(QueryRuleData):
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"""Specific fields for indicator (threat) match rule."""
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@dataclass(frozen=True)
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class Entries:
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@dataclass(frozen=True)
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class ThreatMapEntry:
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field: definitions.NonEmptyStr
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type: Literal["mapping"]
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value: definitions.NonEmptyStr
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entries: List[ThreatMapEntry]
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type: Literal["threat_match"]
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concurrent_searches: Optional[definitions.PositiveInteger]
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items_per_search: Optional[definitions.PositiveInteger]
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threat_mapping: List[Entries]
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threat_filters: Optional[List[dict]]
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threat_query: Optional[str]
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threat_language: Optional[definitions.FilterLanguages]
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threat_index: List[str]
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threat_indicator_path: Optional[str]
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def validate_query(self, meta: RuleMeta) -> None:
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super(ThreatMatchRuleData, self).validate_query(meta)
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if self.threat_query:
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if not self.threat_language:
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raise ValidationError('`threat_language` required when a `threat_query` is defined')
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if self.threat_language == "kuery":
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threat_query_validator = KQLValidator(self.threat_query)
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elif self.threat_language == "eql":
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threat_query_validator = EQLValidator(self.threat_query)
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else:
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return
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threat_query_validator.validate(self, meta)
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# All of the possible rule types
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AnyRuleData = Union[QueryRuleData, EQLRuleData, MachineLearningRuleData, ThresholdQueryRuleData, ThreatMatchRuleData]
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@dataclass(frozen=True)
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class TOMLRuleContents(MarshmallowDataclassMixin):
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"""Rule object which maps directly to the TOML layout."""
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metadata: RuleMeta
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data: AnyRuleData = field(metadata=dict(data_key="rule"))
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@classmethod
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def all_rule_types(cls) -> set:
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types = set()
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for subclass in typing.get_args(AnyRuleData):
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field = next(field for field in dataclasses.fields(subclass) if field.name == "type")
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types.update(typing.get_args(field.type))
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return types
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@classmethod
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def get_data_subclass(cls, rule_type: str) -> typing.Type[BaseRuleData]:
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"""Get the proper subclass depending on the rule type"""
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for subclass in typing.get_args(AnyRuleData):
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field = next(field for field in dataclasses.fields(subclass) if field.name == "type")
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if (rule_type, ) == typing.get_args(field.type):
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return subclass
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raise ValueError(f"Unknown rule type {rule_type}")
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@property
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def id(self) -> definitions.UUIDString:
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return self.data.rule_id
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@property
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def name(self) -> str:
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return self.data.name
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def lock_info(self) -> dict:
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return {"rule_name": self.name, "sha256": self.sha256(), "version": self.autobumped_version}
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@property
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def is_dirty(self) -> Optional[bool]:
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"""Determine if the rule has changed since its version was locked."""
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from .packaging import load_versions
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rules_versions = load_versions()
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if self.id in rules_versions:
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version_info = rules_versions[self.id]
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existing_sha256: str = version_info['sha256']
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return existing_sha256 != self.sha256()
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@property
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def latest_version(self) -> Optional[int]:
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"""Retrieve the latest known version of the rule."""
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from .packaging import load_versions
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rules_versions = load_versions()
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if self.id in rules_versions:
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version_info = rules_versions[self.id]
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version = version_info['version']
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return version
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@property
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def autobumped_version(self) -> Optional[int]:
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"""Retrieve the current version of the rule, accounting for automatic increments."""
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version = self.latest_version
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if version is None:
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return 1
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return version + 1 if self.is_dirty else version
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@validates_schema
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def validate_query(self, value: dict, **kwargs):
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"""Validate queries by calling into the validator for the relevant method."""
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data: AnyRuleData = value["data"]
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metadata: RuleMeta = value["metadata"]
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return data.validate_query(metadata)
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def to_dict(self, strip_none_values=True) -> dict:
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dict_obj = super(TOMLRuleContents, self).to_dict(strip_none_values=strip_none_values)
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return nested_normalize(dict_obj)
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def flattened_dict(self) -> dict:
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flattened = dict()
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flattened.update(self.data.to_dict())
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flattened.update(self.metadata.to_dict())
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return flattened
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@staticmethod
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def _post_dict_transform(obj: dict) -> dict:
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"""Transform the converted API in place before sending to Kibana."""
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# cleanup the whitespace in the rule
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obj = nested_normalize(obj)
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# fill in threat.technique so it's never missing
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for threat_entry in obj.get("threat", []):
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threat_entry.setdefault("technique", [])
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return obj
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def to_api_format(self, include_version=True) -> dict:
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"""Convert the TOML rule to the API format."""
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converted = self.data.to_dict()
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if include_version:
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converted["version"] = self.autobumped_version
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converted = self._post_dict_transform(converted)
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return converted
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@cached
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def sha256(self, include_version=False) -> str:
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# get the hash of the API dict without the version by default, otherwise it'll always be dirty.
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hashable_contents = self.to_api_format(include_version=include_version)
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return utils.dict_hash(hashable_contents)
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@dataclass
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class TOMLRule:
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contents: TOMLRuleContents = field(hash=True)
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path: Optional[Path] = None
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gh_pr: Any = field(hash=False, compare=False, default=None, repr=None)
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@property
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def id(self):
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return self.contents.id
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@property
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def name(self):
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return self.contents.data.name
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def get_asset(self) -> dict:
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"""Generate the relevant fleet compatible asset."""
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return {"id": self.id, "attributes": self.contents.to_api_format(), "type": definitions.SAVED_OBJECT_TYPE}
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def save_toml(self):
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assert self.path is not None, f"Can't save rule {self.name} (self.id) without a path"
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converted = self.contents.to_dict()
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toml_write(converted, str(self.path.absolute()))
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def save_json(self, path: Path, include_version: bool = True):
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path = path.with_suffix('.json')
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with open(str(path.absolute()), 'w', newline='\n') as f:
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json.dump(self.contents.to_api_format(include_version=include_version), f, sort_keys=True, indent=2)
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f.write('\n')
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def downgrade_contents_from_rule(rule: TOMLRule, target_version: str) -> dict:
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"""Generate the downgraded contents from a rule."""
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payload = rule.contents.to_api_format()
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meta = payload.setdefault("meta", {})
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meta["original"] = dict(id=rule.id, **rule.contents.metadata.to_dict())
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payload["rule_id"] = str(uuid4())
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payload = downgrade(payload, target_version)
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return payload
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# avoid a circular import
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from .rule_validators import KQLValidator, EQLValidator # noqa: E402
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