|
| 1 | +import dataclasses |
| 2 | +import logging |
| 3 | +from typing import List |
| 4 | +from typing import Optional |
| 5 | +from uuid import uuid4 |
| 6 | + |
| 7 | +from packageurl import PackageURL |
| 8 | +from django.db.models.query import QuerySet |
| 9 | + |
| 10 | +from vulnerabilities.data_source import Reference |
| 11 | +from vulnerabilities.data_source import AdvisoryData |
| 12 | + |
| 13 | +logger = logging.getLogger(__name__) |
| 14 | + |
| 15 | +MAX_CONFIDENCE = 100 |
| 16 | + |
| 17 | + |
| 18 | +@dataclasses.dataclass(order=True) |
| 19 | +class Inference: |
| 20 | + """ |
| 21 | + This data class expresses the contract between data improvers and the improve runner. |
| 22 | +
|
| 23 | + Only inferences with highest confidence for one vulnerability <-> package |
| 24 | + relationship is to be inserted into the database |
| 25 | + """ |
| 26 | + |
| 27 | + vulnerability_id: str = None |
| 28 | + aliases: List[str] = dataclasses.field(default_factory=list) |
| 29 | + confidence: int = MAX_CONFIDENCE |
| 30 | + summary: Optional[str] = None |
| 31 | + affected_purls: List[PackageURL] = dataclasses.field(default_factory=list) |
| 32 | + fixed_purl: PackageURL = dataclasses.field(default_factory=list) |
| 33 | + references: List[Reference] = dataclasses.field(default_factory=list) |
| 34 | + |
| 35 | + def __post_init__(self): |
| 36 | + if self.confidence > MAX_CONFIDENCE or self.confidence < 0: |
| 37 | + raise ValueError |
| 38 | + |
| 39 | + assert ( |
| 40 | + self.vulnerability_id |
| 41 | + or self.aliases |
| 42 | + or self.summary |
| 43 | + or self.affected_purls |
| 44 | + or self.fixed_purl |
| 45 | + or self.references |
| 46 | + ) |
| 47 | + |
| 48 | + versionless_purls = [] |
| 49 | + for purl in self.affected_purls + [self.fixed_purl]: |
| 50 | + if not purl.version: |
| 51 | + versionless_purls.append(purl) |
| 52 | + |
| 53 | + assert ( |
| 54 | + not versionless_purls |
| 55 | + ), f"Version-less purls are not supported in an Inference: {versionless_purls}" |
| 56 | + |
| 57 | + @classmethod |
| 58 | + def from_advisory_data(cls, advisory_data, confidence, affected_purls, fixed_purl): |
| 59 | + """ |
| 60 | + Return an Inference object while keeping the same values as of advisory_data |
| 61 | + for vulnerability_id, summary and references |
| 62 | + """ |
| 63 | + return cls( |
| 64 | + aliases=advisory_data.aliases, |
| 65 | + confidence=confidence, |
| 66 | + summary=advisory_data.summary, |
| 67 | + affected_purls=affected_purls, |
| 68 | + fixed_purl=fixed_purl, |
| 69 | + references=advisory_data.references, |
| 70 | + ) |
| 71 | + |
| 72 | + |
| 73 | +class Improver: |
| 74 | + """ |
| 75 | + Improvers are responsible to improve the already imported data by a datasource. |
| 76 | + Inferences regarding the data could be generated based on multiple factors. |
| 77 | + """ |
| 78 | + |
| 79 | + @property |
| 80 | + def interesting_advisories(self) -> QuerySet: |
| 81 | + """ |
| 82 | + Return QuerySet for the advisories this improver is interested in |
| 83 | + """ |
| 84 | + raise NotImplementedError |
| 85 | + |
| 86 | + def get_inferences(self, advisory_data: AdvisoryData) -> List[Inference]: |
| 87 | + """ |
| 88 | + Generate and return Inferences for the given advisory data |
| 89 | + """ |
| 90 | + raise NotImplementedError |
| 91 | + |
| 92 | + @classmethod |
| 93 | + def qualified_name(cls): |
| 94 | + """ |
| 95 | + Fully qualified name prefixed with the module name of the improver |
| 96 | + used in logging. |
| 97 | + """ |
| 98 | + return f"{cls.__module__}.{cls.__qualname__}" |
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