FindingEvidenceInner.md

FindingEvidenceInner

Properties

Name	Type	Description	Notes
call_chain	List[int]	Addresses along the call chain.
effect	str	Whether the evidence supports or refutes the finding.
evidence_kind	str	Kind of evidence.
kind	str	How the evidence was derived.
strength	str	How directly the evidence bears on the finding.
constants	List[ReportReferencedConstant]	The constants and where they are referenced.
calls	List[ImportedApiCall]
strings	List[ReportStringMatch]	The matched strings.
similarities	List[ReportFunctionSimilarity]	The similar functions.
apis	List[ImportedApi]
summaries	List[ReportCodeSummary]	Summaries of the decompiled code.
interpretations	List[ReportModelInterpretation]	Interpretations of the code by an AI model.
values	List[ReportBytesConstant]	The well-known constants found in the binary.
description	str	What the rule detects.
entropy	float	Shannon entropy of the secret, in bits per byte.
references	List[int]	Address of the secret in the binary's data.
rule_id	str	Identifier of the secret-scanning rule that matched.
secret	str	The secret as it appears in the binary.
secret_kind	str	Category of the secret.
size	int	Size of the secret in bytes.

Example

from revengai.models.finding_evidence_inner import FindingEvidenceInner

# TODO update the JSON string below
json = "{}"
# create an instance of FindingEvidenceInner from a JSON string
finding_evidence_inner_instance = FindingEvidenceInner.from_json(json)
# print the JSON string representation of the object
print(FindingEvidenceInner.to_json())

# convert the object into a dict
finding_evidence_inner_dict = finding_evidence_inner_instance.to_dict()
# create an instance of FindingEvidenceInner from a dict
finding_evidence_inner_from_dict = FindingEvidenceInner.from_dict(finding_evidence_inner_dict)

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