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sublimemediumRule

Attachment: PDF with Microsoft Purview message impersonation

Detects PDF attachments containing text that impersonates Microsoft Purview secure message notifications, potentially used to trick users into believing they have received legitimate secure communications from Microsoft services.

MITRE ATT&CK

initial-access

Detection Query

type.inbound
and any(filter(attachments, .file_extension == 'pdf'),
        any(ml.nlu_classifier(beta.ocr(.).text).topics,
            .name == 'Secure Message' and .confidence == 'high'
        )
        and strings.icontains(beta.ocr(.).text, "Microsoft Purview Message")
)
// negate highly trusted sender domains unless they fail DMARC authentication
and (
  (
    sender.email.domain.root_domain in $high_trust_sender_root_domains
    and not headers.auth_summary.dmarc.pass
  )
  or sender.email.domain.root_domain not in $high_trust_sender_root_domains
)

Data Sources

Email MessagesEmail HeadersEmail Attachments

Platforms

email
Raw Content
name: "Attachment: PDF with Microsoft Purview message impersonation"
description: "Detects PDF attachments containing text that impersonates Microsoft Purview secure message notifications, potentially used to trick users into believing they have received legitimate secure communications from Microsoft services."
type: "rule"
severity: "medium"
source: |
  type.inbound
  and any(filter(attachments, .file_extension == 'pdf'),
          any(ml.nlu_classifier(beta.ocr(.).text).topics,
              .name == 'Secure Message' and .confidence == 'high'
          )
          and strings.icontains(beta.ocr(.).text, "Microsoft Purview Message")
  )
  // negate highly trusted sender domains unless they fail DMARC authentication
  and (
    (
      sender.email.domain.root_domain in $high_trust_sender_root_domains
      and not headers.auth_summary.dmarc.pass
    )
    or sender.email.domain.root_domain not in $high_trust_sender_root_domains
  )
attack_types:
  - "Credential Phishing"
tactics_and_techniques:
  - "Impersonation: Brand"
  - "PDF"
  - "Social engineering"
detection_methods:
  - "File analysis"
  - "Natural Language Understanding"
  - "Content analysis"
id: "571d4964-dc44-56eb-bff4-11068b1cd119"