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

T1566 T1566.001 T1566.002 T1598 T1598.003

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 not (
  sender.email.domain.root_domain in $high_trust_sender_root_domains
  and coalesce(headers.auth_summary.dmarc.pass, false)
)

Data Sources

Email MessagesEmail HeadersEmail Attachments

Platforms

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 not (
    sender.email.domain.root_domain in $high_trust_sender_root_domains
    and coalesce(headers.auth_summary.dmarc.pass, false)
  )
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"