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sublimemediumRule
Attachment: PDF with credential theft language and invalid reply-to domain
Detects PDF attachments containing high-confidence credential theft language that references the recipient's email address, combined with an invalid reply-to domain header.
Detection Query
type.inbound
and length(recipients.to) == 1
and recipients.to[0].email.domain.valid
and any(headers.reply_to, .email.email == "")
and any(attachments,
.file_type == 'pdf'
and any(file.explode(.),
any(ml.nlu_classifier(.scan.strings.raw).intents,
.name == 'cred_theft' and .confidence == 'high'
)
and strings.icontains(.scan.strings.raw,
recipients.to[0].email.email
)
)
)
// negate highly trusted sender domains unless they fail DMARC authentication or DMARC is missing
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
email
Raw Content
name: "Attachment: PDF with credential theft language and invalid reply-to domain"
description: "Detects PDF attachments containing high-confidence credential theft language that references the recipient's email address, combined with an invalid reply-to domain header."
type: "rule"
severity: "medium"
source: |
type.inbound
and length(recipients.to) == 1
and recipients.to[0].email.domain.valid
and any(headers.reply_to, .email.email == "")
and any(attachments,
.file_type == 'pdf'
and any(file.explode(.),
any(ml.nlu_classifier(.scan.strings.raw).intents,
.name == 'cred_theft' and .confidence == 'high'
)
and strings.icontains(.scan.strings.raw,
recipients.to[0].email.email
)
)
)
// negate highly trusted sender domains unless they fail DMARC authentication or DMARC is missing
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:
- "PDF"
- "Social engineering"
- "Spoofing"
detection_methods:
- "File analysis"
- "Header analysis"
- "Natural Language Understanding"
- "Content analysis"
id: "52e54b5d-940f-5b54-b175-0a55f2a44fa2"