← Back to Explore
sublimehighRule
Headers: Self-sender using Microsoft CompAuth bypass with credential theft content
Detects messages sent to self or invalid domains containing credential theft content that bypass Microsoft's CompAuth while failing both SPF and DMARC authentication checks.
Detection Query
type.inbound
// self sender
and length(recipients.to) == 1
and length(recipients.cc) == 0
and length(recipients.bcc) == 0
and (
sender.email.email == recipients.to[0].email.email
or recipients.to[0].email.domain.valid == false
)
// cred theft
and any(ml.nlu_classifier(body.current_thread.text).intents,
.name == "cred_theft" and .confidence != "low"
)
// microsoft compauth pass, but spf and dmarc fail
and any(headers.hops, any(.fields, strings.icontains(.value, 'compauth=pass')))
and not coalesce(headers.auth_summary.dmarc.pass, false)
and not coalesce(headers.auth_summary.spf.pass, false)
Data Sources
Email MessagesEmail HeadersEmail Attachments
Platforms
email
Tags
Attack surface reduction
Raw Content
name: "Headers: Self-sender using Microsoft CompAuth bypass with credential theft content"
description: "Detects messages sent to self or invalid domains containing credential theft content that bypass Microsoft's CompAuth while failing both SPF and DMARC authentication checks."
type: "rule"
severity: "high"
source: |
type.inbound
// self sender
and length(recipients.to) == 1
and length(recipients.cc) == 0
and length(recipients.bcc) == 0
and (
sender.email.email == recipients.to[0].email.email
or recipients.to[0].email.domain.valid == false
)
// cred theft
and any(ml.nlu_classifier(body.current_thread.text).intents,
.name == "cred_theft" and .confidence != "low"
)
// microsoft compauth pass, but spf and dmarc fail
and any(headers.hops, any(.fields, strings.icontains(.value, 'compauth=pass')))
and not coalesce(headers.auth_summary.dmarc.pass, false)
and not coalesce(headers.auth_summary.spf.pass, false)
tags:
- "Attack surface reduction"
attack_types:
- "Credential Phishing"
tactics_and_techniques:
- "Spoofing"
- "Evasion"
detection_methods:
- "Natural Language Understanding"
- "Header analysis"
- "Sender analysis"
id: "549c4e66-ec29-50f5-aff7-4a85fa7318da"