sublimemediumRule

Suspicious newly registered reply-to domain with engaging financial or urgent language

Detects messages from a mismatched newly registered Reply-to domain that contain a financial or urgent request, or a request and an NLU tag with medium to high confidence, from an untrusted sender. This technique is typically observed in Vendor impersonation.

MITRE ATT&CK

T1566.002 T1534 T1656 T1566 T1598

initial-access

Detection Query

type.inbound
and length(body.current_thread.text) < 5000
and any(headers.reply_to,
        // mismatched reply-to and sender domain
        .email.domain.root_domain != sender.email.domain.root_domain
        // newly registered reply-to domain
        and network.whois(.email.domain).days_old <= 30
)
// request is being made
and any(ml.nlu_classifier(body.current_thread.text).entities,
        .name == "request"
)
// there's financial/urgency OR a tag of medium/high confidence
and (
  any(ml.nlu_classifier(body.current_thread.text).entities,
      .name in ("financial", "urgency")
  )
  or any(ml.nlu_classifier(body.current_thread.text).tags,
         .name is not null and .confidence in ("medium", "high")
  )
)
and (
  not profile.by_sender().solicited

  // 
  // This rule makes use of a beta feature and is subject to change without notice
  // using the beta feature in custom rules is not suggested until it has been formally released
  // 
  or not beta.profile.by_reply_to().solicited
  or (
    profile.by_sender().any_messages_malicious_or_spam
    and not profile.by_sender().any_messages_benign
  )
)
// 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

Raw Content

name: "Suspicious newly registered reply-to domain with engaging financial or urgent language"
description: |
  Detects messages from a mismatched newly registered Reply-to domain that contain a financial or urgent request, or a request and 
  an NLU tag with medium to high confidence, from an untrusted sender. This technique is typically observed in Vendor impersonation.
type: "rule"
severity: "medium"
source: |
  type.inbound
  and length(body.current_thread.text) < 5000
  and any(headers.reply_to,
          // mismatched reply-to and sender domain
          .email.domain.root_domain != sender.email.domain.root_domain
          // newly registered reply-to domain
          and network.whois(.email.domain).days_old <= 30
  )
  // request is being made
  and any(ml.nlu_classifier(body.current_thread.text).entities,
          .name == "request"
  )
  // there's financial/urgency OR a tag of medium/high confidence
  and (
    any(ml.nlu_classifier(body.current_thread.text).entities,
        .name in ("financial", "urgency")
    )
    or any(ml.nlu_classifier(body.current_thread.text).tags,
           .name is not null and .confidence in ("medium", "high")
    )
  )
  and (
    not profile.by_sender().solicited
  
    // 
    // This rule makes use of a beta feature and is subject to change without notice
    // using the beta feature in custom rules is not suggested until it has been formally released
    // 
    or not beta.profile.by_reply_to().solicited
    or (
      profile.by_sender().any_messages_malicious_or_spam
      and not profile.by_sender().any_messages_benign
    )
  )
  // 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:
  - "BEC/Fraud"
tactics_and_techniques:
  - "Social engineering"
detection_methods:
  - "Content analysis"
  - "Header analysis"
  - "Natural Language Understanding"
  - "Sender analysis"
  - "URL analysis"
  - "Whois"
id: "db4d9bb3-0eca-5525-9a99-7c1c773b580f"