Brand impersonation: Aramco

name: "Brand impersonation: Aramco"
description: |
  Impersonation of the petroleum and natural gas company Saudi Aramco.
type: "rule"
severity: "medium"
source: |
  type.inbound
  and length(filter(body.links, .href_url.domain.valid)) < 25
  and (
    strings.ilike(sender.display_name, '*aramco*')
    or strings.ilevenshtein(sender.display_name, 'aramco') <= 1
    or strings.ilike(sender.email.domain.domain, '*aramco*')
    or strings.ilike(subject.subject, '*aramco*')
  )
  and any(ml.nlu_classifier(body.current_thread.text).entities,
          .name == "financial" or .name == "request"
  )
  and (
    (
      any(ml.nlu_classifier(body.current_thread.text).entities,
          strings.ilike(.text, "*aramco*")
      )
      and any(ml.nlu_classifier(body.current_thread.text).entities,
              .name == "urgency"
      )
    )
    or regex.icount(body.current_thread.text, "vendor|supplier") > 3
    or any(headers.reply_to, .email.domain.root_domain in $free_email_providers)
    or network.whois(sender.email.domain).days_old <= 90
    or any(headers.reply_to, network.whois(.email.domain).days_old < 90)
  )
  // Aramco domain negations
  and sender.email.domain.root_domain not in~ (
    'aramco.com',
    'aramcoamericas.com',
    'aramcoventures.com',
    'aramcoworld.com',
    'aramcodigital.com',
    'aramcoeurope.com'
  )
  // negate emails sent by the Aston Martin Aramco F1 Team
  and sender.email.domain.root_domain != "astonmartinf1.com"
  and not strings.icontains(subject.subject, 'Aston Martin')
  and not regex.icontains(subject.subject, 'Formula (?:One|1)', '\bF1\b')
  
  // 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:
  - "Impersonation: Brand"
  - "Lookalike domain"
  - "Social engineering"
detection_methods:
  - "Content analysis"
  - "Header analysis"
  - "HTML analysis"
  - "Natural Language Understanding"
  - "Sender analysis"
id: "96e87699-4007-53c1-a605-ee1cd398839c"