Brand impersonation: Adobe with suspicious language and link

name: "Brand impersonation: Adobe with suspicious language and link"
description: "Email contains an Adobe logo, at least one link, and suspicious link language from a new sender."
type: "rule"
severity: "high"
source: |
  type.inbound
  // All attachments are images or 0 attachments
  and (
    (
      length(attachments) > 0
      and all(attachments, .file_type in $file_types_images)
    )
    or length(attachments) == 0
  )
  and length(body.links) > 0
  and (
    any(ml.logo_detect(file.message_screenshot()).brands,
        .name == "Adobe" and .confidence in ("high")
    )
    or (
      strings.icontains(body.current_thread.text, "adobe")
      and not strings.icontains(body.current_thread.text, "adobe marketplace")
      and not strings.icontains(body.current_thread.text, "adobe analytics")
      and (
        // Leverage topic analysis to pick up on themes
        (
          length(ml.nlu_classifier(body.current_thread.text).topics) == 1
          and all(ml.nlu_classifier(body.current_thread.text).topics,
                  .name == "File Sharing and Cloud Services"
                  and .confidence != "low"
          )
        )
        // Key phrases if topic anlayis doesn't match
        or strings.icontains(body.current_thread.text, 'review the document')
        or strings.icontains(body.current_thread.text, 'access file')
        or strings.icontains(body.current_thread.text, 'pending document')
        or any(body.links, strings.ilike(.display_text, 'review and sign'))
      )
      and length(body.current_thread.text) < 2000
    )
  )
  and (
    (
      //
      // 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
      //
      strings.ilike(beta.ocr(file.message_screenshot()).text,
                    "*review*",
                    "*sign*",
                    "*view*",
                    "open",
                    "*completed document*",
                    "*open agreement*",
                    "*open document*"
      )
      and not strings.ilike(beta.ocr(file.message_screenshot()).text,
                            "*view this email*",
                            "*view*browser*",
                            "*view online*",
                            "*business review*"
      )
    )
    or any(body.links,
           strings.ilike(.display_text,
                         "*review*",
                         "*sign*",
                         "*view*",
                         "open",
                         "*completed document*",
                         "*open agreement*",
                         "*open document*",
                         "*continue*"
           )
           and not strings.ilike(.display_text,
                                 "*view this email*",
                                 "*view*browser*",
                                 "*view online*"
           )
    )
  )
  // Negate replies & forwards
  and (
    (
      (
        length(headers.references) > 0
        or not any(headers.hops,
                   any(.fields, strings.ilike(.name, "In-Reply-To"))
        )
      )
      and not (
        (
          strings.istarts_with(subject.subject, "RE:")
          or strings.istarts_with(subject.subject, "RES:")
          or strings.istarts_with(subject.subject, "R:")
          or strings.istarts_with(subject.subject, "ODG:")
          or strings.istarts_with(subject.subject, "答复:")
          or strings.istarts_with(subject.subject, "AW:")
          or strings.istarts_with(subject.subject, "TR:")
          or strings.istarts_with(subject.subject, "FWD:")
          or regex.imatch(subject.subject,
                          '^\[?/{0,2}(EXT|EXTERNAL)\]?/{0,2}[: ]\s*(RE|FWD?|FW|AW|TR|ODG|答复):.*'
          )
        )
      )
    )
    or length(headers.references) == 0
  )
  // Negate certain common topics
  and not any(ml.nlu_classifier(body.current_thread.text).topics,
              .name in (
                "Advertising and Promotions",
                "Events and Webinars",
                "Professional and Career Development",
                "Newsletters and Digests"
              )
              and .confidence != "low"
  )
  and (
    (
      headers.auth_summary.spf.pass
      and headers.auth_summary.dmarc.pass
      and (
        not profile.by_sender().solicited
        or profile.by_sender().any_messages_malicious_or_spam
        or profile.by_sender_email().days_since.last_contact > 14
      )
      and not profile.by_sender().any_messages_benign
      // Negate affiliates, sub-products & legitimate domains
      and not sender.email.domain.root_domain in (
        "adobe-events.com",
        "frame.io",
        "nudgesecurity.io",
        "adobesignsandbox.com",
        "magento.com",
        "workfront.com"
      )
      // Continued
      and not any(headers.domains, .root_domain == "zohomail.com")
      and not strings.iends_with(headers.message_id, 'omniture.com>')
    )
    or not headers.auth_summary.spf.pass
    or headers.auth_summary.spf.pass is null
    or not headers.auth_summary.dmarc.pass
    or headers.auth_summary.dmarc.pass is null
  )
  // 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:
  - "Credential Phishing"
tactics_and_techniques:
  - "Impersonation: Brand"
  - "Social engineering"
detection_methods:
  - "Computer Vision"
  - "Content analysis"
  - "Header analysis"
  - "Sender analysis"
id: "32cc8bf1-f4d7-549f-a970-eade24b7c6ae"