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Detection rules › Sublime MQL

Brand impersonation: Square

Severity
medium
Type
rule
Source
github.com/sublime-security/sublime-rules

Impersonation of Square, typically containing security-related language, secure message notifications, or credential theft indicators from unauthorized senders.

Threat classification

Sublime's own taxonomy (not MITRE ATT&CK).

CategoryValues
Attack typesCredential Phishing
Tactics and techniquesImpersonation: Brand, Social engineering

Event coverage

Message attribute
body.current_thread
headers.auth_summary
sender
sender.email
type

Rule body MQL

type.inbound
and (
  // levenshtein distance similar to Square
  (
    strings.ilevenshtein(strings.replace_confusables(sender.display_name),
                         'square'
    ) <= 1
    and sender.display_name not in ("SquareX")
  )
  or any(ml.logo_detect(file.message_screenshot()).brands,
         .name == "Square" and .confidence == "high"
  )
)
and (
  (
    any(ml.nlu_classifier(body.current_thread.text).topics,
        .name in (
          "Security and Authentication",
          "Secure Message",
          "Reminders and Notifications"
        )
        and .confidence in ("medium", "high")
    )
    and not any(ml.nlu_classifier(body.current_thread.text).topics,
                .name in ("Newsletters and Digests", "Entertainment and Sports")
                and .confidence in ("medium", "high")
    )
  )
  or (
    any(ml.nlu_classifier(beta.ocr(file.message_screenshot()).text).topics,
        .name in (
          "Security and Authentication",
          "Secure Message",
          "Reminders and Notifications"
        )
        and .confidence in ("medium", "high")
        and beta.ocr(file.message_screenshot()).text != ""
    )
    and not any(ml.nlu_classifier(beta.ocr(file.message_screenshot()).text).topics,
                .name in ("Newsletters and Digests", "Entertainment and Sports")
                and .confidence in ("medium", "high")
    )
  )
  or any(ml.nlu_classifier(body.current_thread.text).intents,
         .name == "cred_theft" and .confidence == "high"
  )
  or any(ml.nlu_classifier(beta.ocr(file.message_screenshot()).text).intents,
         .name == "cred_theft" and .confidence == "high"
  )
)

// and the sender is not in org_domains or from Square domains and passes auth
and not (
  sender.email.domain.root_domain in $org_domains
  or (
    sender.email.domain.root_domain in (
      "square.com",
      "squareup.com",
      "block.com"
    )
    and headers.auth_summary.dmarc.pass
  )
)
// and the sender is not from high trust sender root domains
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
)
and (
  not profile.by_sender().solicited
  or not headers.auth_summary.dmarc.pass
  or not headers.auth_summary.spf.pass
)

Detection logic

Scope: inbound message.

Impersonation of Square, typically containing security-related language, secure message notifications, or credential theft indicators from unauthorized senders.

  1. inbound message
  2. any of:
    • all of:
      • strings.replace_confusables(sender.display_name) is similar to 'square'
      • sender.display_name not in ('SquareX')
    • any of ml.logo_detect(file.message_screenshot()).brands where all hold:
      • .name is 'Square'
      • .confidence is 'high'
  3. any of:
    • all of:
      • any of ml.nlu_classifier(body.current_thread.text).topics where all hold:
        • .name in ('Security and Authentication', 'Secure Message', 'Reminders and Notifications')
        • .confidence in ('medium', 'high')
      • not:
        • any of ml.nlu_classifier(body.current_thread.text).topics where all hold:
          • .name in ('Newsletters and Digests', 'Entertainment and Sports')
          • .confidence in ('medium', 'high')
    • all of:
      • any of ml.nlu_classifier(beta.ocr(file.message_screenshot()).text).topics where all hold:
        • .name in ('Security and Authentication', 'Secure Message', 'Reminders and Notifications')
        • .confidence in ('medium', 'high')
        • beta.ocr(file.message_screenshot()).text is not ''
      • not:
        • any of ml.nlu_classifier(beta.ocr(file.message_screenshot()).text).topics where all hold:
          • .name in ('Newsletters and Digests', 'Entertainment and Sports')
          • .confidence in ('medium', 'high')
    • any of ml.nlu_classifier(body.current_thread.text).intents where all hold:
      • .name is 'cred_theft'
      • .confidence is 'high'
    • any of ml.nlu_classifier(beta.ocr(file.message_screenshot()).text).intents where all hold:
      • .name is 'cred_theft'
      • .confidence is 'high'
  4. none of:
    • sender.email.domain.root_domain in $org_domains
    • all of:
      • sender.email.domain.root_domain in ('square.com', 'squareup.com', 'block.com')
      • headers.auth_summary.dmarc.pass
  5. any of:
    • all of:
      • sender.email.domain.root_domain in $high_trust_sender_root_domains
      • not:
        • headers.auth_summary.dmarc.pass
    • sender.email.domain.root_domain not in $high_trust_sender_root_domains
  6. any of:
    • not:
      • profile.by_sender().solicited
    • not:
      • headers.auth_summary.dmarc.pass
    • not:
      • headers.auth_summary.spf.pass

Inspects: body.current_thread.text, headers.auth_summary.dmarc.pass, headers.auth_summary.spf.pass, sender.display_name, sender.email.domain.root_domain, type.inbound. Sensors: beta.ocr, file.message_screenshot, ml.logo_detect, ml.nlu_classifier, profile.by_sender, strings.ilevenshtein, strings.replace_confusables. Reference lists: $high_trust_sender_root_domains, $org_domains.

Indicators matched (25)

FieldMatchValue
strings.ilevenshteinfuzzysquare
sender.display_namememberSquareX
ml.logo_detect(file.message_screenshot()).brands[].nameequalsSquare
ml.logo_detect(file.message_screenshot()).brands[].confidenceequalshigh
ml.nlu_classifier(body.current_thread.text).topics[].namememberSecurity and Authentication
ml.nlu_classifier(body.current_thread.text).topics[].namememberSecure Message
ml.nlu_classifier(body.current_thread.text).topics[].namememberReminders and Notifications
ml.nlu_classifier(body.current_thread.text).topics[].confidencemembermedium
ml.nlu_classifier(body.current_thread.text).topics[].confidencememberhigh
ml.nlu_classifier(body.current_thread.text).topics[].namememberNewsletters and Digests
ml.nlu_classifier(body.current_thread.text).topics[].namememberEntertainment and Sports
ml.nlu_classifier(beta.ocr(file.message_screenshot()).text).topics[].namememberSecurity and Authentication
13 more
ml.nlu_classifier(beta.ocr(file.message_screenshot()).text).topics[].namememberSecure Message
ml.nlu_classifier(beta.ocr(file.message_screenshot()).text).topics[].namememberReminders and Notifications
ml.nlu_classifier(beta.ocr(file.message_screenshot()).text).topics[].confidencemembermedium
ml.nlu_classifier(beta.ocr(file.message_screenshot()).text).topics[].confidencememberhigh
ml.nlu_classifier(beta.ocr(file.message_screenshot()).text).topics[].namememberNewsletters and Digests
ml.nlu_classifier(beta.ocr(file.message_screenshot()).text).topics[].namememberEntertainment and Sports
ml.nlu_classifier(body.current_thread.text).intents[].nameequalscred_theft
ml.nlu_classifier(body.current_thread.text).intents[].confidenceequalshigh
ml.nlu_classifier(beta.ocr(file.message_screenshot()).text).intents[].nameequalscred_theft
ml.nlu_classifier(beta.ocr(file.message_screenshot()).text).intents[].confidenceequalshigh
sender.email.domain.root_domainmembersquare.com
sender.email.domain.root_domainmembersquareup.com
sender.email.domain.root_domainmemberblock.com