Brand impersonation: TikTok

  1name: "Brand impersonation: TikTok"
  2description: "Detects messages impersonating TikTok through similar display names or logo detection, combined with security-themed content and authentication failures. Excludes legitimate TikTok communications and trusted senders."
  3type: "rule"
  4severity: "medium"
  5source: |
  6  type.inbound
  7  and (
  8    // TikTok Brand Detection 
  9    (
 10      // display name contains tiktok
 11      strings.ilike(strings.replace_confusables(sender.display_name), '*tiktok*')
 12      // levenshtein distance similar to tiktok
 13      or strings.ilevenshtein(strings.replace_confusables(sender.display_name),
 14                              'tiktok'
 15      ) <= 1
 16      or (
 17        length(ml.logo_detect(file.message_screenshot()).brands) == 1
 18        and any(ml.logo_detect(file.message_screenshot()).brands,
 19                .name == "TikTok" and .confidence == "high"
 20        )
 21      )
 22      // hyphenated sender domain contains tiktok
 23      or strings.iends_with(sender.email.domain.root_domain, "-tiktok.com")
 24    )
 25    // OR TikTok verification language
 26    or (
 27      strings.icontains(body.current_thread.text, "tiktok")
 28      and (
 29        strings.icontains(body.current_thread.text, "verified badge")
 30        or strings.icontains(body.current_thread.text, "verification criteria")
 31        or strings.icontains(body.current_thread.text, "activate badge")
 32        or strings.icontains(body.current_thread.text, "verification complete")
 33        or strings.icontains(body.current_thread.text, "almost verified")
 34        or strings.icontains(body.current_thread.text, "review complete")
 35        or strings.icontains(body.current_thread.text, "verify profile")
 36      )
 37    )
 38  )
 39  and (
 40    // ML Topic Analysis and Credential Theft Detection
 41    any(ml.nlu_classifier(body.current_thread.text).topics,
 42        .name in (
 43          "Security and Authentication",
 44          "Secure Message",
 45          "Reminders and Notifications"
 46        )
 47        and .confidence in ("medium", "high")
 48    )
 49    or any(ml.nlu_classifier(beta.ocr(file.message_screenshot()).text).topics,
 50           .name in (
 51             "Security and Authentication",
 52             "Secure Message",
 53             "Reminders and Notifications"
 54           )
 55           and .confidence in ("medium", "high")
 56           and beta.ocr(file.message_screenshot()).text != ""
 57    )
 58    or any(ml.nlu_classifier(body.current_thread.text).intents,
 59           .name == "cred_theft" and .confidence == "high"
 60    )
 61    or any(ml.nlu_classifier(beta.ocr(file.message_screenshot()).text).intents,
 62           .name == "cred_theft" and .confidence == "high"
 63    )
 64  )
 65  // Not from legitimate TikTok or Google domains with DMARC pass
 66  and not (
 67    sender.email.domain.root_domain in $org_domains
 68    or (
 69      sender.email.domain.root_domain in (
 70        "tiktok.com",
 71        "tiktokglobalshop.com",
 72        "tiktokusds.com",
 73        "bytedance.com",
 74        "tiktokacademy.com",
 75        "webassessor.com" // used for this https://ads.tiktok.com/business/en-US/academy/tiktok-certification
 76      )
 77      and headers.auth_summary.dmarc.pass
 78    )
 79  )
 80  // negate iCloud Private Message Relay
 81  and not (
 82    sender.email.domain.root_domain == "privaterelay.appleid.com"
 83    or any(headers.hops, any(.fields, .name == "X-ICLOUD-HME"))
 84  )
 85  // negate highly trusted sender domains unless they fail DMARC authentication
 86  and (
 87    (
 88      sender.email.domain.root_domain in $high_trust_sender_root_domains
 89      and not headers.auth_summary.dmarc.pass
 90    )
 91    or sender.email.domain.root_domain not in $high_trust_sender_root_domains
 92  )
 93  and not profile.by_sender().solicited  
 94
 95attack_types:
 96  - "Credential Phishing"
 97tactics_and_techniques:
 98  - "Impersonation: Brand"
 99  - "Social engineering"
100detection_methods:
101  - "Computer Vision"
102  - "Content analysis"
103  - "Header analysis"
104  - "Natural Language Understanding"
105  - "Optical Character Recognition"
106  - "Sender analysis"
107id: "aaacc8b7-fbbd-596d-9268-d90b92bdfcd7"