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sublimemediumRule

Brand impersonation: United Healthcare

Detects messages impersonating United Healthcare (UHC) by analyzing display names that contain variations of 'United Healthcare' or 'UHC', including those with character substitutions. The rule excludes legitimate messages from verified UHC domains that pass DMARC authentication and handles high-trust sender domains appropriately.

MITRE ATT&CK

initial-access

Detection Query

type.inbound
and (
  // display name contains UHC
  (
    strings.ilike(strings.replace_confusables(sender.display_name),
                  '*united healthcare*'
    )
    or strings.ilike(strings.replace_confusables(sender.display_name), 'UHC*')
    or regex.icontains(sender.display_name, 'united ?health ?care')
  )
  // levenshtein distance similar to UHC
  or strings.ilevenshtein(strings.replace_confusables(sender.display_name),
                          'united healthcare'
  ) <= 1
  or (
    regex.icontains(body.current_thread.text, 'united ?health ?care')
    and (
      regex.icontains(body.current_thread.text, 'quick .{0,10}survey')
      // Free benefits/items lure commonly used in UHC impersonation
      or regex.icontains(body.current_thread.text,
                         'dental (benefits?|coverage).{0,50}(free|eligible|no.{0,10}cost)'
      )
      or regex.icontains(body.current_thread.text,
                         'free.{0,30}(toothbrush|dental|benefit)'
      )
    )
  )
  // Brand name in sender local part from non-UHC domain
  or (
    strings.icontains(sender.email.local_part, "unitedhealthcare")
    and sender.email.domain.root_domain not in (
      "uhc.com",
      "unitedhealthcare.com",
      "uhcmedicaresolutions.com",
      "unitedhealthcareupdate.com",
      "yourhealth-wellnessteam.com",
      "uhc-customer.com",
      "leavesource.com"
    )
  )
)
// and the sender is not in org_domains or from UHC domains and passes auth
and not (
  sender.email.domain.root_domain in $org_domains
  or (
    (
      sender.email.domain.root_domain in (
        "uhc.com",
        "unitedhealthcare.com",
        "uhcmedicaresolutions.com",
        "unitedhealthcareupdate.com",
        "yourhealth-wellnessteam.com",
        "uhc-customer.com",
        "leavesource.com"
      )
      or sender.display_name in (
        "UHCOM Faculty Affairs",
        "UHC Construction Services"
      )
    )
    and headers.auth_summary.dmarc.pass
  )
)
// negate UHC job related posting
and not any(ml.nlu_classifier(body.current_thread.text).topics,
            .name == "Professional and Career Development"
            and .confidence == "high"
)
// 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
)

Data Sources

Email MessagesEmail HeadersEmail Attachments

Platforms

email
Raw Content
name: "Brand impersonation: United Healthcare"
description: "Detects messages impersonating United Healthcare (UHC) by analyzing display names that contain variations of 'United Healthcare' or 'UHC', including those with character substitutions. The rule excludes legitimate messages from verified UHC domains that pass DMARC authentication and handles high-trust sender domains appropriately."
type: "rule"
severity: "medium"
source: |
  type.inbound
  and (
    // display name contains UHC
    (
      strings.ilike(strings.replace_confusables(sender.display_name),
                    '*united healthcare*'
      )
      or strings.ilike(strings.replace_confusables(sender.display_name), 'UHC*')
      or regex.icontains(sender.display_name, 'united ?health ?care')
    )
    // levenshtein distance similar to UHC
    or strings.ilevenshtein(strings.replace_confusables(sender.display_name),
                            'united healthcare'
    ) <= 1
    or (
      regex.icontains(body.current_thread.text, 'united ?health ?care')
      and (
        regex.icontains(body.current_thread.text, 'quick .{0,10}survey')
        // Free benefits/items lure commonly used in UHC impersonation
        or regex.icontains(body.current_thread.text,
                           'dental (benefits?|coverage).{0,50}(free|eligible|no.{0,10}cost)'
        )
        or regex.icontains(body.current_thread.text,
                           'free.{0,30}(toothbrush|dental|benefit)'
        )
      )
    )
    // Brand name in sender local part from non-UHC domain
    or (
      strings.icontains(sender.email.local_part, "unitedhealthcare")
      and sender.email.domain.root_domain not in (
        "uhc.com",
        "unitedhealthcare.com",
        "uhcmedicaresolutions.com",
        "unitedhealthcareupdate.com",
        "yourhealth-wellnessteam.com",
        "uhc-customer.com",
        "leavesource.com"
      )
    )
  )
  // and the sender is not in org_domains or from UHC domains and passes auth
  and not (
    sender.email.domain.root_domain in $org_domains
    or (
      (
        sender.email.domain.root_domain in (
          "uhc.com",
          "unitedhealthcare.com",
          "uhcmedicaresolutions.com",
          "unitedhealthcareupdate.com",
          "yourhealth-wellnessteam.com",
          "uhc-customer.com",
          "leavesource.com"
        )
        or sender.display_name in (
          "UHCOM Faculty Affairs",
          "UHC Construction Services"
        )
      )
      and headers.auth_summary.dmarc.pass
    )
  )
  // negate UHC job related posting
  and not any(ml.nlu_classifier(body.current_thread.text).topics,
              .name == "Professional and Career Development"
              and .confidence == "high"
  )
  // 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
  )
attack_types:
  - "Credential Phishing"
tactics_and_techniques:
  - "Impersonation: Brand"
  - "Social engineering"
detection_methods:
  - "Header analysis"
  - "Sender analysis"
id: "f8dfff1a-8f3e-5301-b2d7-b68a78ad34db"