• Sublime Core Feed
Low Severity

Honorific greeting BEC attempt with sender and reply-to mismatch

Labels

BEC/Fraud
Free email provider
Social engineering
Content analysis
Header analysis
Natural Language Understanding
Sender analysis

Description

Detects generic BEC/Fraud scams by analyzing text within the email body from mismatched senders with other suspicious indicators.

References

No references.

Sublime Security
Created Nov 22nd, 2023 • Last updated Jul 16th, 2025
Feed Source
Sublime Core Feed
Source
GitHub
type.inbound
// mismatched sender (From) and Reply-to + freemail
and any(headers.reply_to,
        length(headers.reply_to) > 0
        and all(headers.reply_to,
                .email.domain.root_domain != sender.email.domain.root_domain
                and .email.domain.root_domain in $free_email_providers
        )
)

// use of honorific
and regex.icontains(body.current_thread.text,
                    '(?:Mr|Mrs|Ms|Miss|Dr|Prof|Sir|Lady|Rev)\.?[ \t]+'
)

// BEC-themed language
and (
  any(ml.nlu_classifier(body.current_thread.text).intents, .name in ("bec", "advance_fee"))
  and any(ml.nlu_classifier(body.current_thread.text).entities,
          .name == "request"
  )
)

// 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
)
and (
  (
    profile.by_sender().prevalence in ("new", "outlier")
    and not profile.by_sender().solicited
  )
  or (
    profile.by_sender().any_messages_malicious_or_spam
    and not profile.by_sender().any_messages_benign
  )
)
and not profile.by_sender().any_messages_benign
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