Detection rules › Sublime MQL

BEC: Employee impersonation with subject manipulation

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

Subject matches the display name of someone in your organization, and the body resembles a BEC attack.

Threat classification

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

CategoryValues
Attack typesBEC/Fraud
Tactics and techniquesImpersonation: Employee, Social engineering

Event coverage

Rule body MQL

type.inbound

// attacks have been observed where the impersonation is
// in the subject, and the display name contains what a
// subject typically would contain. this is an attempt to
// bypass impersonation detections
and subject.base in~ $org_display_names
and any(ml.nlu_classifier(body.current_thread.text).intents,
        .name == "bec" and .confidence in ("medium", "high")
)
and (
  not profile.by_sender().solicited
  or (
    profile.by_sender().any_messages_malicious_or_spam
    and not profile.by_sender().any_messages_benign
  )
)

Detection logic

Scope: inbound message.

Subject matches the display name of someone in your organization, and the body resembles a BEC attack.

  1. inbound message
  2. subject.base in $org_display_names
  3. any of ml.nlu_classifier(body.current_thread.text).intents where all hold:
    • .name is 'bec'
    • .confidence in ('medium', 'high')
  4. any of:
    • not:
      • profile.by_sender().solicited
    • all of:
      • profile.by_sender().any_messages_malicious_or_spam
      • not:
        • profile.by_sender().any_messages_benign

Inspects: body.current_thread.text, subject.base, type.inbound. Sensors: ml.nlu_classifier, profile.by_sender. Reference lists: $org_display_names.

Indicators matched (3)

FieldMatchValue
ml.nlu_classifier(body.current_thread.text).intents[].nameequalsbec
ml.nlu_classifier(body.current_thread.text).intents[].confidencemembermedium
ml.nlu_classifier(body.current_thread.text).intents[].confidencememberhigh