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sublimemediumRule
Callback phishing via calendar invite
Detects calendar invites containing callback phishing language in the DESCRIPTION of the invite.
Detection Query
type.inbound
and length(attachments) > 0
and all(attachments, .content_type in ("text/calendar", "application/ics"))
and any(attachments,
// extract the calendar invite description and use NLU against it
any(file.explode(.),
any(.scan.ics.calendars,
any(.components,
any(ml.nlu_classifier(.description).intents,
.name == "callback_scam"
)
)
)
)
)
and (
not profile.by_sender_email().solicited
and not profile.by_sender_email().any_messages_benign
)
// 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
)
Data Sources
Email MessagesEmail HeadersEmail Attachments
Platforms
email
Raw Content
name: "Callback phishing via calendar invite"
description: "Detects calendar invites containing callback phishing language in the DESCRIPTION of the invite."
type: "rule"
severity: "medium"
source: |
type.inbound
and length(attachments) > 0
and all(attachments, .content_type in ("text/calendar", "application/ics"))
and any(attachments,
// extract the calendar invite description and use NLU against it
any(file.explode(.),
any(.scan.ics.calendars,
any(.components,
any(ml.nlu_classifier(.description).intents,
.name == "callback_scam"
)
)
)
)
)
and (
not profile.by_sender_email().solicited
and not profile.by_sender_email().any_messages_benign
)
// 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
)
attack_types:
- "Callback Phishing"
tactics_and_techniques:
- "Social engineering"
- "Evasion"
detection_methods:
- "File analysis"
- "Header analysis"
- "Natural Language Understanding"
- "Sender analysis"
id: "95c84360-d5a5-5396-b9ce-c61016cb178f"