Medium Severity
COVID-19 themed fraud with sender and reply-to mismatch or compensation award
Description
Detects potential COVID-19 themed BEC/Fraud scams by analyzing text within the email body for mentions of COVID-19 assistance, compensation, or awards from mismatched senders and other suspicious language.
References
No references.
Sublime Security
Created Nov 22nd, 2023 • Last updated Jul 16th, 2025
Feed Source
Sublime Core Feed
Source
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]+',
'Dear Sir'
)
// mention of covid or an international organization
and regex.icontains(body.current_thread.text,
'international (court of justice|monetary fund)',
'united nations',
'western union',
'world bank',
'world health organization',
'interpol',
'treasury',
'\bFEMA\b',
'\bIMF\b'
)
// and mention of covid in subject or body
and (
regex.icontains(subject.subject, 'covid(.{0,5}19)?\b')
or regex.icontains(body.current_thread.text, 'covid(.{0,5}19)?\b')
)
// Check for compensation or award related language
and (
2 of (
any(ml.nlu_classifier(body.current_thread.text).entities, .name == "urgency"),
any(ml.nlu_classifier(body.current_thread.text).entities, .name == "request"),
any(ml.nlu_classifier(body.current_thread.text).entities, .name == "financial")
)
or regex.icontains(subject.subject,
'compensation.{0,20}(award|fund)',
'covid.{0,20}(compensation|award)',
'selected.{0,30}(compensation|award)',
'claim your award',
'reference no')
or regex.icontains(body.current_thread.text,
'compensation.{0,20}(award|fund)',
'covid.{0,20}(compensation|award)',
'selected.{0,30}(compensation|award)',
'claim your award',
'reference no\W\s*[^\s]*cov(?:id)?(?:.{0,5}19)?\b')
)
// 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
Playground
Test against your own EMLs or sample data.