Medium Severity
Advance Fee Fraud (AFF) from freemail provider or suspicious TLD
Description
Advance Fee Fraud (AFF) is a type of BEC/Fraud involving upfront fees for promised future returns, such as lottery scams, inheritance payouts, and investment opportunities. This rule identifies messages from Freemail domains or suspicious TLDS, including those with suspicious reply-to addresses. It utilizes Natural Language Understanding to detect AFF language in their contents.
References
No references.
Sublime Security
Created Oct 17th, 2023 • Last updated Nov 5th, 2024
Feed Source
Sublime Core Feed
Source
type.inbound
and (
sender.email.domain.domain in $free_email_providers
or (
length(headers.reply_to) > 0
and all(headers.reply_to,
(
.email.domain.root_domain in $free_email_providers
or .email.domain.tld in $suspicious_tlds
)
and .email.email != sender.email.email
)
)
or sender.email.domain.tld in $suspicious_tlds
)
and (
any(ml.nlu_classifier(body.current_thread.text).intents,
.name == "advance_fee" and .confidence in ("medium", "high")
)
or (
length(body.current_thread.text) < 200
and regex.icontains(body.current_thread.text,
'(donation|inheritence|\$\d,\d{3}\,\d{3}|lottery)'
)
and (
(
(
length(headers.references) > 0
or not any(headers.hops,
any(.fields, strings.ilike(.name, "In-Reply-To"))
)
)
and not (
(
strings.istarts_with(subject.subject, "RE:")
// out of office auto-reply
or strings.istarts_with(subject.subject, "Automatic reply:")
or strings.istarts_with(subject.subject, "R:")
or strings.istarts_with(subject.subject, "ODG:")
or strings.istarts_with(subject.subject, "答复:")
or strings.istarts_with(subject.subject, "AW:")
or strings.istarts_with(subject.subject, "TR:")
or strings.istarts_with(subject.subject, "FWD:")
or regex.icontains(subject.subject,
'^(\[[^\]]+\]\s?){0,3}(re|fwd?)\s?:'
)
)
)
)
or any(headers.reply_to, .email.email != sender.email.email)
)
)
)
and (
not profile.by_sender().solicited
or profile.by_sender().any_messages_malicious_or_spam
)
and not profile.by_sender().any_false_positives
Playground
Test against your own EMLs or sample data.