Sublime Security medium experimental mql
BEC/Fraud: Student loan callback phishing
This rule detects phishing emails that attempt to engage the recipient by soliciting a callback under the guise of student loan forgiveness or assistance. The messages often come from free email providers, lack a proper HTML structure, and include suspicious indicators such as phone numbers embedded in the text. These emails typically contain language urging the recipient to respond or take immediate action, leveraging urgency around student loan repayment to entice engagement.
Detection Logic
type.inbound
// there is no HTML body
and body.html.raw is null
// but the current thread contains what's most likely an html tag
// (eg. <>'s' followed by a closing </> )
and regex.contains(body.current_thread.text, '<[^>]+>.*?</[^>]+>')
// and the body mentions student loans
and strings.icontains(body.current_thread.text, "Student Loan")
// sourced from a free mail provider
and sender.email.domain.root_domain in $free_email_providers
// contains a phone number
and (
regex.contains(strings.replace_confusables(body.current_thread.text),
'\+?(\d{1}.)?\(?\d{3}?\)?.\d{3}.?\d{4}'
)
or regex.contains(strings.replace_confusables(body.current_thread.text),
'\+\d{1,3}[ilo0-9]{10}'
)
// +12028001238
or regex.contains(strings.replace_confusables(body.current_thread.text),
'[ilo0-9]{3}\.[ilo0-9]{3}\.[ilo0-9]{4}'
)
// 202.800.1238
or regex.contains(strings.replace_confusables(body.current_thread.text),
'[ilo0-9]{3}-[ilo0-9]{3}-[ilo0-9]{4}'
)
// 202-800-1238
or regex.contains(strings.replace_confusables(body.current_thread.text),
'\([ilo0-9]{3}\)\s[ilo0-9]{3}-[ilo0-9]{4}'
)
// (202) 800-1238
or regex.contains(strings.replace_confusables(body.current_thread.text),
'\([ilo0-9]{3}\)[\s-]+[ilo0-9]{3}[\s-]+[ilo0-9]{4}'
)
// (202)-800-1238
or regex.contains(strings.replace_confusables(body.current_thread.text),
'1 [ilo0-9]{3} [ilo0-9]{3} [ilo0-9]{4}'
) // 8123456789
or regex.contains(strings.replace_confusables(body.current_thread.text),
'8\d{9}'
)
)
// contains a request
and any(ml.nlu_classifier(body.current_thread.text).entities,
.name == "request"
) Field Validations
Loading…
Comments (0)
Loading comments...