Detection rules › Sublime MQL
Service abuse: FlipHTML5 with attachment deception and credential theft language
Detects messages that reference attachments without including any, contain links to FlipHTML5 services, and exhibit high-confidence credential theft language patterns.
Threat classification
Sublime's own taxonomy (not MITRE ATT&CK).
| Category | Values |
|---|---|
| Attack types | Credential Phishing |
| Tactics and techniques | Social engineering, Free file host, Evasion |
Event coverage
| Message attribute |
|---|
| body |
| body.current_thread |
| body.links (collection) |
| type |
Rule body MQL
type.inbound
// messages contain wording to "see attached" but contains no attachments
and (
regex.icontains(body.current_thread.text,
"attached|see.*attached|find.*attached|please{0,10}attached"
)
and length(attachments) == 0
)
// and the link goes to fliphtml5 and contains suspect "click me" language
and any(body.links, .href_url.domain.root_domain == "fliphtml5.com")
// and we have confidence its cred theft
and any(ml.nlu_classifier(body.current_thread.text).intents,
.name == "cred_theft" and .confidence != "low"
)
Detection logic
Scope: inbound message.
Detects messages that reference attachments without including any, contain links to FlipHTML5 services, and exhibit high-confidence credential theft language patterns.
- inbound message
all of:
- body.current_thread.text matches 'attached|see.*attached|find.*attached|please{0,10}attached'
- length(attachments) is 0
any of
body.linkswhere:- .href_url.domain.root_domain is 'fliphtml5.com'
any of
ml.nlu_classifier(body.current_thread.text).intentswhere all hold:- .name is 'cred_theft'
- .confidence is not 'low'
Inspects: body.current_thread.text, body.links, body.links[].href_url.domain.root_domain, type.inbound. Sensors: ml.nlu_classifier, regex.icontains.
Indicators matched (3)
| Field | Match | Value |
|---|---|---|
regex.icontains | regex | attached|see.*attached|find.*attached|please{0,10}attached |
body.links[].href_url.domain.root_domain | equals | fliphtml5.com |
ml.nlu_classifier(body.current_thread.text).intents[].name | equals | cred_theft |