Attachment: Fake scan-to-email

Severity: medium
Type: rule
Source: github.com/sublime-security/sublime-rules

Message and attachment resemble an email from a scan-to-email service or device with credential theft language.

Threat classification

Sublime's own taxonomy (not MITRE ATT&CK).

Category	Values
Attack types	Credential Phishing
Tactics and techniques	Free file host, Image as content, PDF, Social engineering

Event coverage

Message attribute
attachments (collection)
body.current_thread
sender.email
type

Rule body MQL

type.inbound
and (
  length(body.current_thread.text) < 1500
  // body length without disclaimer is shorter than 1500 characters
  or (
    any(map(filter(ml.nlu_classifier(body.current_thread.text).entities,
                   .name == "disclaimer"
            ),
            .text
        ),
        (length(body.current_thread.text) - length(.)) < 1500
    )
  )
)
and (
  3 of (
    strings.icontains(body.current_thread.text, "Number of Images:"),
    strings.icontains(body.current_thread.text, "Attachment File Type:"),
    strings.icontains(body.current_thread.text, "Device Model:"),
    strings.icontains(body.current_thread.text, "Device Name:"),
    strings.icontains(body.current_thread.text, "Resolution:"),
    strings.icontains(body.current_thread.text, "File Format:"),
    strings.icontains(body.current_thread.text, "Device Location:")
  )
  or (
    3 of (
      strings.ilike(body.current_thread.text, "*scan date*"),
      strings.ilike(body.current_thread.text, "*was sent from*"),
      strings.ilike(body.current_thread.text, "*of pages*"),
      strings.ilike(body.current_thread.text, "*scanned file*"),
    )
    or any(file.explode(file.message_screenshot()),
           3 of (
             strings.ilike(body.current_thread.text, "*scan date*"),
             strings.ilike(body.current_thread.text, "*was sent from*"),
             strings.ilike(body.current_thread.text, "*of pages*"),
             strings.ilike(body.current_thread.text, "*scanned file*"),
             strings.icontains(body.current_thread.text, "Number of Images:"),
             strings.icontains(body.current_thread.text,
                               "Attachment File Type:"
             ),
             strings.icontains(body.current_thread.text, "Device Name:"),
             strings.icontains(body.current_thread.text, "Device Location:"),
             strings.icontains(body.current_thread.text, "Device Model:")
           )
    )
  )
)
and (
  (
    length(filter(attachments, .file_type in ("pdf"))) == 1
    and any(attachments,
            .file_type == "pdf"
            and (
              any(file.explode(.),
                  (
                    strings.ilike(.scan.ocr.raw,
                                  "*scan date*",
                                  "*was sent from*",
                                  "*of pages*",
                                  "*verif*document*",
                                  "*scanned file*"
                    )
                    or any(ml.nlu_classifier(.scan.ocr.raw).intents,
                           .name == "cred_theft"
                    )
                    or any(ml.logo_detect(..).brands,
                           .name in ("DocuSign", "Microsoft")
                    )
                  )
                  and length(.scan.url.urls) == 1
              )
              // encrypted pdf
              or any(file.explode(.),
                     any(.scan.exiftool.fields, .key == "Encryption")
                     or (
                       .scan.entropy.entropy > 7
                       and any(.scan.strings.strings,
                               strings.icontains(., "/Encrypt")
                       )
                     )
              )
            )
    )
  )
  or length(filter(attachments, .file_type in ("doc", "docx"))) == 1
)
and sender.email.domain.domain not in~ $org_domains
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

Detection logic

Scope: inbound message.

Message and attachment resemble an email from a scan-to-email service or device with credential theft language.

inbound message
any of:
- length(body.current_thread.text) < 1500
- any of map(...) where:
  - length(body.current_thread.text) - length(.) < 1500
any of:
- at least 3 of 7: body.current_thread.text contains any of 7 patterns
  - Number of Images:
  - Attachment File Type:
  - Device Model:
  - Device Name:
  - Resolution:
  - File Format:
  - Device Location:
- any of:
  - at least 3 of 4: body.current_thread.text matches any of 4 patterns
    
    *scan date*
    *was sent from*
    *of pages*
    *scanned file*
  - any of file.explode(...) where:
    
    at least 3 of:
    
    body.current_thread.text matches '*scan date*'
    body.current_thread.text matches '*was sent from*'
    body.current_thread.text matches '*of pages*'
    body.current_thread.text matches '*scanned file*'
    body.current_thread.text contains 'Number of Images:'
    body.current_thread.text contains 'Attachment File Type:'
    body.current_thread.text contains 'Device Name:'
    body.current_thread.text contains 'Device Location:'
    body.current_thread.text contains 'Device Model:'
any of:
- all of:
  - length(filter(attachments, .file_type in ('pdf'))) is 1
  - any of attachments where all hold:
    
    .file_type is 'pdf'
    any of:
    
    any of file.explode(.) where all hold:
    
    any of:
    
    .scan.ocr.raw matches any of 5 patterns
    
    *scan date*
    *was sent from*
    *of pages*
    *verif*document*
    *scanned file*
    any of ml.nlu_classifier(.scan.ocr.raw).intents where:
    
    .name is 'cred_theft'
    any of ml.logo_detect(.).brands where:
    
    .name in ('DocuSign', 'Microsoft')
    length(.scan.url.urls) is 1
    any of file.explode(.) where any holds:
    
    any of .scan.exiftool.fields where:
    
    .key is 'Encryption'
    all of:
    
    .scan.entropy.entropy > 7
    any of .scan.strings.strings where:
    
    . contains '/Encrypt'
- length(filter(attachments, .file_type in ('doc', 'docx'))) is 1
sender.email.domain.domain not in $org_domains
any of:
- not:
  - profile.by_sender().solicited
- all of:
  - profile.by_sender().any_messages_malicious_or_spam
  - not:
    
    profile.by_sender().any_messages_benign
not:
- profile.by_sender().any_messages_benign

Inspects: attachments[].file_type, body.current_thread.text, sender.email.domain.domain, type.inbound. Sensors: file.explode, file.message_screenshot, ml.logo_detect, ml.nlu_classifier, profile.by_sender, strings.icontains, strings.ilike. Reference lists: $org_domains.

Indicators matched (22)

Field	Match	Value
`ml.nlu_classifier(body.current_thread.text).entities[].name`	equals	`disclaimer`
`strings.icontains`	substring	`Number of Images:`
`strings.icontains`	substring	`Attachment File Type:`
`strings.icontains`	substring	`Device Model:`
`strings.icontains`	substring	`Device Name:`
`strings.icontains`	substring	`Resolution:`
`strings.icontains`	substring	`File Format:`
`strings.icontains`	substring	`Device Location:`
`strings.ilike`	substring	`scan date`
`strings.ilike`	substring	`was sent from`
`strings.ilike`	substring	`of pages`
`strings.ilike`	substring	`scanned file`

10 more

`attachments[].file_type`	member	`pdf`
`attachments[].file_type`	equals	`pdf`
`strings.ilike`	substring	`verifdocument*`
`ml.nlu_classifier(file.explode(attachments[])[].scan.ocr.raw).intents[].name`	equals	`cred_theft`
`ml.logo_detect(attachments[]).brands[].name`	member	`DocuSign`
`ml.logo_detect(attachments[]).brands[].name`	member	`Microsoft`
`file.explode(attachments[])[].scan.exiftool.fields[].key`	equals	`Encryption`
`strings.icontains`	substring	`/Encrypt`
`attachments[].file_type`	member	`doc`
`attachments[].file_type`	member	`docx`

`j` / `k`	Scroll down / up
`d` / `u`	Half-page down / up
`gg` / `G`	Top / bottom
`h` / `l`	History back / forward
`f`	Follow link (`Shift` = new tab)
`/`	Focus search
`?`	Toggle this help
`↑` / `↓`	Navigate search results
`Enter`	Open highlighted result
`Esc`	Close results / dialog

`type:`	`events` / `rules` / `providers`
`vendor:`	`sigma` / `elastic` / `splunk` / `kusto` / `chronicle` (vendor name alone also works: `sigma:`, `kql:`, `secops:`…)
`tactic:`	TA-id, slug, or name: `credential_access`, `TA0006`
`technique:`	technique or sub-technique ID: `T1003`, `T1003.001` (alias `tech:`)
`severity:`	`critical` / `high` / `medium` / `low` / `informational` (alias `sev:`)
`risk_score`	Numeric comparison on the Elastic risk score (0 to 100): `risk_score>50`, `risk_score<=20`, `risk_score=99` (alias `risk`; Elastic rules only)
`stages:`	Rules with exactly N pipeline stages
`correlation:`	`single_event` / `sequence` / `alternatives` / `alternatives_cross_log` / `all_required` / `correlated`
`with:`	Co-occurrence event-id; stacks (`with:4624 with:4769`) to require all, while a comma list in one occurrence (`with:4624,4769`) is an either-or group. Implies multi-event
`like:`	Structural neighbors of a rule slug (equivalents + subsumption stricter / broader): `like:comsvcs_lsass_memory_dump-splunk-sysmon`
`groupby:`	Entity-grouping substring match against `group_by_keys`: `groupby:user`, `groupby:host`
`uses:`	Rules whose predicate tree touches the field (any kind, any value): `uses:CommandLine`
`excludes:`	Rules with top-level `not()` clauses on the field (FP whitelists): `excludes:ParentImage`
`field:` / `value:`	Predicate search; narrows rule cards to those with a matching leaf and drives the indicator tier. Unquoted = substring, wildcards allowed (`value:mimikatz`)
`indicator:`	Shorthand for `field:F value:V`: `indicator:Image=*\powershell.exe`
`kind:`	Filter by predicate kind. Narrows rule cards to those carrying a matching predicate leaf (`vendor:elastic kind:cidr_match`) and drives the indicator tier: `contains` / `starts_with` / `ends_with` / `regex` / `cidr` / `eq` / `in` … (operator aliases `op:`/`match:`)
`has:` / `no:`	`sample`, `field`, `notes`, `refs`, `trace`, `thirdparty`, `rule`, `pattern`, `timewindow`, `threshold`, `newterms`, `sigma`/`elastic`/`splunk`/`kusto`/`chronicle`
`-op:val`	Exclude matches; works on most operators but not `type:`/`like:`/`has:`/`no:` (use `no:<flag>` to exclude a rule flag): `tactic:execution -vendor:splunk`. Standalone `-kind:`/`-field:`/`-value:` drop every rule carrying a matching predicate leaf (`type:rules -kind:is_null`)
`field:"…"` / `value:"…"`	Quoted value = anchored exact match (also allows spaces): `value:"net user"`
`a,b`	Comma = OR inside one operator (`vendor:sigma,elastic`, `severity:high,critical`); repeating a facet merges the same way. `field:`/`value:` never split (literal commas)
`vendors:` / `stage:`	Singular and plural spellings fold to the canonical operator and value: `tactics:` = `tactic:`, `type:event` = `type:events`, `correlation:sequences` = `correlation:sequence`, `has:thresholds` = `has:threshold`
`"quoted phrase"`	Exact-match a multi-word phrase (free text)