Spam: Ghostwriting services scam with manipulative language

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

Detects unsolicited messages promoting ghostwriting or book publishing services that use manipulative language patterns commonly seen in scams, such as offering complimentary samples, expressing fascination with the recipient's achievements, or requesting personal information under the guise of writing assistance.

Threat classification

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

Category	Values
Attack types	Spam
Tactics and techniques	Social engineering

Event coverage

Message attribute
body.current_thread
headers.auth_summary
sender.email
subject
type

Rule body MQL

type.inbound
and length(body.current_thread.text) < 1500
and (
  // Ghostwriting and book-related terms in subject
  strings.icontains(subject.subject, "book project")
  or strings.icontains(subject.subject, "ghostwriting")
  or strings.icontains(subject.subject, "becoming an author")
  or strings.icontains(subject.subject, "your book")
  or strings.icontains(subject.subject, "writing project")
  or strings.icontains(subject.subject, "publish")
  or strings.icontains(subject.subject, " author ")

  // Body contains ghostwriting service offers
  or strings.icontains(body.current_thread.text, "ghostwriting")
  or strings.icontains(body.current_thread.text, "ghostwriter")
  or strings.icontains(body.current_thread.text, "writing firm")
  or strings.icontains(body.current_thread.text, "book writing")
  or strings.icontains(body.current_thread.text, "publishing")
)
// Common scam language patterns
and (
  (
    strings.icontains(body.current_thread.text, "complimentary")
    and (
      strings.icontains(body.current_thread.text, "sample")
      or strings.icontains(body.current_thread.text, "chapter")
      or strings.icontains(body.current_thread.text, "consultation")
    )
  )
  or (
    strings.icontains(body.current_thread.text, "fascinated")
    and strings.icontains(body.current_thread.text, "what you have done")
  )
  or strings.icontains(body.current_thread.text, "inspiring stories")
  or strings.icontains(body.current_thread.text, "gather some information")
  or strings.icontains(body.current_thread.text, "few minutes of your time")
  or strings.icontains(body.current_thread.text, "absolutely no obligation")
  or strings.icontains(body.current_thread.text, "writing team")
)
and not (
  any(ml.nlu_classifier(body.current_thread.text).topics,
      .name in (
        "Newsletters and Digests",
        "Events and Webinars",
        "Educational and Research",
        "Financial Communications",
        "Advertising and Promotions",
        "Legal and Compliance",
        "Professional and Career Development"
      )
      and .confidence == "high"
  )
)

// Standard negations
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
)

Detection logic

Scope: inbound message.

inbound message
length(body.current_thread.text) < 1500
any of:
- subject.subject contains 'book project'
- subject.subject contains 'ghostwriting'
- subject.subject contains 'becoming an author'
- subject.subject contains 'your book'
- subject.subject contains 'writing project'
- subject.subject contains 'publish'
- subject.subject contains ' author '
- body.current_thread.text contains 'ghostwriting'
- body.current_thread.text contains 'ghostwriter'
- body.current_thread.text contains 'writing firm'
- body.current_thread.text contains 'book writing'
- body.current_thread.text contains 'publishing'
any of:
- all of:
  - body.current_thread.text contains 'complimentary'
  - any of:
    
    body.current_thread.text contains 'sample'
    body.current_thread.text contains 'chapter'
    body.current_thread.text contains 'consultation'
- all of:
  - body.current_thread.text contains 'fascinated'
  - body.current_thread.text contains 'what you have done'
- body.current_thread.text contains 'inspiring stories'
- body.current_thread.text contains 'gather some information'
- body.current_thread.text contains 'few minutes of your time'
- body.current_thread.text contains 'absolutely no obligation'
- body.current_thread.text contains 'writing team'
not:
- any of ml.nlu_classifier(body.current_thread.text).topics where all hold:
  - .name in ('Newsletters and Digests', 'Events and Webinars', 'Educational and Research', 'Financial Communications', 'Advertising and Promotions', 'Legal and Compliance', 'Professional and Career Development')
  - .confidence is 'high'
any of:
- all of:
  - sender.email.domain.root_domain in $high_trust_sender_root_domains
  - not:
    
    headers.auth_summary.dmarc.pass
- sender.email.domain.root_domain not in $high_trust_sender_root_domains

Inspects: body.current_thread.text, headers.auth_summary.dmarc.pass, sender.email.domain.root_domain, subject.subject, type.inbound. Sensors: ml.nlu_classifier, strings.icontains. Reference lists: $high_trust_sender_root_domains.

Indicators matched (30)

Field	Match	Value
`strings.icontains`	substring	`book project`
`strings.icontains`	substring	`ghostwriting`
`strings.icontains`	substring	`becoming an author`
`strings.icontains`	substring	`your book`
`strings.icontains`	substring	`writing project`
`strings.icontains`	substring	`publish`
`strings.icontains`	substring	`author`
`strings.icontains`	substring	`ghostwriter`
`strings.icontains`	substring	`writing firm`
`strings.icontains`	substring	`book writing`
`strings.icontains`	substring	`publishing`
`strings.icontains`	substring	`complimentary`

18 more

`strings.icontains`	substring	`sample`
`strings.icontains`	substring	`chapter`
`strings.icontains`	substring	`consultation`
`strings.icontains`	substring	`fascinated`
`strings.icontains`	substring	`what you have done`
`strings.icontains`	substring	`inspiring stories`
`strings.icontains`	substring	`gather some information`
`strings.icontains`	substring	`few minutes of your time`
`strings.icontains`	substring	`absolutely no obligation`
`strings.icontains`	substring	`writing team`
`ml.nlu_classifier(body.current_thread.text).topics[].name`	member	`Newsletters and Digests`
`ml.nlu_classifier(body.current_thread.text).topics[].name`	member	`Events and Webinars`
`ml.nlu_classifier(body.current_thread.text).topics[].name`	member	`Educational and Research`
`ml.nlu_classifier(body.current_thread.text).topics[].name`	member	`Financial Communications`
`ml.nlu_classifier(body.current_thread.text).topics[].name`	member	`Advertising and Promotions`
`ml.nlu_classifier(body.current_thread.text).topics[].name`	member	`Legal and Compliance`
`ml.nlu_classifier(body.current_thread.text).topics[].name`	member	`Professional and Career Development`
`ml.nlu_classifier(body.current_thread.text).topics[].confidence`	equals	`high`

`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)