Credential phishing content and link (untrusted sender)

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

Message contains credential theft language and a link to a credential phishing page from an unknown sender. We use Link Analysis in aggressive mode to increase our chances of scanning.

Threat classification

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

Category	Values
Attack types	Credential Phishing
Tactics and techniques	Social engineering

Event coverage

Message attribute
attachments (collection)
body
body.current_thread
body.links (collection)
headers (collection)
headers.auth_summary
headers.hops (collection)
recipients
recipients.to (collection)
sender
sender.email
type

Rule body MQL

type.inbound
and (
  any(ml.nlu_classifier(body.current_thread.text).intents,
      .name == "cred_theft" and .confidence in ("medium", "high")
  )
  // embedded in an image attachment
  // note: don't use message_screenshot() for now
  // because it's not limited to current_thread and may FP
  or any(attachments,
         .file_type in $file_types_images
         and any(file.explode(.),
                 any(ml.nlu_classifier(.scan.ocr.raw).intents,
                     .name == "cred_theft" and .confidence in ("medium", "high")
                 )
         )
  )
)
and any(body.links,
        .href_url.domain.root_domain not in ("outlook.com")
        and .href_url.domain.domain != "play.google.com"
        and ml.link_analysis(., mode="aggressive").effective_url.domain.domain != "play.google.com"
        and ml.link_analysis(., mode="aggressive").credphish.disposition == "phishing"
        and (
          ml.link_analysis(., mode="aggressive").credphish.confidence in (
            "medium",
            "high"
          )
          or ml.link_analysis(., mode="aggressive").credphish.contains_captcha
        )
        and not .href_url.domain.root_domain == "c3reservations.com"
)
and (
  (
    profile.by_sender_email().prevalence in ("new", "outlier")
    and not profile.by_sender_email().solicited
  )
  or (
    profile.by_sender_email().any_messages_malicious_or_spam
    and not profile.by_sender_email().any_messages_benign
  )
  // or there are no recipients
  or length(recipients.to) == 0
  // or the recipients are all invalid 
  or all(recipients.to, .email.domain.valid == false)

  // or the sender exhibits a "self sender" pattern
  or (
    length(recipients.to) == 1
    and recipients.to[0].email.email == sender.email.email
  )
)

// negate docusign 'via' messages
and not (
  any(headers.hops,
      any(.fields,
          .name == "X-Api-Host" and strings.ends_with(.value, "docusign.net")
      )
  )
  and strings.contains(sender.display_name, "via")
)

// negate docusign originated emails
and not any(headers.hops,
            regex.imatch(.received.server.raw, ".+.docusign.(net|com)")
)

// negate highly trusted sender domains unless they fail DMARC authentication
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.

Message contains credential theft language and a link to a credential phishing page from an unknown sender. We use Link Analysis in aggressive mode to increase our chances of scanning.

inbound message
any of:
- any of ml.nlu_classifier(body.current_thread.text).intents where all hold:
  - .name is 'cred_theft'
  - .confidence in ('medium', 'high')
- any of attachments where all hold:
  - .file_type in $file_types_images
  - any of file.explode(.) where:
    
    any of ml.nlu_classifier(.scan.ocr.raw).intents where all hold:
    
    .name is 'cred_theft'
    .confidence in ('medium', 'high')
any of body.links where all hold:
- .href_url.domain.root_domain not in ('outlook.com')
- .href_url.domain.domain is not 'play.google.com'
- ml.link_analysis(.).effective_url.domain.domain is not 'play.google.com'
- ml.link_analysis(.).credphish.disposition is 'phishing'
- any of:
  - ml.link_analysis(.).credphish.confidence in ('medium', 'high')
  - ml.link_analysis(.).credphish.contains_captcha
- not:
  - .href_url.domain.root_domain is 'c3reservations.com'
any of:
- all of:
  - profile.by_sender_email().prevalence in ('new', 'outlier')
  - not:
    
    profile.by_sender_email().solicited
- all of:
  - profile.by_sender_email().any_messages_malicious_or_spam
  - not:
    
    profile.by_sender_email().any_messages_benign
- length(recipients.to) is 0
- all of recipients.to where:
  - .email.domain.valid is False
- all of:
  - length(recipients.to) is 1
  - recipients.to[0].email.email is sender.email.email
not:
- all of:
  - any of headers.hops where:
    
    any of .fields where all hold:
    
    .name is 'X-Api-Host'
    .value ends with 'docusign.net'
  - sender.display_name contains 'via'
not:
- any of headers.hops where:
  - .received.server.raw matches '.+.docusign.(net|com)'
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: attachments[].file_type, body.current_thread.text, body.links, body.links[].href_url.domain.domain, body.links[].href_url.domain.root_domain, headers.auth_summary.dmarc.pass, headers.hops, headers.hops[].fields, headers.hops[].fields[].name, headers.hops[].fields[].value, headers.hops[].received.server.raw, recipients.to, recipients.to[0].email.email, recipients.to[].email.domain.valid, sender.display_name, sender.email.domain.root_domain, sender.email.email, type.inbound. Sensors: file.explode, ml.link_analysis, ml.nlu_classifier, profile.by_sender_email, regex.imatch, strings.contains, strings.ends_with. Reference lists: $file_types_images, $high_trust_sender_root_domains.

Indicators matched (12)

Field	Match	Value
`ml.nlu_classifier(body.current_thread.text).intents[].name`	equals	`cred_theft`
`ml.nlu_classifier(body.current_thread.text).intents[].confidence`	member	`medium`
`ml.nlu_classifier(body.current_thread.text).intents[].confidence`	member	`high`
`ml.nlu_classifier(file.explode(attachments[])[].scan.ocr.raw).intents[].name`	equals	`cred_theft`
`ml.nlu_classifier(file.explode(attachments[])[].scan.ocr.raw).intents[].confidence`	member	`medium`
`ml.nlu_classifier(file.explode(attachments[])[].scan.ocr.raw).intents[].confidence`	member	`high`
`body.links[].href_url.domain.root_domain`	member	`outlook.com`
`body.links[].href_url.domain.root_domain`	equals	`c3reservations.com`
`headers.hops[].fields[].name`	equals	`X-Api-Host`
`strings.ends_with`	suffix	`docusign.net`
`strings.contains`	substring	`via`
`regex.imatch`	regex	`.+.docusign.(net\|com)`

`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)

Credential phishing content and link (untrusted sender)

Threat classification

Event coverage

Rule body MQL

Detection logic

Indicators matched (12)

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