Brand impersonation: Chase Bank

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

Impersonation of Chase Bank and related services to harvest credentials or related information such as dates of birth, phone numbers, social security numbers, ATM pin numbers, drivers license numbers, selfies, and ID card photos.

Threat classification

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

Category	Values
Attack types	Credential Phishing
Tactics and techniques	Impersonation: Brand, Lookalike domain, Social engineering

Event coverage

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

Rule body MQL

type.inbound
and (
  strings.ilike(sender.display_name,
                '*chase sapphire*',
                '*chase card services*',
                '*united mileageplus*',
                "echase*"
  )
  or strings.ilevenshtein(sender.display_name, 'chase sapphire') <= 2
  or strings.ilevenshtein(sender.display_name, 'chase card services') <= 2
  or strings.ilevenshtein(sender.display_name, 'united mileageplus') <= 2
  or (
    (
      strings.ilevenshtein(sender.display_name, 'echase') <= 1
      or (
        strings.icontains(sender.display_name, "bank")
        and strings.icontains(subject.base, "chase bank")
      )
    )
    // Negate Chase sender display name if cred theft, callback phishing, and a Chase logo isn't detected
    and not (
      strings.icontains(sender.display_name, 'chase')
      and not (
        any(ml.nlu_classifier(body.current_thread.text).intents,
            .name == "cred_theft" and .confidence in ("medium", "high")
        )
        or any(ml.nlu_classifier(body.current_thread.text).intents,
               .name == "callback_scam" and .confidence in ("medium", "high")
        )
        or any(ml.logo_detect(file.message_screenshot()).brands,
               strings.starts_with(.name, "Chase")
        )
      )
    )
  )
  or regex.icontains(body.current_thread.text,
                     '(Chase|J\.?\s?P\.?\sMorgan)\s(Privacy|Treasury)\sOperations|(Privacy|Treasury)\sOperations\s(Chase|J\.?\s?P\.?\sMorgan)'
  )
)
and not (
  sender.display_name is not null and sender.display_name in~ ("chaser", "case")
)
and sender.email.domain.root_domain not in~ (
  'chase.com',
  'united.com',
  'transunion.com',
  'shopping-chase.com',
  'chasetravel.com',
  'chaseoffers.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.

inbound message
any of:
- sender.display_name matches any of 4 patterns
  - *chase sapphire*
  - *chase card services*
  - *united mileageplus*
  - echase*
- sender.display_name is similar to 'chase sapphire'
- sender.display_name is similar to 'chase card services'
- sender.display_name is similar to 'united mileageplus'
- all of:
  - any of:
    
    sender.display_name is similar to 'echase'
    all of:
    
    sender.display_name contains 'bank'
    subject.base contains 'chase bank'
  - not:
    
    all of:
    
    sender.display_name contains 'chase'
    none of:
    
    any of ml.nlu_classifier(body.current_thread.text).intents where all hold:
    
    .name is 'cred_theft'
    .confidence in ('medium', 'high')
    any of ml.nlu_classifier(body.current_thread.text).intents where all hold:
    
    .name is 'callback_scam'
    .confidence in ('medium', 'high')
    any of ml.logo_detect(file.message_screenshot()).brands where:
    
    .name starts with 'Chase'
- body.current_thread.text matches '(Chase|J\\.?\\s?P\\.?\\sMorgan)\\s(Privacy|Treasury)\\sOperations|(Privacy|Treasury)\\sOperations\\s(Chase|J\\.?\\s?P\\.?\\sMorgan)'
not:
- all of:
  - sender.display_name is set
  - sender.display_name in ('chaser', 'case')
sender.email.domain.root_domain not in ('chase.com', 'united.com', 'transunion.com', 'shopping-chase.com', 'chasetravel.com', 'chaseoffers.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: body.current_thread.text, headers.auth_summary.dmarc.pass, sender.display_name, sender.email.domain.root_domain, subject.base, type.inbound. Sensors: file.message_screenshot, ml.logo_detect, ml.nlu_classifier, regex.icontains, strings.icontains, strings.ilevenshtein, strings.ilike, strings.starts_with. Reference lists: $high_trust_sender_root_domains.

Indicators matched (25)

Field	Match	Value
`strings.ilike`	substring	`chase sapphire`
`strings.ilike`	substring	`chase card services`
`strings.ilike`	substring	`united mileageplus`
`strings.ilike`	substring	`echase*`
`strings.ilevenshtein`	fuzzy	`chase sapphire`
`strings.ilevenshtein`	fuzzy	`chase card services`
`strings.ilevenshtein`	fuzzy	`united mileageplus`
`strings.ilevenshtein`	fuzzy	`echase`
`strings.icontains`	substring	`bank`
`strings.icontains`	substring	`chase bank`
`strings.icontains`	substring	`chase`
`ml.nlu_classifier(body.current_thread.text).intents[].name`	equals	`cred_theft`

13 more

`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(body.current_thread.text).intents[].name`	equals	`callback_scam`
`strings.starts_with`	prefix	`Chase`
`regex.icontains`	regex	`(Chase\|J\.?\s?P\.?\sMorgan)\s(Privacy\|Treasury)\sOperations\|(Privacy\|Treasury)\sOperations\s(Chase\|J\.?\s?P\.?\sMorgan)`
`sender.display_name`	member	`chaser`
`sender.display_name`	member	`case`
`sender.email.domain.root_domain`	member	`chase.com`
`sender.email.domain.root_domain`	member	`united.com`
`sender.email.domain.root_domain`	member	`transunion.com`
`sender.email.domain.root_domain`	member	`shopping-chase.com`
`sender.email.domain.root_domain`	member	`chasetravel.com`
`sender.email.domain.root_domain`	member	`chaseoffers.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)