Brand impersonation: Microsoft with embedded logo and credential theft language

 1name: "Brand impersonation: Microsoft with embedded logo and credential theft language"
 2description: "This rule detects messages impersonating Microsoft via a logo and contains credential theft language. From a new and unsolicited sender."
 3type: "rule"
 4severity: "high"
 5source: |
 6  type.inbound
 7  and length(attachments) == 0
 8  and any(ml.logo_detect(beta.message_screenshot()).brands,
 9          strings.starts_with(.name, "Microsoft")
10  )
11  and any(ml.nlu_classifier(body.current_thread.text).intents,
12          .name == "cred_theft" and .confidence in ("medium", "high")
13  )
14  and (
15    not (
16      headers.auth_summary.dmarc.pass
17      and headers.auth_summary.dmarc.details.from.domain in (
18        "azureadnotifications.us",
19        "microsoft.com",
20        "sharepointonline.com",
21        "cloudappsecurity.com",
22        "microsoftsupport.com",
23        "microsoft.onmicrosoft.com",
24        "yammer.com"
25      )
26    )
27    or headers.auth_summary.dmarc.pass is null
28    or headers.auth_summary.dmarc.details.from.domain is null
29  )
30  and (
31    not profile.by_sender().solicited
32    or (
33      profile.by_sender().any_messages_malicious_or_spam
34      and not profile.by_sender().any_false_positives
35    )
36  )
37  
38  // negate highly trusted sender domains unless they fail DMARC authentication
39  and (
40    (
41      sender.email.domain.root_domain in $high_trust_sender_root_domains
42      and not headers.auth_summary.dmarc.pass
43    )
44    or sender.email.domain.root_domain not in $high_trust_sender_root_domains
45  )
46  and not profile.by_sender().any_false_positives  
47
48attack_types:
49  - "Credential Phishing"
50tactics_and_techniques:
51  - "Impersonation: Brand"
52  - "Social engineering"
53detection_methods:
54  - "Computer Vision"
55  - "Natural Language Understanding"
56  - "Sender analysis"
57id: "3ee9ef3d-8ec4-5df0-a8a2-5c6d037eb17a"