Callback Phishing NLU body or attachment from first-time sender
Detects callback scams by analyzing text within images of receipts or invoices from first time senders.
Sublime rule (View on GitHub)
1name: "Callback Phishing NLU body or attachment from first-time sender"
2description: |
3 Detects callback scams by analyzing text within images of receipts or invoices from first time senders.
4type: "rule"
5severity: "medium"
6source: |
7 type.inbound
8 and length(attachments) < 5
9 and (
10 any(attachments,
11 (.file_type in $file_types_images or .file_type == "pdf")
12 and any(file.explode(.),
13
14 // exclude images taken with mobile cameras and screenshots from android
15 not any(.scan.exiftool.fields,
16 .key == "Model"
17 or .key == "Software" and strings.starts_with(.value, "Android")
18 )
19 and any(ml.nlu_classifier(.scan.ocr.raw).intents,
20 .name == "callback_scam" and .confidence == "high"
21 )
22 )
23 )
24 or any(ml.nlu_classifier(body.current_thread.text).intents,
25 .name in ("callback_scam")
26 and .confidence == "high"
27 and length(body.current_thread.text) < 1500
28 )
29 )
30 and not (
31 any(headers.domains, .domain == "smtp-out.gcp.bigcommerce.net")
32 and strings.icontains(body.html.raw, "bigcommerce.com")
33 )
34 and (
35 (
36 profile.by_sender().prevalence in ("new", "outlier")
37 and not profile.by_sender().solicited
38 )
39 or (
40 profile.by_sender().any_messages_malicious_or_spam
41 and not profile.by_sender().any_false_positives
42 )
43 )
44
45 // negate highly trusted sender domains unless they fail DMARC authentication
46 and
47 (
48 (
49 sender.email.domain.root_domain in $high_trust_sender_root_domains
50 and (
51 any(distinct(headers.hops, .authentication_results.dmarc is not null),
52 strings.ilike(.authentication_results.dmarc, "*fail")
53 )
54 )
55 )
56 or sender.email.domain.root_domain not in $high_trust_sender_root_domains
57 )
58
59attack_types:
60 - "Callback Phishing"
61tactics_and_techniques:
62 - "Out of band pivot"
63 - "Social engineering"
64detection_methods:
65 - "Content analysis"
66 - "File analysis"
67 - "Optical Character Recognition"
68 - "Natural Language Understanding"
69 - "Sender analysis"
70id: "b93c6f94-c9a3-587a-8eb5-6856754f8222"