Brand impersonation: USPS

 1name: "Brand impersonation: USPS"
 2description: "Impersonation of the United States Postal Service."
 3type: "rule"
 4severity: "high"
 5source: |
 6  type.inbound
 7  and (
 8    any(ml.logo_detect(file.message_screenshot()).brands, .name == "USPS")
 9    or sender.display_name =~ "USPS"
10  )
11  and length(body.links) > 0
12  and 2 of (
13    any(body.links,
14        strings.ilike(.display_text,
15                      "*check now*",
16                      "*track*",
17                      "*package*",
18                      '*view your order*'
19        )
20    ),
21    strings.ilike(body.current_thread.text,
22                  "*returned*to*sender*",
23                  "*redelivery*"
24    ),
25    // impersonal greeting
26    any(ml.nlu_classifier(body.current_thread.text).entities,
27        .name == "recipient" and .text =~ "Customer"
28    ),
29    // no links go to usps.com
30    all(body.links, .href_url.domain.root_domain != "usps.com")
31  )
32  and (
33    sender.email.domain.root_domain not in (
34        "usps.com", 
35        "opinions-inmoment.com", // https://faq.usps.com/s/article/USPS-Customer-Experience-Surveys
36        "shipup.co", // third party shipping company
37        "withings.com" // third party shipping company
38    )
39    or (
40      sender.email.domain.root_domain in (
41          "usps.com", 
42          "opinions-inmoment.com" // https://faq.usps.com/s/article/USPS-Customer-Experience-Surveys
43      )
44      and not headers.auth_summary.dmarc.pass
45    )
46  )
47  
48  // negate highly trusted sender domains unless they fail DMARC authentication
49  and (
50    (
51      sender.email.domain.root_domain in $high_trust_sender_root_domains
52      and not headers.auth_summary.dmarc.pass
53    )
54    or sender.email.domain.root_domain not in $high_trust_sender_root_domains
55  )  
56attack_types:
57  - "Credential Phishing"
58tactics_and_techniques:
59  - "Image as content"
60  - "Impersonation: Brand"
61  - "Social engineering"
62detection_methods:
63  - "Computer Vision"
64  - "Content analysis"
65  - "Natural Language Understanding"
66  - "Sender analysis"
67id: "28b9130a-d8e0-50af-97c9-c1b8f4c46d68"