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Description

Detects messages impersonating HR departments containing many links with malformed domains, suspicious TLD patterns, and credential theft language detected through URL analysis.

References

No references.

Sublime Security
Created Dec 3rd, 2025 • Last updated Dec 3rd, 2025
Source
type.inbound
// high number of links
and length(body.links) > 20
// hr-related subject or sender
and (
  regex.icontains(sender.display_name, "\\bhr\\b")
  or strings.icontains(sender.display_name, "human resources")
  or strings.icontains(sender.display_name, "employee relation")
  or regex.icontains(subject.subject, "sal[ai1l|]r[i1l|]es")
  or regex.icontains(subject.subject, "hr__.{0,30}")
  or regex.icontains(subject.subject, "work.{0,5}hours")
  or regex.icontains(subject.subject,
                     "instant:.{0,20}(salaries|salary|changed|update)"
  )
  or strings.icontains(body.current_thread.text, "vacation plan")
)
// suspect domain irregularities (like www.,company.com)
and any(body.links,
        (
          // malformed domains with comma variations
          regex.icontains(.display_text, "www.?,")
          // multiple consecutive dots
          or regex.icontains(.display_text, "\\.{2,}")
          // comma in domain position  
          or regex.icontains(.display_text, "\\.,")
          // suspicious TLD patterns that might be typosquatting
          or regex.icontains(.display_text, "\\.(tu|cg|mv|tk|3v|ct|jh)/")
          // random characters in TLD position
          or regex.icontains(.display_text,
                             "\\.[a-z0-9]{1,3}/[a-z0-9]+/[a-z0-9]+/"
          )
          // URLs that contain obvious credential theft terms in the path
          or regex.icontains(.display_text,
                             "/(sal[ai1l|]r[i1l|]es|login|auth|verify|portal|payment)/"
          )
        )
        and .visible == true
        and any(ml.nlu_classifier(beta.ocr(ml.link_analysis(.).screenshot).text).intents,
                .name == "cred_theft" and .confidence == "high"
        )
)
// exclusions for legitimate sources 
and not any(ml.nlu_classifier(body.current_thread.text).topics,
            .name in (
              "Security and Authentication",
              "Secure Message",
              "Newsletters and Digests",
              "Entertainment and Sports"
            )
            and .confidence in ("medium", "high")
)
// exclude messages with a bunch of previous corrospondance
and not length(body.previous_threads) > 5
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