:warning: THIS IS A EXPERIMENTAL DETECTION

This detection has been marked experimental by the Splunk Threat Research team. This means we have not been able to test, simulate, or build datasets for this detection. Use at your own risk. This analytic is NOT supported.

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Description

The following analytic utilizes the Certificates Datamodel to look for punycode domains, starting with xn–, found in the SSL issuer email domain. The presence of punycode here does not equate to evil, therefore we need to decode the punycode to determine what it translates to. Remove the CyberChef recipe as needed and decode manually. Note that this is not the exact location of the malicious punycode to trip CVE-2022-3602, but a method to at least identify fuzzing occurring on these email paths. What does evil look like? it will start with

  • Type: Hunting
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud

  • Last Updated: 2022-11-01
  • Author: Michael Haag, Splunk
  • ID: 696694df-5706-495a-81f2-79501fa11b90

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1573 Encrypted Channel Command And Control
Kill Chain Phase
  • Reconnaissance
  • Delivery
NIST
  • DE.CM
CIS20
  • CIS 3
  • CIS 5
  • CIS 16
CVE
1
2
3
4
5
6
7
8
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Certificates.All_Certificates by All_Certificates.SSL.ssl_issuer_email_domain All_Certificates.SSL.ssl_issuer All_Certificates.SSL.ssl_subject_email All_Certificates.SSL.dest All_Certificates.SSL.src All_Certificates.SSL.sourcetype All_Certificates.SSL.ssl_subject_email_domain 
| `drop_dm_object_name("All_Certificates.SSL")` 
| eval punycode=if(like(ssl_issuer_email_domain,"%xn--%"),1,0) 
| where punycode=1 
| cyberchef infield="ssl_issuer_email_domain" outfield="convertedPuny" jsonrecipe="[{"op":"From Punycode","args":[true]}]" 
| table ssl_issuer_email_domain convertedPuny ssl_issuer ssl_subject_email dest src sourcetype ssl_subject_email_domain 
| `ssl_certificates_with_punycode_filter`

Macros

The SPL above uses the following Macros:

:information_source: ssl_certificates_with_punycode_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Required fields

List of fields required to use this analytic.

  • All_Certificates.SSL.ssl_issuer_email_domain
  • All_Certificates.SSL.ssl_issuer
  • All_Certificates.SSL.ssl_subject_email
  • All_Certificates.SSL.dest
  • All_Certificates.SSL.src
  • All_Certificates.SSL.sourcetype
  • All_Certificates.SSL.ssl_subject_email_domain

How To Implement

Ensure data is properly being ingested into the Certificates datamodel. If decoding the of interest, the CyberChef app is needed https://splunkbase.splunk.com/app/5348. If decoding is not needed, remove the cyberchef lines.

Known False Positives

False positives may be present if the organization works with international businesses. Filter as needed.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
15.0 50 30 A x509 certificate has been identified to have punycode in the SSL issuer email domain on $dest$.

:information_source: The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

Reference

Test Dataset

Replay any dataset to Splunk Enterprise by using our replay.py tool or the UI. Alternatively you can replay a dataset into a Splunk Attack Range

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