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This detection identifies potential Pass the Token or Pass the Hash credential exploits. We detect the main side effect of these attacks, which is a transition from the dominant Kerberos logins to rare NTLM logins for a given user, as reported by a detination device.

  • Type: TTP
  • Product: Splunk Behavioral Analytics
  • Datamodel:
  • Last Updated: 2021-11-05
  • Author: Stanislav Miskovic, Splunk
  • ID: 82e76b80-5cdb-4899-9b43-85dbe777b36d


ID Technique Tactic
T1550 Use Alternate Authentication Material Defense Evasion, Lateral Movement
T1550.002 Pass the Hash Defense Evasion, Lateral Movement

| from read_ssa_enriched_events() 
| eval timestamp=      parse_long(ucast(map_get(input_event, "_time"), "string", null)), dest_user=      lower(ucast(map_get(input_event, "dest_user_primary_artifact"), "string", null)), dest_user_id=   ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=       ucast(map_get(input_event, "dest_device_id"), "string", null), signature_id=   lower(ucast(map_get(input_event, "signature_id"), "string", null)), authentication_method=  lower(ucast(map_get(input_event, "authentication_method"), "string", null))

| where signature_id = "4624" AND (authentication_method="ntlmssp" OR authentication_method="kerberos") AND dest_user_id != null AND dest_device_id != null

| eval isKerberos=if(authentication_method == "kerberos", 1, 0), isNtlm=if(authentication_method == "ntlmssp", 1, 0), timeNTLM=if(isNtlm > 0, timestamp, null)

| stats sum(isKerberos) as totalKerberos, sum(isNtlm)     as totalNtlm, min(timestamp)  as startTime, min(timeNTLM)   as startNTLMTime, max(timestamp)  as endTime, max(timeNTLM)   as endNTLMTime by dest_user_id, dest_user, dest_device_id, span(timestamp, 86400s)

| where NOT dest_user="-" AND totalKerberos > 0 AND totalNtlm > 0 AND endTime - startTime > 1800000 AND (totalKerberos > 10 * totalNtlm AND totalKerberos > 50)  AND (endTime - startTime) > 3 * (endNTLMTime - startNTLMTime)

| eval start_time=ucast(startNTLMTime, "long", null), end_time=ucast(endNTLMTime, "long", null), entities=mvappend(dest_user_id, dest_device_id), body=create_map(["total_kerberos", totalKerberos, "total_ntlm", totalNtlm, "analysis_start_time", startTime, "analysis_end_time", endTime, "pth_start_time", startNTLMTime, "pth_end_time", endNTLMTime])

| into write_ssa_detected_events();

Associated Analytic Story

How To Implement

You must be ingesting Windows Security logs from endpoint devices, i.e., destinations of interest. Please make sure that event ID 4624 is being logged.

Required field

  • _time
  • signature_id
  • dest_user
  • dest_user_id
  • dest_device_id
  • authentication_method

Kill Chain Phase

  • Lateral Movement

Known False Positives

Environments in which NTLM is used extremely rarely and for benign purposes (such as a rare use of SMB shares).


Risk Score Impact Confidence Message
72.0 80 90 Potential lateral movement and credential stealing via Pass the Token or Pass the Hash techniques. Operation is performed via credentials of the account $dest_user_id$ and observed by the destination device $dest_device_id$


Test Dataset

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

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