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.

Try in Splunk Security Cloud


The following analytic detects emails that contain attachments with suspicious file extensions. Detecting and responding to emails with suspicious attachments can mitigate the risks associated with phishing and malware attacks, thereby protecting the organization's data and systems from potential harm. The detection is made by using a Splunk query that searches for emails in the datamodel=Email where the filename of the attachment is not empty. The analytic uses the tstats command to summarize the count, first time, and last time of the emails that meet the criteria. It groups the results by the source user, file name, and message ID of the email. The detection is important because it indicates potential phishing or malware delivery attempts in which an attacker attempts to deliver malicious content through email attachments, which can lead to data breaches, malware infections, or unauthorized access to sensitive information. Next steps include reviewing the identified emails and attachments and analyzing the source user, file name, and message ID to determine if they are legitimate or malicious. Additionally, you must inspect any relevant on-disk artifacts associated with the attachments and investigate any concurrent processes to identify the source of the attack.

  • Type: Anomaly
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Email
  • Last Updated: 2023-04-14
  • Author: David Dorsey, Splunk
  • ID: 473bd65f-06ca-4dfe-a2b8-ba04ab4a0084




ID Technique Tactic
T1566.001 Spearphishing Attachment Initial Access
T1566 Phishing Initial Access
Kill Chain Phase
  • Delivery
  • DE.AE
  • CIS 13
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `drop_dm_object_name("All_Email")` 
| `suspicious_email_attachments` 
| `suspicious_email_attachment_extensions_filter` 


The SPL above uses the following Macros:

:information_source: suspicious_email_attachment_extensions_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.

  • _time
  • All_Email.file_name
  • All_Email.src_user
  • All_Email.message_id

How To Implement

You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model.
Splunk Phantom Playbook Integration
If Splunk Phantom is also configured in your environment, a Playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk https://splunkbase.splunk.com/app/3411/, and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox.

Known False Positives

None identified

Associated Analytic Story


Risk Score Impact Confidence Message
25.0 50 50 tbd

: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.


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

source | version: 3