| ID | Technique | Tactic |
|---|---|---|
| T1036 | Masquerading | Stealth |
Detection: Linux Suspicious Staging of Alternate System Files
Description
The following analytic detects the creation of sensitive system files such as nsswitch.conf, passwd, shadow, sudoers, and NSS shared libraries outside of their canonical /etc or /var/lib/docker paths. This technique is associated with CVE-2025-32463 (chwoot), where an attacker stages a fake root directory containing spoofed system configuration files and NSS libraries to manipulate how privileged processes such as sudo resolve name services, enabling local privilege escalation by hijacking library loading without modifying the real /etc directory.
Search
1
2| tstats `security_content_summariesonly`
3 count min(_time) as firstTime max(_time) as lastTime
4
5FROM datamodel=Endpoint.Filesystem WHERE
6
7Filesystem.file_path IN (
8 "*/etc/group",
9 "*/etc/nsswitch.conf",
10 "*/etc/pam.conf",
11 "*/etc/passwd",
12 "*/etc/shadow",
13 "*/etc/sudo.conf",
14 "*/etc/sudoers",
15 "*/libnss_*"
16)
17NOT Filesystem.file_path IN (
18 "/etc/*",
19 "/var/lib/docker/*"
20)
21
22BY Filesystem.action Filesystem.dest Filesystem.file_access_time
23 Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time
24 Filesystem.file_name Filesystem.file_path Filesystem.process_guid
25 Filesystem.process_id Filesystem.user Filesystem.vendor_product
26
27
28| `drop_dm_object_name(Filesystem)`
29
30| `security_content_ctime(firstTime)`
31
32| `security_content_ctime(lastTime)`
33
34| `linux_suspicious_staging_of_alternate_system_files_filter`
Data Source
| Name | Platform | Sourcetype | Source |
|---|---|---|---|
| Sysmon for Linux EventID 11 | 'sysmon:linux' |
'Syslog:Linux-Sysmon/Operational' |
Macros Used
| Name | Value |
|---|---|
| security_content_summariesonly | summariesonly=summariesonly_config allow_old_summaries=oldsummaries_config fillnull_value=fillnull_config`` |
| linux_suspicious_staging_of_alternate_system_files_filter | search * |
linux_suspicious_staging_of_alternate_system_files_filter is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
Annotations
Default Configuration
This detection is configured by default in Splunk Enterprise Security to run with the following settings:
| Setting | Value |
|---|---|
| Disabled | true |
| Cron Schedule | 0 * * * * |
| Earliest Time | -70m@m |
| Latest Time | -10m@m |
| Schedule Window | auto |
| Creates Finding (Notable) | No |
| Creates Intermediate Finding (Risk Event) | Yes |
Implementation
The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain file creation events including the file path and user context. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the Filesystem node of the Endpoint data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
Known False Positives
Developers or system administrators may stage alternate system files for legitimate testing or containerized environments. Filter based on known development or testing systems.
Associated Analytic Story
Intermediate Findings
| Message | Entity Field | Entity Type | Risk Score |
|---|---|---|---|
| Suspicious Staging of Alternate System File - [$file_name$] observed on [$dest$]. | dest | system | 20 |
Threat Objects
| Field | Type |
|---|---|
| file_name | file_name |
References
Detection Testing
| Test Type | Status | Dataset | Source | Sourcetype |
|---|---|---|---|---|
| Validation | ✅ Passing | N/A | N/A | N/A |
| Unit | ✅ Passing | Dataset | Syslog:Linux-Sysmon/Operational |
sysmon:linux |
| Integration | ✅ Passing | Dataset | Syslog:Linux-Sysmon/Operational |
sysmon:linux |
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: GitHub |
Version: 1