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.

 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 Linux icon Linux '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

- MITRE ATT&CK
+ Kill Chain Phases
+ NIST
+ CIS
- Threat Actors
ID Technique Tactic
T1036 Masquerading Stealth
Exploitation
DE.AE
CIS 10

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
Anomaly detections generate Intermediate Findings (Risk Events). They do not generate a Finding (Notable) directly.

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