Detection: Linux Auditd Find Private Keys

Description

The following analytic detects suspicious attempts to find private keys, which may indicate an attacker's effort to access sensitive cryptographic information. Private keys are crucial for securing encrypted communications and data, and unauthorized access to them can lead to severe security breaches, including data decryption and identity theft. By monitoring for unusual or unauthorized searches for private keys, this analytic helps identify potential threats to cryptographic security, enabling security teams to take swift action to protect the integrity and confidentiality of encrypted information.

1`linux_auditd` `linux_auditd_normalized_execve_process` 
2| rename host as dest 
3| where  (LIKE (process_exec, "%find%") OR LIKE (process_exec, "%grep%")) AND (LIKE (process_exec, "%.pem%") OR LIKE (process_exec, "%.cer%") OR LIKE (process_exec, "%.crt%") OR LIKE (process_exec, "%.pgp%") OR LIKE (process_exec, "%.key%") OR LIKE (process_exec, "%.gpg%")OR LIKE (process_exec, "%.ppk%") OR LIKE (process_exec, "%.p12%")OR LIKE (process_exec, "%.pfx%")OR LIKE (process_exec, "%.p7b%")) 
4| stats count min(_time) as firstTime max(_time) as lastTime by argc process_exec dest 
5| `security_content_ctime(firstTime)` 
6| `security_content_ctime(lastTime)`
7| `linux_auditd_find_private_keys_filter`

Data Source

Name Platform Sourcetype Source Supported App
Linux Auditd Execve Linux icon Linux 'linux:audit' '/var/log/audit/audit.log' N/A

Macros Used

Name Value
linux_auditd sourcetype="linux:audit"
linux_auditd_find_private_keys_filter search *
linux_auditd_find_private_keys_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
T1552.004 Private Keys Credential Access
T1552 Unsecured Credentials Credential Access
KillChainPhase.EXPLOITAITON
NistCategory.DE_CM
Cis18Value.CIS_10
Rocke
Scattered Spider
TeamTNT

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 Notable Yes
Rule Title %name%
Rule Description %description%
Notable Event Fields user, dest
Creates Risk Event True
This configuration file applies to all detections of type TTP. These detections will use Risk Based Alerting and generate Notable Events.

Implementation

To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed

Known False Positives

Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.

Associated Analytic Story

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
A [$process_exec$] event occurred on host - [$dest$] to find private keys. 64 80 80
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

References

Detection Testing

Test Type Status Dataset Source Sourcetype
Validation Passing N/A N/A N/A
Unit Passing Dataset /var/log/audit/audit.log linux:audit
Integration ✅ Passing Dataset /var/log/audit/audit.log linux:audit

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: 2