Windows Java Spawning Shells
THIS IS A EXPERIMENTAL DETECTION
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.
Description
The following analytic identifies the process name of java.exe and w3wp.exe spawning a Windows shell. This is potentially indicative of exploitation of the Java application and may be related to current event CVE-2021-44228 (Log4Shell). The shells included in the macro are "cmd.exe", "powershell.exe". Upon triage, review parallel processes and command-line arguments to determine legitimacy.
- Type: TTP
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2023-01-23
- Author: Michael Haag, Splunk
- ID: 28c81306-5c47-11ec-bfea-acde48001122
Annotations
Kill Chain Phase
- Delivery
NIST
- DE.CM
CIS20
- CIS 10
CVE
ID | Summary | CVSS |
---|---|---|
CVE-2021-44228 | Apache Log4j2 2.0-beta9 through 2.15.0 (excluding security releases 2.12.2, 2.12.3, and 2.3.1) JNDI features used in configuration, log messages, and parameters do not protect against attacker controlled LDAP and other JNDI related endpoints. An attacker who can control log messages or log message parameters can execute arbitrary code loaded from LDAP servers when message lookup substitution is enabled. From log4j 2.15.0, this behavior has been disabled by default. From version 2.16.0 (along with 2.12.2, 2.12.3, and 2.3.1), this functionality has been completely removed. Note that this vulnerability is specific to log4j-core and does not affect log4net, log4cxx, or other Apache Logging Services projects. | 9.3 |
CVE-2022-47966 | Multiple Zoho ManageEngine on-premise products, such as ServiceDesk Plus through 14003, allow remote code execution due to use of Apache xmlsec (aka XML Security for Java) 1.4.1, because the xmlsec XSLT features, by design in that version, make the application responsible for certain security protections, and the ManageEngine applications did not provide those protections. | None |
Search
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| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=java.exe OR Processes.parent_process_name=w3wp.exe `windows_shells` by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
| `drop_dm_object_name(Processes)`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `windows_java_spawning_shells_filter`
Macros
The SPL above uses the following Macros:
windows_java_spawning_shells_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
- Processes.dest
- Processes.user
- Processes.parent_process_name
- Processes.parent_process
- Processes.original_file_name
- Processes.process_name
- Processes.process
- Processes.process_id
- Processes.parent_process_path
- Processes.process_path
- Processes.parent_process_id
How To Implement
To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. Restrict the analytic to publicly facing endpoints to reduce false positives. Add any additional identified web application process name to the query. Add any further Windows process names to the macro (ex. LOLBins) to further expand this query.
Known False Positives
Filtering may be required on internal developer build systems or classify assets as web facing and restrict the analytic based on that.
Associated Analytic Story
RBA
Risk Score | Impact | Confidence | Message |
---|---|---|---|
40.0 | 80 | 50 | An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ spawning a Windows shell, potentially indicative of exploitation. |
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
Reference
- https://blog.netlab.360.com/ten-families-of-malicious-samples-are-spreading-using-the-log4j2-vulnerability-now/
- https://gist.github.com/olafhartong/916ebc673ba066537740164f7e7e1d72
- https://www.horizon3.ai/manageengine-cve-2022-47966-technical-deep-dive/
- https://github.com/horizon3ai/CVE-2022-47966/blob/3a51c6b72ebbd87392babd955a8fbeaee2090b35/CVE-2022-47966.py
- https://blog.viettelcybersecurity.com/saml-show-stopper/
- https://www.horizon3.ai/manageengine-cve-2022-47966-iocs/
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: 2