In Splunk AI Toolkit versions below 6.0.1, a user who holds the "power" Splunk role could modify app-provided scheduled searches to run arbitrary Search Processing Language (SPL) using the permissions of the search owner, which could allow access to all relevant data and affect system integrity. The vulnerability is possible because Splunk AI Toolkit gives the "power" Splunk role permission to modify scheduled searches that run using the permissions of the search owner.

In Splunk AI Toolkit versions below 6.0.0, a user who holds the "power" Splunk role could execute arbitrary code on the Splunk server by loading a model file containing crafted sparse matrix data. The deserialization of untrusted data is possible because a model codec in Splunk AI Toolkit deserializes sparse matrix data without guarding against embedded pickle content. For more information see Troubleshoot the Splunk Machine Learning Toolkit (https://help.splunk.com/en/splunk-cloud-platform/apply-machine-learning/machine-learning-toolkit-user-guide/5.5.0/troubleshooting-mltk/troubleshoot-the-splunk-machine-learning-toolkit) in the Splunk documentation.

In Splunk AI Toolkit versions below 6.0.0, a low-privileged user who does not hold the "admin" or "power" Splunk roles could start, stop, and configure containers, and read or modify connection and configuration data through the Representational State Transfer (REST) API. The missing authorization is possible because multiple REST API handlers in Splunk AI Toolkit do not enforce authorization checks. For more information see Troubleshoot the Splunk Machine Learning Toolkit (https://help.splunk.com/en/splunk-cloud-platform/apply-machine-learning/machine-learning-toolkit-user-guide/5.5.0/troubleshooting-mltk/troubleshoot-the-splunk-machine-learning-toolkit) in the Splunk documentation.

In Splunk AI Toolkit versions below 6.0.0, a user who can upload models could overwrite a model being uploaded by another user by sending a concurrent upload request for the same model name, causing the resulting model lookup entry to reference attacker-controlled content. The race condition is possible because Splunk AI Toolkit does not verify that the uploaded content belongs to the request that creates the model lookup entry. For more information see Troubleshoot the Splunk Machine Learning Toolkit (https://help.splunk.com/en/splunk-cloud-platform/apply-machine-learning/machine-learning-toolkit-user-guide/5.5.0/troubleshooting-mltk/troubleshoot-the-splunk-machine-learning-toolkit) in the Splunk documentation.

In Splunk AI Toolkit versions below 6.0.0, a user who does not hold the "admin" or "power" Splunk roles could obtain predictable or default credentials for connected container services. The use of hard-coded credentials is possible because Splunk AI Toolkit generates or stores credentials for connected container services using predictable or hard-coded default values. For more information see Connections tab in the AI Toolkit (https://help.splunk.com/en/splunk-cloud-platform/apply-machine-learning/use-ai-toolkit/5.7.2/ai-toolkit-commands-macros-and-visualizations/connections-tab-in-the-ai-toolkit) in the Splunk documentation.

In Splunk AI Toolkit versions below 6.0.1, a user who does not hold the "admin" or "power" Splunk roles could delete the experiment history of another user without permission through the Representational State Transfer (REST) API. The vulnerability is possible because Splunk AI Toolkit deletes experiment history before it verifies that the user can delete the associated experiment. For more information see Experiment Assistants (https://help.splunk.com/en/splunk-cloud-platform/apply-machine-learning/use-ai-toolkit/5.6.4/experiment-assistants) in the Splunk documentation.

In Splunk AI Toolkit versions below 6.0.0, a user who holds the "power" Splunk role could access and delete all relevant data in experiment history, including data associated with other users. The vulnerability is possible because Splunk AI Toolkit does not preserve the trusted experiment scope when it processes caller-controlled query values before accessing restricted history data. For more information see Experiment Assistants (https://help.splunk.com/en/splunk-cloud-platform/apply-machine-learning/use-ai-toolkit/5.6.4/experiment-assistants) in the Splunk documentation.

In Splunk AI Toolkit versions below 6.0.0, a user that holds a role with the schedulesearch capability could cause a scheduled search to load and deserialize a model file through the apply search command. The improper access control is possible because Splunk AI Toolkit does not mark the apply search command as risky. For more information see Troubleshoot the AI Toolkit (https://help.splunk.com/en/splunk-enterprise/apply-machine-learning/use-ai-toolkit/5.7.3/troubleshooting-the-ai-toolkit/troubleshoot-the-ai-toolkit) in the Splunk documentation.

In Splunk AI Toolkit versions below 5.7.3, a low-privileged user that does not hold the 'admin' or 'power' roles could access confidential data that was restricted through srchFilter configurations on custom roles.<br><br>The app contains an authorize.conf configuration file with a srchFilter entry that modifies the built-in ‘user’ role. Because the Splunk platform combines inherited search filters with the OR SPL operator, the injected filter overrides more restrictive filters on child roles.

In Splunk AI Toolkit versions below 5.7.4, a user who holds the "admin" Splunk role could execute arbitrary OS commands on the host running the Splunk Enterprise instance.

The vulnerability is possible because of an unsafe shell execution pattern in the btool configuration helper, which constructs OS command strings from dynamic parameters without disabling shell interpretation.

In Splunk AI Toolkit versions below 5.7.4, a low-privileged user that does not hold the "admin" or "power" Splunk roles could cause the Splunk AI Toolkit to make outbound requests over HTTP to a server that an attacker controls, which could allow for data exfiltration.

The vulnerability exists because of an insecure default domain allowlist in the Splunk AI Toolkit, which does not restrict outbound AI agent requests to approved external domains.