CVE-2026-76395 in AI Toolkit
Summary
by MITRE • 08/20/2026
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.
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Analysis
by VulDB Data Team • 08/20/2026
The vulnerability identified in Splunk AI Toolkit versions prior to 6.0.0 represents a critical security flaw rooted in improper handling of serialized data, specifically within the component responsible for processing sparse matrix model files. This issue allows an authenticated user with the power role on the Splunk platform to execute arbitrary code remotely or locally depending on the deployment context. The core technical deficiency lies in the deserialization mechanism used by the machine learning toolkit's codec when loading model artifacts. Instead of validating and sanitizing the input data structure, the system directly processes embedded pickle content within sparse matrix definitions. This lack of safeguards against untrusted data during the unpickling process creates a direct path for remote code execution attacks.
From an industry standards perspective, this vulnerability is classified under CWE-502, which denotes Deserialization of Untrusted Data. The attack vector leverages Python's pickle module, a common serialization format that inherently supports arbitrary object instantiation and method calls during the deserialization process. When an attacker crafts a sparse matrix file containing maliciously constructed pickle payloads, the Splunk AI Toolkit interprets these instructions as legitimate code to be executed upon loading. This aligns with ATT&CK technique T1059, specifically sub-technique 008 for Python Command Scripting Interpreter, where attackers use scripting languages to execute commands on a target system. The exploitation does not require complex buffer overflows or memory corruption exploits but relies entirely on the logical flaw of trusting serialized input without verification.
The operational impact of this vulnerability is severe due to the privilege level required and the capabilities granted by successful exploitation. While the attacker must possess the power role, which is a standard elevated permission set in Splunk environments often assigned to analysts or developers for creating dashboards and running searches, it does not typically imply full administrative control over the underlying operating system. However, once arbitrary code execution is achieved through this deserialization flaw, the consequences extend far beyond the application layer. The attacker can execute commands with the privileges of the Splunk service account, which often runs as a privileged user on the host machine. This enables lateral movement within the network, exfiltration of sensitive data indexed by Splunk, installation of persistent backdoors, and potential compromise of other systems accessible from the Splunk server's network segment.
Mitigation strategies must focus immediately on upgrading to Splunk AI Toolkit version 6.0.0 or later, where this deserialization flaw has been addressed through stricter input validation and safer serialization practices. In environments where immediate patching is not feasible due to operational constraints, administrators should enforce strict access controls limiting the power role to only those users who absolutely require it for their job functions. Additionally, implementing network segmentation to restrict direct access to Splunk management interfaces from untrusted networks can reduce the attack surface. Security monitoring solutions should be configured to detect anomalous processes spawned by the Splunk service and unusual outbound connections that may indicate successful exploitation of this vulnerability. Regular audits of installed apps and model files within the AI Toolkit environment are also recommended to ensure no malicious artifacts have been introduced into the system.