CVE-2026-76396 in AI Toolkit
Summary
by MITRE • 08/20/2026
In Splunk AI Toolkit versions below 6.0.0, a user that holds a role with the schedule_search 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.
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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 improper access control flaw that allows authenticated users with specific permissions to execute arbitrary code or cause denial of service through maliciously crafted model files. The core technical issue stems from the design of the apply search command, which is intended for legitimate machine learning operations but lacks sufficient safeguards against untrusted input during the deserialization process. When a user possessing the schedule_search capability initiates a scheduled search that utilizes this command, the system attempts to load and deserialize a specified model file without verifying its integrity or origin. This behavior creates an avenue for exploitation where an attacker can supply a maliciously constructed object graph within the model file. Upon execution, the deserialization mechanism processes these objects, potentially leading to remote code execution if the payload is designed to exploit vulnerabilities in the underlying Java Virtual Machine or associated libraries, or causing application crashes and resource exhaustion through denial of service conditions.
The root cause lies in Splunk AI Toolkit not marking the apply search command as risky within its internal security framework. In enterprise software development, commands that involve file system access, network communication, or object instantiation are typically classified with higher risk levels to trigger additional validation checks, sandboxing, or privilege restrictions. By failing to assign this elevated risk classification, the toolkit bypasses critical security controls that would otherwise prevent unprivileged users from invoking operations capable of loading arbitrary external resources. This oversight effectively grants any user with schedule_search privileges the ability to influence system behavior beyond their intended scope, violating the principle of least privilege and enabling lateral movement or persistence within the Splunk environment if combined with other vulnerabilities.
From an operational perspective, this vulnerability poses significant risks to organizations relying on Splunk for security information and event management alongside AI-driven analytics. An attacker who has gained access to a user account with schedule_search capabilities can exploit this flaw to compromise the integrity of the data processing pipeline. Successful exploitation could result in the exfiltration of sensitive log data processed by the system, manipulation of analytical outputs used for threat detection, or complete disruption of security monitoring services due to service crashes. The impact is particularly severe because scheduled searches often run with elevated privileges and at regular intervals, meaning a single successful exploit can lead to persistent compromise without requiring further user interaction after initial setup.
Mitigation strategies must focus on immediate remediation through software updates and compensating controls where upgrades are not immediately feasible. Organizations should upgrade Splunk AI Toolkit to version 6.0.0 or later, as this release addresses the improper access control by correctly classifying the apply search command with appropriate risk levels and implementing stricter validation for model file inputs. Until the patch is applied, administrators should restrict the schedule_search capability to only those users who absolutely require it, thereby reducing the attack surface available to potential adversaries. Additionally, input validation mechanisms can be strengthened at the application level by ensuring that any files referenced in search commands are sourced from trusted directories and verified against known good hashes or digital signatures. Monitoring for unusual patterns in scheduled searches, such as frequent references to external model paths or unexpected deserialization errors, can also aid in early detection of exploitation attempts.
This vulnerability aligns with CWE-269 Improper Privilege Management, specifically regarding the failure to enforce appropriate access controls on system functions that handle sensitive operations like file loading and object instantiation. It also relates closely to CWE-502 Deserialization of Untrusted Data, as the core flaw involves processing external input without adequate validation or sanitization before deserialization occurs. In terms of adversary tactics, this exploit maps to MITRE ATT&CK technique T1068 Exploitation for Privilege Escalation if used to gain higher system access, and potentially T1496 Resource Hijacking if leveraged for denial of service purposes. Understanding these mappings helps security teams prioritize response efforts based on established threat intelligence frameworks and industry best practices for securing enterprise analytics platforms against insider threats or compromised accounts.