CVE-2026-100308 in GluonTS
Summary
by MITRE • 09/29/2026
Deserialization of untrusted data in the model loading component in Amazon GluonTS before 0.17.0 might allow context-dependent attackers to execute arbitrary operating system commands with the privileges of the loading process via a crafted serialized model directory.
To remediate this issue, users should upgrade to version 0.17.0 or later.
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Analysis
by VulDB Data Team • 09/29/2026
The vulnerability identified in Amazon GluonTS prior to version 0.17.0 represents a critical security flaw rooted in the improper handling of serialized data during the model loading process. This issue is classified under CWE-502, which denotes Deserialization of Untrusted Data. The core technical failure lies in the application's willingness to accept and deserialize arbitrary objects from unverified sources without sufficient validation or sanitization. When a user provides a crafted serialized model directory, the underlying Python deserialization mechanisms, typically involving libraries such as pickle, are invoked to reconstruct the object state. Because these mechanisms can execute code during the reconstruction phase, an attacker who controls the content of the serialized data can inject malicious payloads that result in arbitrary command execution on the host system with the privileges assigned to the GluonTS loading process.
From a tactical perspective, this vulnerability aligns with ATT&CK technique T1059, specifically Command and Scripting Interpreter, as it allows for the direct execution of operating system commands through the abuse of legitimate software functionality. The attack vector is context-dependent, meaning that an attacker must have some level of access to influence or provide the model directory being loaded by a victim process. This could occur in scenarios where users share custom models within a team environment, ingest models from external datasets, or when automated pipelines pull unverified artifacts for training and inference purposes. The severity is heightened because machine learning workflows often run with elevated privileges to manage large-scale data processing and resource allocation, thereby amplifying the potential impact of successful exploitation.
The operational impact of this vulnerability extends beyond simple code execution. Successful exploitation can lead to a complete compromise of the underlying infrastructure hosting the GluonTS instance. Attackers may use this foothold to exfiltrate sensitive training data, manipulate model outputs for adversarial purposes, or pivot further into internal networks if the compromised host has network access. In cloud environments like AWS, where GluonTS is frequently deployed via SageMaker or EC2 instances, this could result in unauthorized resource usage, financial loss through crypto-mining, or broader lateral movement across the account's resources. The trust boundary between data ingestion and code execution is effectively bypassed, turning a standard machine learning utility into an arbitrary command execution vector.
To remediate this security risk, organizations must immediately upgrade Amazon GluonTS to version 0.17.0 or later, where the deserialization logic has been hardened to prevent the execution of malicious payloads during object reconstruction. In addition to upgrading, it is imperative to implement strict input validation and allowlisting for any data sources involved in model loading. Developers should avoid using unsafe deserialization methods such as pickle.load on untrusted inputs whenever possible, opting instead for safer serialization formats like JSON or protocol buffers that do not support arbitrary code execution during parsing. Furthermore, adopting a principle of least privilege by running GluonTS processes with minimal required permissions can mitigate the impact if an exploitation attempt occurs. Regular security audits and static analysis tools configured to detect unsafe deserialization patterns should be integrated into the CI/CD pipeline to prevent similar vulnerabilities from being introduced in custom model implementations or extensions.