CVE-2026-112086 in ESPnet
Summary
by MITRE • 10/19/2026
A vulnerability was found in ESPnet up to 202610.post2. This affects the function build_model of the file espnet3/publication/inference_model.py of the component Inference Model Handler. The manipulation of the argument _target_ results in deserialization. It is possible to launch the attack remotely. The exploit has been made public and could be used. The patch is identified as f217b24f06de55e332cab10231469d5890dbbe00. It is best practice to apply a patch to resolve this issue.
Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.
Analysis
by VulDB Data Team • 10/19/2026
The vulnerability identified in ESPnet versions up to 202610.post2 represents a critical security flaw within the Inference Model Handler component, specifically located in the build_model function of the espnet3/publication/inference_model.py file. This issue stems from improper handling of the target argument during model initialization processes. The core technical defect is an insecure deserialization vulnerability where user-supplied input passed via the target parameter is directly interpreted by Python's serialization mechanisms without adequate validation or sanitization. In typical deep learning frameworks like ESPnet, configuration files often specify which classes to instantiate for various components of a neural network architecture. When these configurations are loaded from untrusted sources, an attacker can craft malicious payloads that exploit this deserialization pathway.
This insecure deserialization allows for remote code execution (RCE) with the privileges of the process running the ESPnet service or script. By manipulating the target argument to point to arbitrary classes within Python's standard library or installed packages, such as os.system or subprocess.Popen, an attacker can execute system commands on the host machine. The exploit has been publicly disclosed and is actively available for use by threat actors, significantly increasing the risk of exploitation in production environments where ESPnet services are exposed to external inputs or untrusted configuration files. This capability enables attackers to gain full control over the affected system, leading to potential data exfiltration, lateral movement within a network, or complete compromise of the underlying infrastructure hosting the speech processing models.
From an industry standards perspective, this vulnerability aligns with CWE-502, which describes Deserialization of Untrusted Data. The attack vector is classified under MITRE ATT&CK technique T1059, specifically Command and Scripting Interpreter, as it allows for arbitrary command execution through the manipulation of configuration parameters. Furthermore, because the exploit can be launched remotely without authentication if the service is exposed to untrusted networks or accepts user-defined model configurations via APIs, it falls under high-severity remote exploitation scenarios often associated with CWE-94 Improper Control of Generation of Code (Code Injection) when considering how the deserialized object leads to code execution.
The operational impact of this vulnerability is severe for organizations relying on ESPnet for speech recognition or synthesis tasks that involve loading models from external sources, such as model hubs, user uploads, or dynamic configuration endpoints. An attacker leveraging this flaw can bypass application-level security controls and execute arbitrary operating system commands. This could result in the theft of sensitive data processed by the service, disruption of availability through resource exhaustion or destructive commands, and potential pivoting to other systems within the organization's network perimeter. Given that ESPnet is often used in research and production environments where performance and flexibility are prioritized over strict security hardening, this vulnerability poses a significant risk if proper input validation is not implemented at the application layer.
To mitigate this risk, it is imperative to apply the patch identified as f217b24f06de55e332cab10231469d5890dbbe00 immediately. This update addresses the insecure deserialization by restricting or validating the classes that can be instantiated via the target argument, ensuring that only expected and safe model components are loaded. In addition to applying the patch, organizations should implement defense-in-depth strategies such as running ESPnet services in isolated containers with minimal privileges, using allowlists for configuration parameters, and avoiding direct deserialization of untrusted data. Network-level controls like firewalls can also help restrict access to inference endpoints from untrusted networks until the vulnerability is fully remediated across all instances.