CVE-2026-112086
Deferred Deferred - Pending Action

Deserialization Flaw in ESPnet Inference Model

Publication date: 2026-10-19

Last updated on: 2026-10-19

Assigner: VulDB

Description
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.

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Meta Information

Published
2026-10-19
Last Modified
2026-10-19
Generated
2026-10-19
AI Q&A
2026-10-19
EPSS Evaluated
N/A
NVD
CVE-2026-112086
EUVD
EUVD-2026-104089

Affected Vendors & Products

Showing 1 associated CPE
Vendor Product Version / Range
n/a ESPnet 202610.post2

Helpful Resources

Exploitability

CWE
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KEV
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CWE ID Description
CWE-20 The product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly.
CWE-502 The product deserializes untrusted data without sufficiently ensuring that the resulting data will be valid.

Attack-Flow Graph

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Executive Summary

CVE-2026-112086 is a vulnerability in ESPnet3's publication bundles that allows arbitrary code execution even when security checks are enabled. The issue occurs because Hydra's _target_ configurations are not properly validated. Attackers can bypass checks by using nested _target_ chains that reference already-installed callables like those in Python's standard library or ESPnet itself. This enables malicious bundles to execute arbitrary code, such as writing files or running system commands, without triggering safety mechanisms.

Detection Guidance

To detect this vulnerability, inspect ESPnet publication bundles for malicious _target_ configurations. Check for nested targets or references to dangerous functions like os.system or torch.load. Use commands like grep -r "_target_" /path/to/bundles to search for suspicious configurations. Review logs for model loading failures or unexpected code execution during inference.

Impact Analysis

If you use ESPnet3 with untrusted publication bundles, an attacker could exploit this vulnerability to execute arbitrary code on your system. This could lead to unauthorized file access, data theft, or system compromise. The vulnerability affects multiple entry points like InferenceModel.from_packed() and from_pretrained(), meaning attackers could target users loading models from untrusted sources.

Compliance Impact

This vulnerability could lead to unauthorized data access or modification, violating GDPR's data protection principles or HIPAA's security requirements for protected health information. If exploited, it may result in data breaches, unauthorized processing, or loss of data integrity, all of which are compliance violations requiring breach notifications and potential penalties.

Mitigation Strategies

Apply the patch from commit f217b24f06de55e332cab10231469d5890dbbe00 or update to a patched version. Disable untrusted bundle loading by setting trust_user_code=False. Restrict bundle sources to trusted repositories. Monitor for unusual activity in model loading processes.

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