CVE-2026-116103 (GCVE-0-2026-116103)
Vulnerability from cvelistv5 – Published: 2026-10-26 00:45 – Updated: 2026-10-26 00:45 Unsupported When Assigned
VLAI
EPSS
VEX
Title
Sony Neural Network Libraries Encode Param encode_decode_param.py load_param_in_txt code injection
Summary
A vulnerability was identified in Sony Neural Network Libraries up to 1.39.0. This affects the function load_param_in_txt of the file python/src/nnabla/utils/cli/encode_decode_param.py of the component Encode Param Command Handler. Such manipulation leads to code injection. It is possible to launch the attack remotely. The exploit is publicly available and might be used. The vendor was contacted early about this disclosure but did not respond in any way. This vulnerability only affects products that are no longer supported by the maintainer.
Severity
Assigner
References
5 references
| URL | Tags |
|---|---|
| https://vuldb.com/vuln/426171 | vdb-entrytechnical-description |
| https://vuldb.com/vuln/426171/cti | signaturepermissions-required |
| https://vuldb.com/cve/CVE-2026-116103 | third-party-advisory |
| https://vuldb.com/submit/1003039 | third-party-advisory |
| https://github.com/3em0/cve_repo/blob/main/2026/N… | exploit |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Sony | Neural Network Libraries |
Affected:
1.0
Affected: 1.1 Affected: 1.2 Affected: 1.3 Affected: 1.4 Affected: 1.5 Affected: 1.6 Affected: 1.7 Affected: 1.8 Affected: 1.9 Affected: 1.10 Affected: 1.11 Affected: 1.12 Affected: 1.13 Affected: 1.14 Affected: 1.15 Affected: 1.16 Affected: 1.17 Affected: 1.18 Affected: 1.19 Affected: 1.20 Affected: 1.21 Affected: 1.22 Affected: 1.23 Affected: 1.24 Affected: 1.25 Affected: 1.26 Affected: 1.27 Affected: 1.28 Affected: 1.29 Affected: 1.30 Affected: 1.31 Affected: 1.32 Affected: 1.33 Affected: 1.34 Affected: 1.35 Affected: 1.36 Affected: 1.37 Affected: 1.38 Affected: 1.39.0 cpe:2.3:a:sony:neural_network_libraries:*:*:*:*:*:*:*:* |
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
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The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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