CVE-2023-25661: TensorFlow denial of service via invalid input to Convolution3DTranspose

Summary

CVE-2023-25661 affects TensorFlow versions prior to 2.11.1. A malicious invalid input to the `Convolution3DTranspose` function triggers a Check Failed crash in a model, enabling denial of service. Models containing this layer, deployed in ML applications or cloud services, are exposed, and an attacker must be able to supply input to a `Convolution3DTranspose` call.

Mitigation

Fixed in 2.11.1. Users are advised to upgrade to version 2.11.1. There are no known workarounds for this vulnerability.

Affected packages in the Exposure Registry

Matched by package name and ecosystem. Each entry shows whether the package delegates to a language model and how many tracked packages depend on it.

• tensorflowPyPINo LLM dependency detected · 26 tracked dependents

Related items

• LowCloakFEM: Agentic FEM Code Generation for Red-Teaming Computational Resource ExhaustionSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)
• MediumThe Rise of LLMJacking: How Threat Actors Monetize Compromised Cloud and Model ResourcesSimilar attack · Google Threat Intelligence
• InfoThe Blind Spots of Detection: Adversarial Perturbation Attacks against Watchdogs in Distributed LLM InferenceSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)
• HighCVE-2026-118761: Kubeflow Pipelines decompression bomb in pipeline archive uploadSimilar attack · NVD/CVE Database
• LowOpenCode User Hit with Hostile Backlash After Reporting Free AI LoopholeSimilar attack · Socket Blog