PT-2026-41668

Name of the Vulnerable Software and Affected Versions SGLangs affected versions not specified Description The multimodal generation runtime scheduler's ROUTER socket binds to 0.0.0.0 by default. It contains a sink that calls the pickle.loads function on incoming messages, which can lead to remote...

CVE-2026-31222

The snorkel library thru v0.10.0 contains an insecure deserialization vulnerability CWE-502 in the Trainer.load method of the Trainer class. The method loads model checkpoint files using torch.load without enabling the security-restrictive weightsonly=True parameter. This default behavior allows...

CVE-2026-31223

The snorkel library thru v0.10.0 contains a critical insecure deserialization vulnerability CWE-502 in the BaseLabeler.load method of the BaseLabeler class. The method loads serialized labeler models using the unsafe pickle.load function on user-supplied file paths without any validation or...

CVE-2026-31219

The loadmodel function in the neuralmagictraining.py script of the optimate project in commit a6d302f912b481c94370811af6b11402f51d377f 2024-07-21 is vulnerable to insecure deserialization CWE-502. When a user provides a single model file path e.g., .pt or .pth via the --model command-line argumen...

CVE-2026-31237

The Ludwig framework thru 0.10.4 is vulnerable to insecure deserialization CWE-502 through its predict method. When a user provides a dataset file path to the predict method, the framework automatically determines the file format. If the file is a pickle .pkl file, it is loaded using...

CVE-2026-31238

The Ludwig framework thru 0.10.4 is vulnerable to insecure deserialization CWE-502 in its model serving component. When starting a model server with the ludwig serve command, the framework loads model weight files using torch.load without enabling the security-restrictive weightsonly=True...

CVE-2026-31235

The imgaug library thru 0.4.0 contains an insecure deserialization vulnerability in its BackgroundAugmenter class within the multicore.py module. The class uses Python's pickle module to deserialize data received via a multiprocessing queue in the augmentimagesworker method without any safety...

CVE-2026-8597 Missing integrity verification in Triton inference handler in Amazon SageMaker Python SDK

Missing integrity verification in the Triton inference handler in Amazon SageMaker Python SDK v2 before v2.257.2 and v3 before v3.8.0 might allow a remote authenticated actor to achieve code execution in inference containers via replacement of model artifacts in S3 with a specially crafted pickle...

CVE-2026-8597

Missing integrity verification in the Triton inference handler in Amazon SageMaker Python SDK v2 before v2.257.2 and v3 before v3.8.0 might allow a remote authenticated actor to achieve code execution in inference containers via replacement of model artifacts in S3 with a specially crafted pickle...

PickleFuzzer: A Case Study in Fuzzing for Discrepancies between Python Pickle Implementations

Python's native serialization protocol, pickle, is a powerful but insecure format for transferring untrusted data. It is frequently used, especially for saving machine learning models, despite known security challenges. While developers sometimes mitigate this risk by restricting imports during...

PT-2026-41118

Name of the Vulnerable Software and Affected Versions Amazon SageMaker Python SDK versions prior to 2.257.2 Amazon SageMaker Python SDK versions prior to 3.8.0 Description Missing integrity verification in the Triton inference handler allows a remote authenticated actor with S3 write access to th...

Amazon SageMaker Python SDK 安全漏洞

Amazon SageMaker Python SDK is a development toolkit provided by Amazon, Inc., for building, training, and deploying machine learning models. Versions of the Amazon SageMaker Python SDK prior to v2.257.2 and v3.8.0 contained security vulnerabilities. These vulnerabilities stemmed from a lack of...

CVE-2026-31214

The torch-checkpoint-shrink.py script in the ml-engineering project in commit 0099885db36a8f06556efe1faf552518852cb1e0 2025-20-27 contains an insecure deserialization vulnerability CWE-502. The script uses torch.load to process PyTorch checkpoint files .pt without enabling the security-restrictiv...

CVE-2026-31249

CosyVoice thru commit 6e01309e01bc93bbeb83bdd996b1182a81aaf11e 2025-30-21 contains an insecure deserialization vulnerability CWE-502 in its makeparquetlist.py data processing tool. The script loads PyTorch .pt files utterance embeddings, speaker embeddings, speech tokens using torch.load without...

CVE-2026-31251

CosyVoice thru commit 6e01309e01bc93bbeb83bdd996b1182a81aaf11e 2025-30-21 contains an insecure deserialization vulnerability CWE-502 in its gRPC server component. When the server starts, it loads the speech synthesis model from a user-specified directory using torch.load without enabling the...

CVE-2026-31250

CosyVoice thru commit 6e01309e01bc93bbeb83bdd996b1182a81aaf11e 2025-30-21 contains an insecure deserialization vulnerability CWE-502 in its averagemodel.py model averaging tool. The script loads PyTorch checkpoint files epoch.pt for model averaging using torch.load without enabling the...

CVE-2026-31252

CosyVoice thru commit 6e01309e01bc93bbeb83bdd996b1182a81aaf11e 2025-30-21 contains an insecure deserialization vulnerability CWE-502 in its model loading component. The framework uses torch.load to load model weight files e.g., llm.pt, flow.pt, hift.pt without enabling the security-restrictive...

Ludwig framework is vulnerable to insecure deserialization through its predict() method.

The Ludwig framework thru 0.10.4 is vulnerable to insecure deserialization CWE-502 through its predict method. When a user provides a dataset file path to the predict method, the framework automatically determines the file format. If the file is a pickle .pkl file, it is loaded using...

Ludwig framework is vulnerable to insecure deserialization in its model serving component

The Ludwig framework thru 0.10.4 is vulnerable to insecure deserialization CWE-502 in its model serving component. When starting a model server with the ludwig serve command, the framework loads model weight files using torch.load without enabling the security-restrictive weightsonly=True...

Deserialization of Untrusted Data

Overview ludwig is a Declarative machine learning: End-to-end machine learning pipelines using data-driven configurations. Affected versions of this package are vulnerable to Deserialization of Untrusted Data via the predict method. An attacker can execute arbitrary code by supplying a maliciousl...