Oinone Pamirs appConfigQuery PamirsParserConfig.java JsonUtils.parseMap deserialization

A vulnerability was identified in Oinone Pamirs up to 7.2.0. This affects the function JsonUtils.parseMap of the file PamirsParserConfig.java of the component appConfigQuery Interface. Such manipulation leads to deserialization. The attack can be launched 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.

Insecure deserialization

SEPPmail Secure Email Gateway before version 15.0.4 insecurely deserializes untrusted data, which can be reached from the new GINA UI and may allow unauthenticated remote attackers to execute code via a crafted serialized object.

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 weights_only=True parameter. This default behavior allows the deserialization of arbitrary Python objects via the Pickle module. A remote attacker can exploit this by providing a maliciously crafted model file, leading to arbitrary code execution on the victim's system when the file is loaded via the vulnerable method.

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 security controls. Python's pickle module is inherently dangerous for deserializing untrusted data, as it can execute arbitrary code during the deserialization process. A remote attacker can exploit this by providing a maliciously crafted pickle file, leading to arbitrary code execution on the victim's system when the file is loaded via the vulnerable method.

The snorkel library thru v0.10.0 contains an insecure deserialization vulnerability (CWE-502) in the MultitaskClassifier.load() method of the MultitaskClassifier class. The method loads model weight files using torch.load() without enabling the security-restrictive weights_only=True parameter. This default behavior allows the deserialization of arbitrary Python objects via the Pickle module. A remote attacker can exploit this by providing a maliciously crafted model file, leading to arbitrary code execution on the victim's system when the file is loaded via the vulnerable method.

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-restrictive weights_only=True parameter. This oversight allows the deserialization of arbitrary Python objects via the pickle module. A remote attacker can exploit this by providing a maliciously crafted checkpoint file, leading to arbitrary code execution in the context of the user running the script.

The _load_model() function in the neural_magic_training.py script of the optimate project in commit a6d302f912b481c94370811af6b11402f51d377f (2024-07-21) is vulnerable to insecure deserialization (CWE-502). When loading a model state dictionary from a state_dict.pt file via torch.load(), the function does not enable the weights_only=True security parameter. This allows the deserialization of arbitrary Python objects through the Pickle module. A remote attacker can exploit this by providing a maliciously crafted state_dict.pt file within a directory specified via the --model argument, leading to arbitrary code execution during the deserialization process on the victim's system.

The _load_model() function in the neural_magic_training.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 argument, the function loads the file using torch.load() without enabling the weights_only=True security parameter. This allows the deserialization of arbitrary Python objects through the Pickle module. A remote attacker can exploit this by providing a maliciously crafted model file, leading to arbitrary code execution during deserialization on the victim's system.

Deserialization of Untrusted Data in GitLab

GitLab has remediated an issue in GitLab EE affecting all versions from 11.9 before 18.9.7, 18.10 before 18.10.6, and 18.11 before 18.11.3 that could have allowed an unauthenticated user to cause denial of service by uploading a specially crafted file due to improper validation.

DataHub OIDC REDIRECT_URL Cookie Deserialization Vulnerability

DataHub is an open-source metadata platform. Prior to 1.5.0.3, The DataHub frontend (datahub-frontend-react) deserializes attacker-controlled Java objects from the REDIRECT_URL HTTP cookie during the OIDC callback flow, with no integrity protection (no HMAC, no encryption). This is a Deserialization of Untrusted Data vulnerability (CWE-502) affecting the GET /callback/oidc endpoint. Successful exploitation requires a valid user account in the configured OIDC identity provider This vulnerability is fixed in 1.5.0.3.

Insecure Deserialization in PyTorch-Lightning Leads to Remote Code Execution

PyTorch-Lightning versions 2.6.0 and earlier contain an insecure deserialization vulnerability (CWE-502) in the checkpoint loading mechanism. The LightningModule.load_from_checkpoint() method, which is commonly used to load saved model states, internally calls torch.load() without setting the security-restrictive weights_only=True parameter. This default behavior allows the deserialization of arbitrary Python objects via the Pickle module. A remote attacker can exploit this by providing a maliciously crafted checkpoint file, leading to arbitrary code execution on the victim's system when the file is loaded.

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 weights_only=True parameter. This default behavior allows the deserialization of arbitrary Python objects via the pickle module. An attacker can exploit this by providing a maliciously crafted PyTorch model file, leading to arbitrary code execution on the system hosting the Ludwig model server.

Microsoft SharePoint Server Remote Code Execution Vulnerability

Deserialization of untrusted data in Microsoft Office SharePoint allows an authorized attacker to execute code over a network.

Microsoft SharePoint Server Remote Code Execution Vulnerability

Deserialization of untrusted data in Microsoft Office SharePoint allows an authorized attacker to execute code over a network.

Microsoft SharePoint Server Remote Code Execution Vulnerability

Deserialization of untrusted data in Microsoft Office SharePoint allows an authorized attacker to execute code over a network.

Microsoft SharePoint Server Remote Code Execution Vulnerability

Deserialization of untrusted data in Microsoft Office SharePoint allows an authorized attacker to execute code over a network.

Microsoft SharePoint Server Remote Code Execution Vulnerability

Deserialization of untrusted data in Microsoft Office SharePoint allows an authorized attacker to execute code over a network.

Adobe Connect | Deserialization of Untrusted Data (CWE-502)

Adobe Connect versions 2025.9.15, 2025.8.157 and earlier are affected by a Deserialization of Untrusted Data vulnerability that could result in arbitrary code execution in the context of the current user. An attacker could exploit this vulnerability to execute arbitrary code. Exploitation of this issue requires user interaction in that a victim must visit a maliciously crafted URL or interact with a compromised web page. Scope is changed.

The Adversarial Robustness Toolbox (ART) thru 1.20.1 contains an insecure deserialization vulnerability (CWE-502) in its Kubeflow component's model loading functionality. When loading model weights from a file (e.g., model.pt) during robustness evaluation, the code uses torch.load() without the security-restrictive weights_only=True parameter. This allows the deserialization of arbitrary Python objects via the Pickle module. An attacker can exploit this by uploading a maliciously crafted model file to an object storage location referenced by the pipeline, or by controlling the model_id parameter to point to such a file. When the pipeline loads the model, the malicious payload is executed, leading to remote code execution.

BIG-IP and BIG-IQ Configuration utility vulnerability

An authenticated remote code execution vulnerability through undisclosed vectors exists in the BIG-IP and BIG-IQ Configuration utility.  Note: Software versions which have reached End of Technical Support (EoTS) are not evaluated.