| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| 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 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. |
| PyTorch Lightning is a deep learning framework to pretrain and finetune AI models. Versions 2.6.2 and 2.6.2 have introduced functionality consistent with a credential harvesting mechanism. |
| 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. |
| WWW::Mechanize::Cached versions before 2.00 for Perl deserialize cached HTTP responses from a world-writable on-disk cache, enabling local response forgery and code execution.
With no explicit cache backend, WWW::Mechanize::Cached constructs a default Cache::FileCache under /tmp/FileCache without overriding the backend's documented directory_umask of 000, so the cache root and its subdirectories are created mode 0777 with no sticky bit. Cache entries are named by sha1_hex of the request and read back through Storable::thaw on the next cache hit.
A local attacker with write access to the cache tree can replace a victim's cache entry for a known URL with an arbitrary frozen HTTP::Response blob, causing the victim's next get() of that URL to return attacker controlled response bytes. Because the bytes are passed to Storable::thaw, a victim process that has loaded any class with a side-effectful STORABLE_thaw, DESTROY, or overload hook can be escalated to arbitrary code execution. |
| A supply chain attack compromised the official installation packages of DAEMON Tools Lite (Windows versions 12.5.0.2421 through 12.5.0.2434), distributed from the legitimate website daemon-tools.cc between approximately April 8, 2026, and May 5, 2026. Attackers gained unauthorized access to the vendor's (AVB Disc Soft) build or distribution infrastructure and trojanized three binaries: DTHelper.exe, DiscSoftBusServiceLite.exe, and DTShellHlp.exe. These files were digitally signed with the legitimate AVB Disc Soft code-signing certificate, allowing the malicious installers to appear trustworthy and bypass signature-based detection. |
| 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. |
| The mamba language model framework thru 2.2.6 is vulnerable to insecure deserialization (CWE-502) when loading pre-trained models from HuggingFace Hub. The MambaLMHeadModel.from_pretrained() method uses torch.load() to load the pytorch_model.bin weight file without enabling 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 publishing a malicious model repository on HuggingFace Hub. When a victim loads a model from this repository, arbitrary code is executed on the victim's system in the context of the mamba process. |
| The CosyVoice project thru commit 6e01309e01bc93bbeb83bdd996b1182a81aaf11e (2025-30-21) contains an insecure deserialization vulnerability (CWE-502) in its model loading process. When loading model files (.pt) from a user-specified directory (via the --model_dir argument), 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 providing a maliciously crafted model directory containing .pt files with embedded pickle payloads. When a victim loads this directory using CosyVoice's web interface, the malicious payload is executed, leading to remote code execution on the victim's system. |
| Horovod thru 0.28.1 contains an insecure deserialization vulnerability (CWE-502) in its KVStore HTTP server component. The KVStore server, used for distributed task coordination, lacks authentication and authorization controls, allowing any remote attacker to write arbitrary data via HTTP PUT requests. When a Horovod worker reads data from the KVStore (via HTTP GET), it deserializes the data using cloudpickle.loads() without verifying its source or integrity. An attacker can exploit this by sending a malicious pickle payload to the server before the legitimate data is written, causing the victim worker to deserialize and execute arbitrary code, leading to remote code execution. |
| 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 _augment_images_worker() method without any safety checks. An attacker who can influence the data placed into this queue (e.g., through social engineering, malicious input scripts, or a compromised shared queue) can provide a malicious pickle payload. When deserialized, this payload can execute arbitrary code in the context of the worker process, leading to remote or local code execution depending on the deployment scenario. |
| 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 pandas.read_pickle() without any validation or security restrictions. This allows the deserialization of arbitrary Python objects via the unsafe pickle module. A remote attacker can exploit this by providing a maliciously crafted pickle file, leading to arbitrary code execution on the system running the Ludwig prediction. |
| 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. |
| On 2026-05-11, between approximately 19:20 and 19:26 UTC, 84 malicious versions across 42 @tanstack/* packages were published to the npm registry. The publishes were authenticated via the legitimate GitHub Actions OIDC trusted-publisher binding for TanStack/router, but the publish workflow itself was not modified. The attacker chained three known vulnerability classes — a pull_request_target "Pwn Request" misconfiguration, GitHub Actions cache poisoning across the fork↔base trust boundary, and runtime memory extraction of the OIDC token from the Actions runner process — to publish credential-stealing malware under a trusted identity. Each affected package received exactly two malicious versions, published a few minutes apart. |
| 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. |
| Deserialization of untrusted data in Microsoft Office SharePoint allows an authorized attacker to execute code over a network. |
| Deserialization of untrusted data in Microsoft Office SharePoint allows an authorized attacker to execute code over a network. |
| Deserialization of untrusted data in Microsoft Office SharePoint allows an authorized attacker to execute code over a network. |
