Vulnerabilidades

A security vulnerability has been detected in DLR-RM stable-baselines3 up to 2.9.0. This affects the function PPO.load/load_replay_buffer/VecNormalize.load of the file save_util.py. Such manipulation leads to deserialization. It is possible to launch the attack remotely. The exploit has been disclosed publicly and may…

Kedro-Datasets provides data connectors for Kedro. From version 5.0.0 until 9.5.0, kedro_datasets_experimental.pytorch.PyTorchDataset in kedro-datasets loads .pt model files with torch.load without enforcing weights_only=True, and user-supplied load_args are silently dropped. On PyTorch versions earlier than 2.6, a…

Hugging Face PyTorch Image Models checkpoint Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Hugging Face PyTorch Image Models. User interaction is required to exploit this vulnerability in that the…

Protection mechanism failure for some Intel(R) LLM Library for PyTorch within Ring 3: User Applications may allow an escalation of privilege. Unprivileged software adversary with a privileged user combined with a low complexity attack may enable escalation of privilege. This result may potentially occur via local…

Deserialization of untrusted data for some Intel(R) Extension for PyTorch before version 2.8.0 within Ring 3: User Applications may allow an escalation of privilege. Unprivileged software adversary with an unauthenticated user combined with a low complexity attack may enable escalation of privilege. This result may…

Protection mechanism failure for some Intel(R) oneCCL Bindings for PyTorch before version v2.8.0 within Ring 3: User Applications may allow an escalation of privilege. Unprivileged software adversary with a privileged user combined with a low complexity attack may enable escalation of privilege. This result may…

Hugging Face peft's LoRA-GA and CorDA initialization modules (src/peft/tuners/lora/corda.py lines ~102 and ~163, and src/peft/tuners/lora/loraga.py line ~101) call torch.load on config-specified cache/covariance files without weights_only=True, bypassing peft's own safe-loading wrapper used elsewhere in the codebase.

A vulnerability in keras-team/keras version 3.15.0 allows unsafe deserialization of attacker-controlled PyTorch pickle data through the public `keras.layers.TorchModuleWrapper.from_config` method. This method invokes `torch.load(..., weights_only=False)` without requiring an explicit unsafe opt-in, such as a…

PyTorch Lightning through 2.6.5, fixed in commit d710d68, contains a remote code execution vulnerability in the _load_state function that imports and executes attacker-controlled module names from checkpoint _instantiator hyperparameters. Attackers can craft malicious checkpoint files that bypass weights_only=True…

A vulnerability has been found in yashbhalgat HashNeRF-pytorch up to 82885e698295982504eb6a26d060a6b2473e3706. Affected by this issue is the function torch.load of the file run_nerf.py of the component Checkpoint File Handler. The manipulation of the argument ckpt_path leads to deserialization. The attack must be…

picklescan before 0.0.28 fails to detect malicious pickle files that use torch.utils.data.datapipes.utils.decoder.basichandlers in reduce methods, allowing attackers to bypass safety checks. Remote attackers can embed undetected malicious code in pickle files that executes during deserialization, enabling remote code…

picklescan before 0.0.28 fails to detect malicious torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_expression function calls in pickle files. Attackers can embed undetected code in pickle files that executes remote code when loaded by victims.

picklescan before 0.0.28 fails to detect malicious pickle files that exploit torch._dynamo.guards.GuardBuilder.get function in reduce methods. Attackers can craft pickle files with embedded code that evades picklescan detection and executes arbitrary commands when loaded.

picklescan before 0.0.30 fails to detect malicious pickle files that invoke torch.utils.bottleneck.__main__.run_autograd_prof function. Attackers can embed undetected code in pickle files that executes during deserialization, enabling remote code execution.

picklescan before 0.0.30 fails to detect malicious pickle files using idlelib.run.Executive.runcode in reduce methods. Attackers can embed undetected code in pickle files that executes during pickle.load, enabling remote code execution in PyTorch models and supply chain attacks.

picklescan before 0.0.28 fails to detect malicious pickle files using torch.utils.collect_env.run function in reduce methods. Attackers can embed undetected code in pickle files that executes remote commands when loaded by victims.

picklescan before 0.0.28 fails to detect malicious torch.jit.unsupported_tensor_ops.execWrapper function calls embedded in pickle files. Attackers can craft malicious pickle files that bypass picklescan detection and execute arbitrary code when loaded via pickle.load().

picklescan before 1.0.3 contains a scanning bypass vulnerability in the scan_pytorch function that allows attackers to embed malicious magic numbers via dynamic eval using the __reduce__ trick. Attackers can craft malicious PyTorch payloads that evade picklescan detection while remaining executable, enabling arbitrary…

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 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…

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…

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…

The Adversarial Robustness Toolbox (ART) thru 1.20.1 contains a remote code execution vulnerability in its Kubeflow component. The robustness evaluation function for PyTorch models uses the unsafe eval() function to dynamically evaluate user-supplied strings for the LossFn and Optimizer parameters without any…

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…

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…