Pytorch

Remote code execution in HuggingFace Transformers library allows arbitrary code execution via malicious checkpoint files. The `_load_rng_state()` method in the `Trainer` class calls `torch.load()` without the `weights_only=True` parameter, enabling deserialization attacks when PyTorch versions below 2.6 are used with torch>=2.2. An attacker can craft a malicious `rng_state.pth` checkpoint file that executes arbitrary code when loaded by an application using affected Transformers versions. The fix is available in version v5.0.0rc3, and no public exploit has been independently confirmed at time of analysis.

NVIDIA APEX for Linux contains a deserialization of untrusted data vulnerability that affects environments using PyTorch versions earlier than 2.6. An attacker with low privileges on an adjacent network can exploit this flaw to achieve code execution, denial of service, privilege escalation, data tampering, and information disclosure with scope change (CVSS 9.0 Critical). No KEV listing or public POC availability has been reported at this time.

A deserialization vulnerability exists in PyTorch 2.10.0 within the pt2 Loading Handler component, allowing local attackers with low privileges to achieve confidentiality, integrity, and availability impacts through untrusted data processing. The vulnerability (CWE-502) is confirmed to have a publicly available exploit and has been reported to the project via pull request PR#176791, though remediation status remains unclear. With a CVSS score of 5.3 and exploitation probability marked as probable (E:P), this represents a moderate real-world risk primarily affecting local development and deployment environments.

PyTorch is a Python package that provides tensor computation. [CVSS 8.8 HIGH]

vLLM is an inference and serving engine for large language models (LLMs). Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, low attack complexity.

An issue was discovered in PyTorch v2.5 and v2.7.1. Rated low severity (CVSS 3.3), this vulnerability is low attack complexity. Public exploit code available and no vendor patch available.

An issue in pytorch v2.7.0 can lead to a Denial of Service (DoS) when a PyTorch model consists of torch.Tensor.to_sparse() and torch.Tensor.to_dense() and is compiled by Inductor. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. This Uncontrolled Resource Consumption vulnerability could allow attackers to cause denial of service by exhausting system resources.

A buffer overflow occurs in pytorch v2.7.0 when a PyTorch model consists of torch.nn.Conv2d, torch.nn.functional.hardshrink, and torch.Tensor.view-torch.mv() and is compiled by Inductor, leading to a. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. This Uncontrolled Resource Consumption vulnerability could allow attackers to cause denial of service by exhausting system resources.

A Name Error occurs in pytorch v2.7.0 when a PyTorch model consists of torch.cummin and is compiled by Inductor, leading to a Denial of Service (DoS). Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity.

pytorch v2.8.0 was discovered to contain an integer overflow in the component torch.nan_to_num-.long(). Rated medium severity (CVSS 5.3), this vulnerability is remotely exploitable, no authentication required, low attack complexity. No vendor patch available.

A syntax error in the component proxy_tensor.py of pytorch v2.7.0 allows attackers to cause a Denial of Service (DoS). Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity.

pytorch v2.8.0 was discovered to display unexpected behavior when the components torch.rot90 and torch.randn_like are used together. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. No vendor patch available.

An issue in the component torch.linalg.lu of pytorch v2.8.0 allows attackers to cause a Denial of Service (DoS) when performing a slice operation. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. No vendor patch available.

PyTorch before 3.7.0 has a bernoulli_p decompose function in decompositions.py even though it lacks full consistency with the eager CPU implementation, negatively affecting nn.Dropout1d,. Rated medium severity (CVSS 5.3), this vulnerability is remotely exploitable, no authentication required, low attack complexity.

In PyTorch before 2.7.0, bitwise_right_shift produces incorrect output for certain out-of-bounds values of the "other" argument. Rated medium severity (CVSS 5.3), this vulnerability is remotely exploitable, no authentication required, low attack complexity. This Out-of-bounds Write vulnerability could allow attackers to write data beyond allocated buffer boundaries leading to code execution or crashes.

In PyTorch before 2.7.0, when torch.compile is used, FractionalMaxPool2d has inconsistent results. Rated medium severity (CVSS 5.3), this vulnerability is remotely exploitable, no authentication required, low attack complexity.

In PyTorch before 2.7.0, when inductor is used, nn.Fold has an assertion error. Rated medium severity (CVSS 5.3), this vulnerability is remotely exploitable, no authentication required, low attack complexity.

In PyTorch through 2.6.0, when eager is used, nn.PairwiseDistance(p=2) produces incorrect results. Rated medium severity (CVSS 5.3), this vulnerability is remotely exploitable, no authentication required, low attack complexity.

An Improper Input Validation vulnerability in the scanning logic of mmaitre314 picklescan versions up to and including 0.0.30 allows a remote attacker to bypass pickle files security checks by. Rated critical severity (CVSS 9.3), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.

A Regular Expression Denial of Service (ReDoS) vulnerability exists in the Hugging Face Transformers library, specifically in the `convert_tf_weight_name_to_pt_weight_name()` function. Rated medium severity (CVSS 5.3), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.

vLLM, an inference and serving engine for large language models (LLMs), has an issue in versions 0.6.5 through 0.8.4 that ONLY impacts environments using the `PyNcclPipe` KV cache transfer. Rated critical severity (CVSS 9.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.

A vulnerability was found in PyTorch 2.6.0+cu124. Rated medium severity (CVSS 4.8), this vulnerability is low attack complexity. No vendor patch available.

PyTorch is a Python package that provides tensor computation with strong GPU acceleration and deep neural networks built on a tape-based autograd system. Rated critical severity (CVSS 9.3), this vulnerability is remotely exploitable, no authentication required, low attack complexity. No vendor patch available.

A vulnerability, which was classified as problematic, was found in PyTorch 2.6.0. Rated medium severity (CVSS 4.8), this vulnerability is low attack complexity. Public exploit code available.

A vulnerability, which was classified as problematic, has been found in PyTorch 2.6.0.cuda.memory.caching_allocator_delete of the file c10/cuda/CUDACachingAllocator.cpp. Rated medium severity (CVSS 4.8), this vulnerability is low attack complexity. Public exploit code available and no vendor patch available.

A vulnerability classified as problematic has been found in PyTorch 2.6.0. Rated medium severity (CVSS 4.8), this vulnerability is low attack complexity. Public exploit code available and no vendor patch available.

A vulnerability classified as critical was found in PyTorch 2.6.0. Rated medium severity (CVSS 4.8), this vulnerability is low attack complexity. No vendor patch available.

A vulnerability classified as critical has been found in PyTorch 2.6.0. Rated medium severity (CVSS 4.8), this vulnerability is low attack complexity. No vendor patch available.

A vulnerability was found in PyTorch 2.6.0. Rated medium severity (CVSS 4.8), this vulnerability is low attack complexity. No vendor patch available.

A vulnerability was found in PyTorch 2.6.0. Rated medium severity (CVSS 4.8), this vulnerability is low attack complexity. No vendor patch available.

A vulnerability, which was classified as problematic, has been found in PyTorch 2.6.0+cu124. Rated medium severity (CVSS 4.8), this vulnerability is low attack complexity. Public exploit code available and no vendor patch available.

A vulnerability in lightning-ai/pytorch-lightning version 2.3.2 allows an attacker to cause a denial of service by sending an unexpected POST request to the `/api/v1/state` endpoint of. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.

In lightning-ai/pytorch-lightning version 2.3.2, a vulnerability exists in the `LightningApp` when running on a Windows host. Rated critical severity (CVSS 9.1), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.

In the latest version of pytorch/serve, the script 'upload_results_to_s3.sh' references the S3 bucket 'benchmarkai-metrics-prod' without ensuring its ownership or confirming its accessibility. Rated medium severity (CVSS 6.3), this vulnerability is remotely exploitable, no authentication required, low attack complexity. No vendor patch available.

A vulnerability was found in PyTorch 2.6.0+cu124. It has been rated as problematic. [CVSS 2.5 LOW]

A vulnerability was found in PyTorch 2.6.0+cu124. It has been declared as critical. [CVSS 5.0 MEDIUM]

PickleScan before 0.0.23 can be bypassed by flipping specific ZIP file header flag bits, allowing malicious pickle files to evade detection inside PyTorch model archives. An attacker can embed arbitrary code execution payloads that PickleScan misses but PyTorch's torch.load() still processes. A proof-of-concept exists and a patch is available in version 0.0.23.

picklescan before 0.0.23 is vulnerable to a ZIP archive manipulation attack that causes it to crash when attempting to extract and scan PyTorch model archives. By modifying the filename in the ZIP header while keeping the original filename in the directory listing, an attacker can make PickleScan raise a BadZipFile error. However, PyTorch's more forgiving ZIP implementation still allows the mod...