Pytorch
CVE-2025-1945
CRITICAL
GHSA-w8jq-xcqf-f792
Vulnerability Assessment
| Weakness |
CWE
OWASP Top 10:2025
A08:2025 - Software or Data Integrity Failures
ASVS 5.0
V15 - Secure Coding and Architecture Data authenticity verification
|
| Impact | CVSS 9.8 (Critical) with network vector, low complexity, no privileges or interaction required. EPSS probability 0.31%. A public PoC exists, significantly increasing real-world exploitation risk. Any ML pipeline that scans models with PickleScan before loading is vulnerable. |
| Exploit ScenarioDerived | An attacker publishes a backdoored PyTorch model to a public model hub. The archive passes PickleScan validation but contains a pickle payload that executes a reverse shell when loaded by a researcher or CI/CD pipeline using torch.load(). |
Get an e-mail when this CVE is exploited, patched or re-scored. Free account.
Alert meAnalysisAIv1 – Mar 12, 2026
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.
Earlier versions of this analysis (1)
PickleScan before 0.0.23 fails to detect malicious pickle payloads hidden in PyTorch archives via ZIP flag bit manipulation, enabling code execution on model load.
RemediationAI
Upgrade PickleScan to 0.0.23 or later. Consider using torch.load() with weights_only=True where possible. Audit existing model archives in your pipelines.
Affected Packages
Source: OSV (Open Source Vulnerabilities)
Affects your stack
Affects 1 package in pypi
Checking this CVE against your own stacks and servers is part of the Basic plan and up.
Technical ContextAI · show details
The vulnerability exploits a discrepancy between how PickleScan parses ZIP archives and how PyTorch extracts them. By toggling specific bits in ZIP local file headers, an attacker crafts archives where malicious .pkl files are invisible to the scanner but fully functional when loaded by torch.load(). This is a classic parser differential vulnerability (CWE-345).
Know when Pytorch gets its next vulnerability
- Watch products and get an email when something new is published
- Check this CVE against your own stack and servers
- Exploit and PoC references, threat intelligence from 22 sources
No credit card · 7-day full trial
Severity by source
AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
Primary rating from NVD · only source for this CVE.
CVSS VectorNVD
Lifecycle Timeline
3DescriptionCVE.org
picklescan before 0.0.23 fails to detect malicious pickle files inside PyTorch model archives when certain ZIP file flag bits are modified. By flipping specific bits in the ZIP file headers, an attacker can embed malicious pickle files that remain undetected by PickleScan while still being successfully loaded by PyTorch's torch.load(). This can lead to arbitrary code execution when loading a compromised model.
Executive ViewAI
CVE-2025-1945 presents critical security risk rated CVSS 9.8. Successful exploitation could critically impact the confidentiality, integrity, and availability of affected systems. Proof-of-concept code is publicly available, increasing the risk of exploitation. A patch is available and should be applied immediately given the critical severity.
Apply vendor patch. If immediate patching is not feasible: (1) Implement network-level access controls to limit exposure; (2) Enable enhanced monitoring and alerting; (3) Apply principle of least privilege.
Threat Intelligence Overview
NVD
CVE.org
EPSS
How this decision was reached
The engine reads each signal below and applies a fixed list of rules from top to bottom. The first rule that matches sets the recommended action – like a statement, every line is shown.
What every action level means
SSVC AssessmentENISA
Priority Score
Attack Technique
Authentication bypass attacks exploit flaws in the verification mechanisms that control access to systems and applications.
Insecure deserialization occurs when an application converts serialized data (a stream of bytes representing an object's state) back into a living object without proper validation.
Remote Code Execution represents the critical moment when an attacker successfully runs arbitrary code on a target system without physical access.
TorchServe is a tool for serving and scaling PyTorch models in production. Rated critical severity (CVSS 9.8), this vuln
In PyTorch <=2.4.1, the RemoteModule has Deserialization RCE. Rated critical severity (CVSS 9.8), this vulnerability is
In PyTorch before trunk/89695, torch.jit.annotations.parse_type_line can cause arbitrary code execution because eval is
PyTorch is a Python package that provides tensor computation. [CVSS 8.8 HIGH]
A vulnerability in lightning-ai/pytorch-lightning version 2.3.2 allows an attacker to cause a denial of service by sendi
Out-of-bounds heap read in PyTorch torchvision's native GIF decoder (versions through 0.28.0) lets attackers who supply
picklescan before 0.0.23 is vulnerable to a ZIP archive manipulation attack that causes it to crash when attempting to e
TorchServe is a flexible and easy-to-use tool for serving and scaling PyTorch models in production. Rated critical sever
PyTorch is a Python package that provides tensor computation with strong GPU acceleration and deep neural networks built
NVIDIA APEX for Linux contains a deserialization of untrusted data vulnerability that affects environments using PyTorch
A vulnerability, which was classified as problematic, has been found in PyTorch 2.6.0+cu124. Rated medium severity (CVSS
A vulnerability, which was classified as problematic, has been found in PyTorch 2.6.0.cuda.memory.caching_allocator_dele
Exploit-DB