Focus on lightningai vulnerabilities and metrics.
Last updated: 21 May 2026, 22:25 UTC
This page consolidates all known Common Vulnerabilities and Exposures (CVEs) associated with lightningai. We track both calendar-based metrics (using fixed periods) and rolling metrics (using gliding windows) to give you a comprehensive view of security trends and risk evolution. Use these insights to assess risk and plan your patching strategy.
For a broader perspective on cybersecurity threats, explore the comprehensive list of CVEs by vendor and product. Stay updated on critical vulnerabilities affecting major software and hardware providers.
Total lightningai CVEs: 8
Earliest CVE date: 23 Dec 2021, 18:15 UTC
Latest CVE date: 14 May 2026, 15:16 UTC
Latest CVE reference: CVE-2026-44484
30-day Count (Rolling): 2
365-day Count (Rolling): 2
Calendar-based Variation
Calendar-based Variation compares a fixed calendar period (e.g., this month versus the same month last year), while Rolling Growth Rate uses a continuous window (e.g., last 30 days versus the previous 30 days) to capture trends independent of calendar boundaries.
Month Variation (Calendar): 0%
Year Variation (Calendar): -50.0%
Month Growth Rate (30-day Rolling): 0.0%
Year Growth Rate (365-day Rolling): -50.0%
Average CVSS: 2.1
Max CVSS: 10.0
Critical CVEs (≥9): 1
| Range | Count |
|---|---|
| 0.0-3.9 | 6 |
| 4.0-6.9 | 1 |
| 7.0-8.9 | 0 |
| 9.0-10.0 | 1 |
These are the five CVEs with the highest CVSS scores for lightningai, sorted by severity first and recency.
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.
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.
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 `LightningApp`. This issue occurs due to improper handling of unexpected state values, which results in the server shutting down.
In lightning-ai/pytorch-lightning version 2.3.2, a vulnerability exists in the `LightningApp` when running on a Windows host. The vulnerability occurs at the `/api/v1/upload_file/` endpoint, allowing an attacker to write or overwrite arbitrary files by providing a crafted filename. This can lead to potential remote code execution (RCE) by overwriting critical files or placing malicious files in sensitive locations.
A vulnerability in the /v1/runs API endpoint of lightning-ai/pytorch-lightning v2.2.4 allows attackers to exploit path traversal when extracting tar.gz files. When the LightningApp is running with the plugin_server, attackers can deploy malicious tar.gz plugins that embed arbitrary files with path traversal vulnerabilities. This can result in arbitrary files being written to any directory in the victim's local file system, potentially leading to remote code execution.
A remote code execution (RCE) vulnerability exists in the lightning-ai/pytorch-lightning library version 2.2.1 due to improper handling of deserialized user input and mismanagement of dunder attributes by the `deepdiff` library. The library uses `deepdiff.Delta` objects to modify application state based on frontend actions. However, it is possible to bypass the intended restrictions on modifying dunder attributes, allowing an attacker to construct a serialized delta that passes the deserializer whitelist and contains dunder attributes. When processed, this can be exploited to access other modules, classes, and instances, leading to arbitrary attribute write and total RCE on any self-hosted pytorch-lightning application in its default configuration, as the delta endpoint is enabled by default.
Code Injection in GitHub repository pytorchlightning/pytorch-lightning prior to 1.6.0.
pytorch-lightning is vulnerable to Deserialization of Untrusted Data