Export limit exceeded: 409343 CVEs match your query. Please refine your search to export 10,000 CVEs or fewer.

Export limit exceeded: 409343 CVEs match your query. Please refine your search to export 10,000 CVEs or fewer.

Export limit exceeded: 409343 CVEs match your query. Please refine your search to export 10,000 CVEs or fewer.

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Search Results (3 CVEs found)

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CVE Vendors Products Updated CVSS v3.1
CVE-2026-104103 1 Ludwig-ai 1 Ludwig 2026-10-17 8.1 High
Ludwig is a low-code framework for building custom LLMs, neural networks, and other AI models. Prior to 0.17.8, ludwig.datasets.archives.extract_archive() validated tar member names before calling TarFile.extractall() but did not validate symlink members or hard-link members. A crafted archive could create a link that escaped the extraction directory and then write a later member through that link, placing attacker-controlled content at an arbitrary filesystem path with the extracting process's privileges. The same containment check used os.path.commonprefix, so a sibling path sharing the destination's character prefix could also pass. The dataset-loading path can download and extract configured archive URLs, but exploitation requires a user to process an archive from a source controlled or compromised by the attacker on supported Python 3.12 and 3.13 runtimes. Python 3.14 is not affected because its default tar data filter rejects escaping links. This issue is fixed in version 0.17.8.
CVE-2026-31238 1 Ludwig-ai 1 Ludwig 2026-05-14 9.8 Critical
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.
CVE-2026-31237 1 Ludwig-ai 1 Ludwig 2026-05-14 9.8 Critical
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.