CVE-2026-104103: Path Traversal (CVSS 8.1)

Description

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.

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CVSS v3.1 Score

8.1

HIGH

CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:H/A:H

EPSS — Exploit Prediction

0.0049

Probability of exploitation

0.41%

Percentile rank

EPSS estimates the probability that this vulnerability will be exploited in the wild within the next 30 days. A higher score means more likely to be exploited.

Weakness Type (CWE)

CWE-22 Path Traversal

CWE-59 CWE-59

References

Other References

https://github.com/ludwig-ai/ludwig/commit/7f534f31e725cc5362c8f6f85537488443de279a https://github.com/ludwig-ai/ludwig/releases/tag/v0.17.8 https://github.com/ludwig-ai/ludwig/security/advisories/GHSA-qvrm-3x4c-rcxf

Frequently Asked Questions

What is CVE-2026-104103? +

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. It has a CVSS v3.1 base score of 8.1 (HIGH).

How severe is CVE-2026-104103? +

CVE-2026-104103 has a CVSS v3.1 score of 8.1 out of 10, rated HIGH. This is a high-severity vulnerability that should be prioritized for patching. The EPSS score is 0.0049, placing it in the 0th percentile for exploitation probability.

How do I check if I'm vulnerable to CVE-2026-104103? +

You can use Secably's free Website Scanner to check your website for known vulnerabilities. For infrastructure scanning, use the Port Scanner to identify exposed services that may be affected. Check the vendor advisories linked above for specific patch and version information.

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