HighVulnerability

CVE-2026-58474: whichllm code injection in run and snippet commands via GGUF filenames

Source: NVD(opens in a new tab)
Identifier: CVE-2026-58474
Published: August 26, 2026
Record updated: October 9, 2026

Known exploitation: Not listed in the CISA Known Exploited Vulnerabilities catalog at the last check.
EPSS: 0.9%

Summary

whichllm before 0.5.16 has a code injection flaw in its run and snippet commands. A remote attacker who controls a HuggingFace repository can craft a GGUF filename containing double quotes or other special characters to execute arbitrary code on the user's machine, before any model download occurs. The script generation function in cli.py interpolates the rfilename value from the Hub API siblings field into generated Python source without escaping.

Mitigation

The source does not state a fix yet. Check the original advisory for updates.

Details

Scores and exploitation

CVSS score: 8.8(high)
CVSS vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
Attack vector: network
Attack complexity: low
Privileges required: none
User interaction: required
EPSS: 0.9%Estimated probability that this CVE is exploited in the next 30 days, from the FIRST Exploit Prediction Scoring System. Read on November 4, 2026.
Disclosure date: August 26, 2026

Weaknesses and techniques

Weakness (CWE): CWE-94
Attack pattern (CAPEC): CAPEC-242
AI threat technique (MITRE ATLAS): AML.T0010

Affected

Vendors: HuggingFace

Classification

Attack type: Supply chain,
Other
Attack sophistication: Moderate
AI component targeted: Framework
Impact: Confidentiality,
Integrity,
Availability
Labels: security

How this record was produced

Summaries, mitigations and labels on this page are generated automatically from the source. The linked source is authoritative. The source gives this advisory a description and no title, so the title above is a headline generated from the source text.

Original source: https://nvd.nist.gov/vuln/detail/CVE-2026-58474

First tracked: August 26, 2026 at 08:08 PM ET, from NVD/CVE Database

Classifier: claude-haiku-5-5, prompt v4

Classifier confidence: 90% (self-assessed)

Summary

Mitigation

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