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CVE-2024-6868VulnerabilityPublished Updated

CVE-2024-6868: mudler/localai · Improper Link Resolution Before File Access

Affects mudler/localaiSource CVE Database V5

0

Not known to be exploited · CVSS 8.1 High.

Data as of 25 Oct 2026, 19:02 UTC

CVSS 3.0 base score
8.1High
How severe the flaw is on the 0–10 CVSS scale.
EPSS
1.5% chance of exploitation in 30 days – higher than 74% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
CVSS:3.0/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:H

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 29 Oct 2024

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponised: no evidence on record.

    No public exploit code indexed in Metasploit or Exploit-DB

  4. Exploited in the wild: no evidence on record.

    Not in CISA KEV and no source reports exploitation

  5. Observed by OffSeq Mirage: no evidence on record.

    No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 2026-10-25.

What to do

Affected software

ProductAffectedFixed in
GitHubmudler/localaipkg:github/mudler/localaiOther threatsAffected
  • =2.17.1
Fixed in–

Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.

AI-suggested

1. Apply patches or updates from mudler/localai as soon as they become available to address the improper archive extraction behavior. 2. Until patches are released, disable or restrict the use of automatic archive extraction for model configurations, especially from untrusted sources. 3. Implement strict validation and sanitization of archive contents before extraction, ensuring no file paths can escape the intended extraction directory (e.g., by rejecting entries with '../' or absolute paths). 4. Employ filesystem access controls and sandboxing to limit the permissions of the mudler/localai process, preventing it from writing outside designated directories. 5. Monitor file system changes in the models directory and critical backend asset locations for unauthorized modifications. 6. Restrict privileges of users or automated systems that can upload or configure models to mudler/localai to minimize the risk of malicious archive uploads. 7. Conduct regular security audits and penetration testing focused on archive handling and deployment pipelines. 8. Consider network segmentation and firewall rules to limit exposure of mudler/localai services to trusted networks only.

Description

mudler/LocalAI version 2.17.1 allows for arbitrary file write due to improper handling of automatic archive extraction. When model configurations specify additional files as archives (e.g., .tar), these archives are automatically extracted after downloading. This behavior can be exploited to perform a 'tarslip' attack, allowing files to be written to arbitrary locations on the server, bypassing checks that normally restrict files to the models directory. This vulnerability can lead to remote code execution (RCE) by overwriting backend assets used by the server.

References

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