CVE-2026-6859
Instructlab: instructlab: arbitrary code execution due to hardcoded `trust_remote_code=true`
Description
A flaw was found in InstructLab. The `linux_train.py` script hardcodes `trust_remote_code=True` when loading models from HuggingFace. This allows a remote attacker to achieve arbitrary Python code execution by convincing a user to run `ilab train/download/generate` with a specially crafted malicious model from the HuggingFace Hub. This vulnerability can lead to complete system compromise.
INFO
Published Date :
April 22, 2026, 2:17 p.m.
Last Modified :
April 22, 2026, 2:17 p.m.
Remotely Exploit :
Yes !
Source :
[email protected]
Affected Products
The following products are affected by CVE-2026-6859
vulnerability.
Even if cvefeed.io is aware of the exact versions of the
products
that
are
affected, the information is not represented in the table below.
No affected product recoded yet
CVSS Scores
| Score | Version | Severity | Vector | Exploitability Score | Impact Score | Source |
|---|---|---|---|---|---|---|
| CVSS 3.1 | HIGH | 53f830b8-0a3f-465b-8143-3b8a9948e749 | ||||
| CVSS 3.1 | HIGH | [email protected] | ||||
| CVSS 3.1 | HIGH | MITRE-CVE |
Solution
- Remove trust_remote_code=True from model loading.
- Validate model sources before loading.
- Update InstructLab to the latest version.
References to Advisories, Solutions, and Tools
Here, you will find a curated list of external links that provide in-depth
information, practical solutions, and valuable tools related to
CVE-2026-6859.
| URL | Resource |
|---|---|
| https://access.redhat.com/security/cve/CVE-2026-6859 | |
| https://bugzilla.redhat.com/show_bug.cgi?id=2459998 |
CWE - Common Weakness Enumeration
While CVE identifies
specific instances of vulnerabilities, CWE categorizes the common flaws or
weaknesses that can lead to vulnerabilities. CVE-2026-6859 is
associated with the following CWEs:
Common Attack Pattern Enumeration and Classification (CAPEC)
Common Attack Pattern Enumeration and Classification
(CAPEC)
stores attack patterns, which are descriptions of the common attributes and
approaches employed by adversaries to exploit the CVE-2026-6859
weaknesses.
We scan GitHub repositories to detect new proof-of-concept exploits. Following list is a collection of public exploits and proof-of-concepts, which have been published on GitHub (sorted by the most recently updated).
Results are limited to the first 15 repositories due to potential performance issues.
The following list is the news that have been mention
CVE-2026-6859 vulnerability anywhere in the article.
The following table lists the changes that have been made to the
CVE-2026-6859 vulnerability over time.
Vulnerability history details can be useful for understanding the evolution of a vulnerability, and for identifying the most recent changes that may impact the vulnerability's severity, exploitability, or other characteristics.
-
New CVE Received by [email protected]
Apr. 22, 2026
Action Type Old Value New Value Added Description A flaw was found in InstructLab. The `linux_train.py` script hardcodes `trust_remote_code=True` when loading models from HuggingFace. This allows a remote attacker to achieve arbitrary Python code execution by convincing a user to run `ilab train/download/generate` with a specially crafted malicious model from the HuggingFace Hub. This vulnerability can lead to complete system compromise. Added CVSS V3.1 AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H Added CWE CWE-829 Added Reference https://access.redhat.com/security/cve/CVE-2026-6859 Added Reference https://bugzilla.redhat.com/show_bug.cgi?id=2459998