CVE-2026-6859: Red Hat Enterprise Linux AI (RHEL AI) 3 · Untrusted Functionality Inclusion
Affects Red Hat Enterprise Linux AI (RHEL AI) 3Source CVE Database V5
Not known to be exploited · CVSS 8.8 High.
Data as of 29 Oct 2026, 19:04 UTC
- EPSS
- 0.4% chance of exploitation in 30 days – higher than 38% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:HExploitation evidence
Disclosed: evidence found.
CVE published 22 Apr 2026
Detectable: no evidence on record.
No public nuclei template indexed
Weaponized: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by 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 29 Oct 2026.
What to do
Affected software
| Product | Affected | Fixed in |
|---|---|---|
| Red Hat Enterprise Linux AI (RHEL AI) 3rhelai3/bootc-aws-cuda-rhel9 | Affected– | Fixed in– |
| Red Hat Enterprise Linux AI (RHEL AI) 3rhelai3/bootc-azure-cuda-rhel9 | Affected– | Fixed in– |
| Red Hat Enterprise Linux AI (RHEL AI) 3rhelai3/bootc-azure-rocm-rhel9 | Affected– | Fixed in– |
| Red Hat Enterprise Linux AI (RHEL AI) 3rhelai3/bootc-cuda-rhel9 | Affected– | Fixed in– |
| Red Hat Enterprise Linux AI (RHEL AI) 3rhelai3/bootc-gcp-cuda-rhel9 | Affected– | Fixed in– |
| Red Hat Enterprise Linux AI (RHEL AI) 3rhelai3/bootc-rocm-rhel9 | Affected– | Fixed in– |
AI-suggested
Until an official fix is available, users should avoid running `ilab train/download/generate` commands with models from untrusted sources such as the HuggingFace Hub. Restricting usage to trusted models and exercising caution with user interaction can reduce risk.
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.