Red Hat OpenShift AI (RHOAI): Improper Neutralization of Argument Delimiters in a Command
CVE-2026-31230 is a high-severity improper neutralization of argument delimiters in a command vulnerability in Red Hat OpenShift AI (RHOAI) from Red Hat, with a CVSS 3.1 base score of 8.8 out of 10. It can be exploited remotely over the network by an attacker with a low-privileged account, without any action from a user. There is no confirmed exploitation so far. EPSS estimates a 0.55% chance of exploitation within 30 days, which is low compared with other CVEs.
High severity with no evidence of exploitation so far.
What to do
- Check which version of Red Hat OpenShift AI (RHOAI) you run against the affected versions below, and install the vendor's latest release.
Affected products and versions
| Vendor | Product | Affected versions | Fixed in |
|---|---|---|---|
| Red Hat | Red Hat OpenShift AI (RHOAI) | Not specified in the record | Not stated |
Version data comes from the organisation that assigned the CVE. Confirm against the vendor advisory before deciding you are unaffected.
How the severity score breaks down
- Attack vector
- NetworkCan be exploited remotely over a network, including the internet.
- Attack complexity
- LowNo special conditions are needed; the attack works reliably.
- Privileges required
- LowA basic user account is enough.
- User interaction
- NoneNo action by a victim is required.
- Scope
- UnchangedImpact stays within the vulnerable component.
- Confidentiality
- HighAll data handled by the component can be read.
- Integrity
- HighData or code can be modified at will.
- Availability
- HighThe component can be taken down completely.
Vector: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H. Scored by CNA.
| Scoring system | Score | Severity | Scored by |
|---|---|---|---|
| CVSS 3.1 | 9.8 | Critical | CISA (ADP) |
| CVSS 3.1 | 8.8 | High | CNA |
Official description
The Adversarial Robustness Toolbox (ART) thru 1.20.1 contains a command-line argument injection vulnerability in its Kubeflow component (robustness_evaluation_fgsm_pytorch.py). The script uses the unsafe eval() function to parse string values provided via the --clip_values and --input_shape command-line arguments. This allows an attacker to inject arbitrary Python code into these arguments, which will be executed when eval() is called. The vulnerability can be exploited remotely if an attacker can control these arguments (e.g., through pipeline configuration or automated scripts), leading to arbitrary code execution on the system running the ART evaluation.
Source: the CVE record as published by its numbering authority, via NVD and CVE.org.
The underlying weakness: Improper Neutralization of Argument Delimiters in a Command
The product constructs a string for a command to be executed by a separate component in another control sphere, but it does not properly delimit the intended arguments, options, or switches within that command string.
How developers prevent this class of flaw
- Understand all the potential areas where untrusted inputs can enter your product: parameters or arguments, cookies, anything read from the network, environment variables, request headers as well as content, URL components, e-mail, files, databases, and any external systems that provide data to the application. Perform input validation at well-defined interfaces.
- Directly convert your input type into the expected data type, such as using a conversion function that translates a string into a number. After converting to the expected data type, ensure that the input's values fall within the expected range of allowable values and that multi-field consistencies are maintained.
- When exchanging data between components, ensure that both components are using the same character encoding. Ensure that the proper encoding is applied at each interface. Explicitly set the encoding you are using whenever the protocol allows you to do so.
More Improper Neutralization of Argument Delimiters in a Command vulnerabilities (CWE-88)
Timeline
- CVE record published.
- CISA published its SSVC assessment (exploitation: none, automatable: yes, technical impact: total).
- Record last changed materially (score, affected products, status or references).
References
Other references
Other vulnerabilities in Red Hat OpenShift AI (RHOAI)
- 9.1 Critical severity
- 7.7 High severity
- 7.5 High severity
- 7.7 High severity
- 9.1 Critical severity
- 7.4 High severity
Recent Improper Neutralization of Argument Delimiters in a Command vulnerabilities
- 8.2 High severity
- 9.8 Critical severity
- 7.8 High severity
- 9.6 Critical severity
- 9.8 Critical severity
- 9.1 Critical severity
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