Potential Vulnerability in ACON Library: Improper Input Validation Leading to Malicious Code Execution
ACON is a widely-used library of tools for machine learning that focuses on adaptive correlation optimization. A potential vulnerability has been identified in the input validation process, which could lead to arbitrary code execution if exploited. This issue could allow an attacker to submit malicious input data, bypassing input validation, resulting in remote code execution in certain machine learning applications using the ACON library. All users utilizing ACON’s input-handling functions are potentially at risk. Specifically, machine learning models or applications that ingest user-generated data without proper sanitization are the most vulnerable. Users running ACON on production servers are at heightened risk, as the vulnerability could be exploited remotely. As of time of publication, it is unclear whether a fix is available.
Problem Types
| Type | CWE ID | Description |
|---|
| CWE | CWE-20 | CWE-20: Improper Input Validation |
Type: CWE
Description: CWE-20: Improper Input Validation
Metrics
| Version | Base score | Base severity | Vector |
|---|
| 4.0 | 8.1 | HIGH | CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N/E:U |
Version: 4.0
Base score: 8.1
Base severity: HIGH
Vector: CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N/E:U