CVE-2024-49361

Potential Vulnerability in ACON Library: Improper Input Validation Leading to Malicious Code Execution

Published Oct 18, 2024 CVSS 4.0 · 8.1 High EPSS < 1% KEV · Not listed

Description

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.

Affected Products

Default status is the baseline for the product, each version can override it (e.g. patched versions marked unaffected).

Vendor Product Default status Versions

torinriley

ACON

affected

Version	Status	Constraints
`<= 1.1.0`	affected	—

No data.

No data available yet.

Remediation

No solution or workaround provided in the CVE record

Tracking

Advisories

Source	ID	Title
EUVD	EUVD-2024-43402	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.

Metrics

Attack Vector Network

Attack Complexity Low

Privileges Required None

Attack Requirements None

User Interaction None

Vulnerable System Confidentiality Impact High

Vulnerable System Integrity Impact High

Vulnerable System Availability Impact High

Subsequent System Confidentiality Impact None

Subsequent System Integrity Impact None

Subsequent System Availability Impact None

No CVSS v3.1

No CVSS v3.0

No CVSS v2

This CVE is not in the KEV list.

Exploit prediction score: < 1% (0.734% probability within 30 days)

Exploitation none

Automatable yes

Technical Impact total

References

Link	Providers
https://github.com/torinriley/ACON/security/advisories/GHSA-345g-6rmp-3cv9

History

Oct 18, 2024 9:15 PM First appearance in the feed. Affected versions updated. Affected vendors updated. Scoring metrics updated View changes

Type	Values Removed	Values Added
First Time appeared		Acon Acon acon
CPEs		cpe:2.3:a:acon:acon::::::::
Vendors & Products		Acon Acon acon
Metrics		ssvc `{'options': {'Automatable': 'yes', 'Exploitation': 'none', 'Technical Impact': 'total'}, 'version': '2.0.3'}`

Oct 18, 2024 7:00 PM Description updated. Title updated. Weaknesses updated. Added references. Scoring metrics updated View changes

Type	Values Removed	Values Added
Description		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.
Title		Potential Vulnerability in ACON Library: Improper Input Validation Leading to Malicious Code Execution
Weaknesses		CWE-20
References		https://github.com/torinriley/ACON/security/advisories/GHSA-345g-6rmp-3cv9
Metrics		cvssV4_0 `{'score': 8.1, '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'}`