CVE-2014-0866 in Algorithmics
Summary
by MITRE
RICOS in IBM Algo Credit Limits (aka ACLM) 4.5.0 through 4.7.0 before 4.7.0.03 FP5 in IBM Algorithmics sends cleartext credentials over HTTP, which allows remote attackers to obtain sensitive information by sniffing the network.
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Analysis
by VulDB Data Team • 10/01/2024
The vulnerability identified as CVE-2014-0866 affects IBM Algo Credit Limits (ACLM) version 4.5.0 through 4.7.0 before 4.7.0.03 FP5, specifically within the RICOS component of the IBM Algorithmics suite. This security flaw represents a critical weakness in the system's authentication mechanism that exposes sensitive credential information during network communication. The vulnerability stems from the application's improper handling of authentication data, where credentials are transmitted in cleartext over unencrypted HTTP connections rather than utilizing secure encrypted protocols.
The technical implementation of this vulnerability involves the transmission of user authentication credentials over HTTP without any form of encryption or tokenization. When users authenticate to the RICOS component of ACLM, their usernames and passwords are sent as plain text within HTTP request headers or body content, making them immediately accessible to any network observer who can capture and analyze traffic. This cleartext transmission occurs across the network without any transport layer security measures such as TLS/SSL encryption, which would normally protect sensitive information during transit. The vulnerability specifically affects the authentication and authorization mechanisms that are fundamental to access control within the credit limits application.
From an operational impact perspective, this vulnerability creates a severe risk landscape for organizations utilizing IBM ACLM systems. Remote attackers with network access can easily intercept and decode authentication credentials using standard packet sniffing tools such as tcpdump, Wireshark, or similar network analysis utilities. Once obtained, these cleartext credentials can be used to gain unauthorized access to the credit limits system, potentially leading to data breaches, financial manipulation, and unauthorized changes to credit parameters. The attack vector is particularly dangerous because it requires minimal technical expertise to exploit, making it attractive to both malicious actors and automated attack tools. Organizations may face significant regulatory compliance issues if credit limit data is compromised, particularly in financial services environments where such information is highly sensitive.
The vulnerability aligns with CWE-312 (Cleartext Storage of Sensitive Information) and CWE-319 (Cleartext Transmission of Sensitive Information) categories, which specifically address the improper handling of sensitive data during transmission and storage phases. From an ATT&CK framework perspective, this vulnerability maps to T1046 (Network Service Scanning) and T1566 (Phishing) as attackers can leverage network sniffing capabilities to gather credentials, and T1078 (Valid Accounts) since compromised credentials can be used for legitimate access to the system. The exploitation of this vulnerability also falls under the broader category of credential theft and man-in-the-middle attacks that are commonly used in enterprise security breaches.
Organizations should implement immediate mitigations including mandatory use of HTTPS encryption for all authentication endpoints, deployment of network segmentation to isolate critical systems, and implementation of network monitoring solutions to detect unusual traffic patterns. The most effective long-term solution involves upgrading to IBM ACLM version 4.7.0.03 FP5 or later, which includes proper encryption mechanisms for credential transmission. Additionally, organizations should consider implementing multi-factor authentication, network access controls, and regular security audits to prevent exploitation of similar vulnerabilities. Security teams should also establish incident response procedures specifically designed to handle credential compromise scenarios and ensure proper monitoring of authentication logs for suspicious activities.