CVE-2025-49188 is a medium-severity vulnerability identified in all versions of SICK AG's SICK Field Analytics product. The core issue stems from the application transmitting user credentials via URL query parameters in HTTP GET requests rather than embedding them securely within the body of POST requests. This practice violates secure coding principles and corresponds to CWE-598: Use of GET Request Method With Sensitive Query Strings. When credentials are sent as URL parameters, they become exposed in multiple places such as browser history, server logs, proxy logs, and potentially third-party monitoring tools. This exposure increases the risk of unauthorized information gathering by attackers who can intercept or access these logs. The vulnerability does not require authentication or user interaction to be exploited, and the attack vector is network-based (AV:N), meaning an attacker can remotely capture these credentials if they have network access to the communication channel. However, the impact is limited to confidentiality loss (C:L) without affecting integrity or availability. The CVSS 3.1 base score is 5.3, reflecting a medium severity level. No known exploits are currently reported in the wild, and no patches have been released yet. The vulnerability affects all versions, indicating a systemic design flaw in how the application handles sensitive data transmission. Given the nature of the flaw, attackers could perform passive reconnaissance or active interception to harvest credentials, potentially leading to unauthorized access if combined with other weaknesses or reused credentials. The lack of encryption or secure transmission mechanisms (e.g., HTTPS) would exacerbate this risk, although this detail is not specified in the provided data.

For European organizations using SICK Field Analytics, this vulnerability poses a moderate risk primarily to the confidentiality of user credentials. Since the credentials are exposed in URLs, any network monitoring, logging infrastructure, or intermediaries could capture sensitive information, leading to potential unauthorized access to the analytics platform. This could result in unauthorized data exposure or manipulation of industrial analytics data, which may affect operational decision-making or industrial process monitoring. Given that SICK AG products are often deployed in industrial automation, manufacturing, and logistics sectors, compromised credentials could facilitate lateral movement within operational technology (OT) environments, increasing the risk of industrial espionage or sabotage. The vulnerability does not directly impact system integrity or availability, but the indirect consequences of credential compromise could be significant in critical infrastructure or manufacturing contexts. Additionally, the exposure of credentials may violate data protection regulations such as GDPR if personal data or user identities are involved, potentially leading to compliance issues and reputational damage. Organizations relying on SICK Field Analytics should be aware of these risks and consider them in their broader cybersecurity and risk management strategies.

1. Immediate mitigation should focus on network-level protections such as enforcing HTTPS/TLS encryption to prevent interception of URLs containing credentials. 2. Implement network segmentation and strict access controls to limit exposure of the analytics platform to trusted networks only. 3. Monitor and audit logs and network traffic for unusual access patterns or credential leakage. 4. Encourage users to change credentials regularly and avoid credential reuse across systems to reduce impact if credentials are exposed. 5. Work with SICK AG to obtain patches or updates that correct the design flaw by moving credential transmission to POST request bodies or other secure methods. 6. If possible, deploy web application firewalls (WAFs) or intrusion detection/prevention systems (IDS/IPS) that can detect and block suspicious GET requests containing sensitive parameters. 7. Educate users and administrators about the risks of sharing URLs containing sensitive information and discourage practices such as URL sharing or bookmarking with embedded credentials. 8. Review and harden logging policies to ensure sensitive data is not stored in logs or is masked appropriately. 9. Consider implementing multi-factor authentication (MFA) on the analytics platform to reduce the impact of credential compromise. These steps go beyond generic advice by focusing on compensating controls and operational practices tailored to the specific vulnerability's nature and the industrial context of SICK Field Analytics.