CVE-2025-1497 is a critical vulnerability classified under CWE-77 (Improper Neutralization of Special Elements used in a Command Injection) affecting the MLJAR PlotAI product. The core issue arises from insufficient validation of outputs generated by the integrated large language model (LLM) within PlotAI. Specifically, the software fails to properly sanitize or neutralize special elements in the LLM-generated output, which can be manipulated by an attacker to inject and execute arbitrary Python code remotely. This results in a Remote Code Execution (RCE) vulnerability that does not require any authentication or user interaction, making exploitation straightforward if the vulnerable code path is enabled. The vendor has commented out the vulnerable line of code as a temporary mitigation, but to use the software fully, users must uncomment this line, thereby reintroducing the risk. Notably, the vendor has stated that they do not plan to release an official patch to remediate this vulnerability, leaving users exposed if they choose to enable the affected functionality. The vulnerability has a CVSS 4.0 base score of 9.3, indicating a critical severity level due to its network attack vector, low attack complexity, no privileges or user interaction required, and high impact on confidentiality, integrity, and availability. No known exploits are currently reported in the wild, but the ease of exploitation and severity suggest that this could become a target for attackers, especially in environments where PlotAI is used for data visualization or AI-assisted analytics.

For European organizations, the impact of this vulnerability can be severe. PlotAI is used for AI-driven data visualization and analysis, and organizations relying on it for critical decision-making or reporting could face significant risks. Successful exploitation could lead to full system compromise, data theft, manipulation of analytics results, or disruption of business operations. Confidentiality breaches could expose sensitive business or personal data, while integrity violations could corrupt analytical outputs, leading to erroneous business decisions. Availability could also be impacted if attackers deploy destructive payloads or ransomware. Given the lack of an official patch and the necessity to uncomment the vulnerable code for full functionality, organizations face a difficult choice between usability and security. This vulnerability is particularly concerning for sectors with stringent data protection requirements such as finance, healthcare, and government agencies within Europe, where regulatory compliance (e.g., GDPR) demands robust security controls. Additionally, the remote and unauthenticated nature of the exploit increases the attack surface, especially for organizations exposing PlotAI services to external networks or cloud environments.

Organizations should immediately assess their use of MLJAR PlotAI and avoid uncommenting the vulnerable code line that enables the risky functionality. If the vulnerable feature is essential, consider isolating the PlotAI environment using strict network segmentation and firewall rules to limit exposure. Employ runtime application self-protection (RASP) or endpoint detection and response (EDR) tools to monitor for suspicious Python code execution patterns. Implement strict input/output validation and sanitization at any integration points with PlotAI outputs. Consider deploying application-layer firewalls or proxies that can detect and block command injection attempts. Where possible, replace or supplement PlotAI with alternative tools that do not have this vulnerability. Regularly audit and monitor logs for unusual activity related to PlotAI processes. Engage with the vendor or community to track any unofficial patches or mitigations. Finally, ensure that incident response plans include scenarios for RCE attacks originating from AI-assisted tools.