CVE-2025-1497 in PlotAI
Summary
by MITRE • 03/10/2025
A vulnerability, that could result in Remote Code Execution (RCE), has been found in PlotAI. Lack of validation of LLM-generated output allows attacker to execute arbitrary Python code. Vendor commented out vulnerable line, further usage of the software requires uncommenting it and thus accepting the risk. The vendor does not plan to release a patch to fix this vulnerability.
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Analysis
by VulDB Data Team • 03/25/2025
This vulnerability exists within the PlotAI software platform where insufficient validation of large language model generated content creates a critical remote code execution risk. The flaw stems from the improper handling of user-supplied inputs that are processed through LLM components, allowing malicious actors to inject and execute arbitrary Python code on affected systems. The vulnerability represents a severe security weakness that directly enables attackers to gain full control over the target environment, making it a prime target for exploitation in adversarial scenarios.
The technical implementation of this vulnerability occurs when the software processes outputs from LLM components without adequate sanitization or validation mechanisms. This creates an attack surface where malicious payloads can be embedded within the generated content and subsequently executed as Python code. The vulnerability is classified as a code injection flaw that aligns with CWE-94, which specifically addresses "Improper Control of Generation of Code" and falls under the broader category of CWE-74, "Improper Neutralization of Special Elements in Output Used by a Downstream Component." The attack vector is particularly dangerous because it leverages the legitimate functionality of LLM processing to deliver malicious payloads.
The operational impact of this vulnerability is devastating for organizations using PlotAI, as it provides attackers with complete system compromise capabilities. Once exploited, adversaries can execute commands with the privileges of the affected application, potentially leading to data exfiltration, lateral movement within networks, and establishment of persistent backdoors. The risk is amplified by the fact that the vendor has chosen not to provide a patch, leaving users with no official remediation path. This approach essentially forces organizations to make a conscious decision between functionality and security, with the default state being inherently insecure.
Organizations should implement immediate mitigations including network segmentation to limit access to PlotAI systems, monitoring for suspicious code execution patterns, and restricting the use of LLM-generated content processing. The lack of vendor support for patching this vulnerability means that defensive measures must be implemented at the network and application level. Security teams should consider disabling or heavily restricting the functionality that triggers this vulnerability until alternative solutions are available. This vulnerability also highlights the broader risks associated with integrating AI systems that generate code without proper validation mechanisms, emphasizing the need for secure AI development practices and adherence to security standards such as those outlined in the OWASP AI Security Top 10 framework.
The vendor's decision not to release a patch represents a significant security oversight that leaves users exposed to ongoing exploitation risks. This approach of leaving known vulnerabilities unpatched while continuing to support the software creates an unacceptable risk profile for enterprise environments. Organizations should carefully evaluate their continued use of PlotAI given the vendor's stance on remediation, as this represents a fundamental failure in security responsibility. The vulnerability serves as a cautionary example of how AI integration can introduce new attack vectors that require careful consideration of security implications before deployment.