CVE-2024-49375 in rasa
Summary
by MITRE • 01/14/2025
Open source machine learning framework. A vulnerability has been identified in Rasa that enables an attacker who has the ability to load a maliciously crafted model remotely into a Rasa instance to achieve Remote Code Execution. The prerequisites for this are: 1. The HTTP API must be enabled on the Rasa instance eg with `--enable-api`. This is not the default configuration. 2. For unauthenticated RCE to be exploitable, the user must not have configured any authentication or other security controls recommended in our documentation. 3. For authenticated RCE, the attacker must posses a valid authentication token or JWT to interact with the Rasa API. This issue has been addressed in rasa version 3.6.21 and all users are advised to upgrade. Users unable to upgrade should ensure that they require authentication and that only trusted users are given access.
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Analysis
by VulDB Data Team • 01/14/2025
The vulnerability identified as CVE-2024-49375 affects the Rasa open source machine learning framework, presenting a critical remote code execution risk that stems from improper model validation mechanisms within the platform's API handling capabilities. This flaw specifically targets instances where the HTTP API has been explicitly enabled through the `--enable-api` flag, which represents a non-default configuration that significantly increases attack surface exposure. The vulnerability operates through a sophisticated exploitation vector that leverages the model loading functionality, allowing attackers to craft maliciously formatted models that, when processed by the Rasa instance, execute arbitrary code on the underlying system. This represents a severe compromise of the principle of least privilege and demonstrates a critical failure in input sanitization and validation processes that should prevent untrusted code execution within the application boundary.
The technical exploitation of this vulnerability requires a specific combination of conditions that must be met for successful execution, creating a multi-layered attack scenario that aligns with ATT&CK technique T1059.001 for command and script injection. The prerequisite configuration of HTTP API enablement creates an initial attack surface that must be present for exploitation to occur, while the authentication requirements determine the level of access needed for successful compromise. In unauthenticated scenarios, the vulnerability becomes particularly dangerous as it allows any remote attacker to achieve code execution without prior authentication, provided that the system lacks proper security controls. When authentication is required, the attack vector shifts to credential compromise or token theft, which aligns with ATT&CK technique T1566 for credential access and T1548.001 for abuse of privileges. The vulnerability demonstrates a weakness in the model loading pipeline that should implement strict validation and sandboxing mechanisms to prevent arbitrary code execution, representing a failure in the software's defense-in-depth principles and input validation security controls.
The operational impact of CVE-2024-49375 extends far beyond simple code execution, as successful exploitation can result in complete system compromise and potential lateral movement within network environments. Attackers who successfully exploit this vulnerability gain the ability to execute arbitrary commands with the privileges of the Rasa service account, potentially leading to data exfiltration, system persistence, and further network reconnaissance. The vulnerability's impact is particularly concerning in environments where Rasa instances are deployed in production systems with sensitive data processing capabilities, as it could enable attackers to access confidential information or disrupt critical business processes. Organizations using Rasa for natural language processing and conversational AI applications face significant risk, as these systems often process sensitive user data and may be integrated with other critical infrastructure components. The vulnerability also represents a failure in the principle of secure by default configuration, as the default installation does not expose the vulnerable API, but rather requires explicit enabling by administrators who may not fully understand the security implications of their configuration choices.
Mitigation strategies for CVE-2024-49375 must address both immediate remediation and long-term security hardening measures to protect against exploitation. The most effective immediate solution is upgrading to Rasa version 3.6.21 or later, which includes patches that address the model loading validation vulnerabilities. Organizations that cannot immediately upgrade should implement strict access controls and authentication mechanisms, ensuring that all API endpoints require proper authentication tokens or JWTs for access. The implementation of network segmentation and firewall rules that restrict access to the Rasa API endpoints to trusted IP addresses provides additional layers of defense. Security controls should include monitoring for unusual model loading activities and implementing automated detection mechanisms that can identify potentially malicious model files. Organizations should also conduct comprehensive security reviews of their Rasa configurations to ensure that unnecessary API endpoints are disabled and that proper authentication mechanisms are in place. The vulnerability highlights the importance of following security best practices such as those outlined in the OWASP Top 10 and NIST Cybersecurity Framework, particularly focusing on secure coding practices and proper input validation. Additionally, implementing principle of least privilege access controls and regular security audits of API configurations will help prevent similar vulnerabilities from being exploited in the future.