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.
This vulnerability carries a CRITICAL severity rating with a CVSS v3.1 score of 9.0, indicating it can be exploited remotely over the network but requires specific conditions to be met without requiring user interaction and does not require pre-existing privileges . The vulnerability impacts confidentiality (data exposure), integrity (unauthorized modifications), and availability (service disruption) for affected systems.
Reported in 2025, this vulnerability emerged during an era marked by increased sophistication in supply chain attacks, cloud infrastructure vulnerabilities, and software-as-a-service (SaaS) security challenges. Security practices during this period emphasized zero-trust architectures, container security, and API protection.
2025-01-14T19:15:31.813
2026-04-15T00:35:42.020
Deferred
CVSSv3.1: 9.0 (CRITICAL)
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