CVE-2023-6019 Scanner

Ray is a distributed computing framework primarily employed for scaling Python applications from single servers to large clusters. Data scientists, machine learning engineers, and researchers extensively use Ray to run parallel, distributed, and scalable applications in various environments. It is designed to simplify the process of building and executing complex applications, making it particularly valuable in computational and data-intense tasks. Ray is often found in research institutions, tech companies, and any environment where large-scale data processing and machine learning tasks are essential. As a platform, it facilitates the execution of distributed tasks and the deployment of machine learning models across multiple nodes, providing efficient resource utilization and performance optimization. The vulnerability in question can compromise the intended secure operations of such systems by allowing unauthorized command executions.

The Command Injection vulnerability detected in Ray enables an attacker to execute arbitrary OS commands on the host system via the cpu_profile API endpoint. It is a critical security issue as it allows malicious users to interfere with the host OS, leading to potential unauthorized control over the system. Command injection vulnerabilities like this one result from improper sanitization of user input, permitting characters or strings that are interpretable as a command. Once exploited, this vulnerability could be used to execute commands that the attacker designs, potentially leading to further system compromises. Command injection vulnerabilities are particularly dangerous as they can allow attackers to gain additional access to other parts of the network, retrieve confidential data, or disrupt service operations. Recognizing and addressing these vulnerabilities is crucial for maintaining the security and integrity of sensitive systems.

In the technical details, the vulnerable endpoint is the cpu_profile within the Ray Dashboard application. Through manipulations in the API request parameters, specifically via GET requests to the worker/cpu_profile path, malicious commands can be injected. The exploitation occurs by leveraging base64 encoded payload while escaping the intended execution context using special characters such as backticks. By crafting payloads that correctly encode the command injection into the request format, attackers can disrupt or completely compromise the host environment. Moreover, the use of GET requests may expose the parameters and payload to logging or midway interception that could further aid exploitation if not encrypted or suitably protected. Vigilance in sanitizing inputs and altering the methods of interacting with execution contexts is essential to prevent such exploit pathways.

The possible effects of this vulnerability include the execution of arbitrary system commands, which can compromise the host running the Ray dashboard. An attacker could escalate their privileges, pivot to other critical systems within the network, exfiltrate sensitive data, or even install malicious software. The attack can lead to service disruption, data breaches, and monetary, reputational, and operational damages. If these vulnerabilities are exploited, organizations might face prolonged downtime, loss of intellectual property, and exposure of customer data. Ensuring consistent application of security patches and adherence to best coding practices can mitigate these risks, preserving the integrity and confidentiality of affected systems.

REFERENCES

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