CVE-2023-43654 Scanner

TorchServe is a robust tool widely used in production environments for serving and scaling PyTorch models. It is utilized by data scientists and developers for deploying machine learning models. PyTorch is often employed in academic research and commercial applications due to its dynamic computation capabilities. TorchServe allows users to import and serve trained models efficiently within their infrastructure. It provides scalability to handle large volumes of requests, making it suitable for industrial-scale applications. The tool fosters an easy integration with other services and platforms, enhancing workflow efficiency in AI model deployment.

The vulnerability in question is a Server-Side-Request-Forgery (SSRF) flaw found in TorchServe. This critical issue arises due to inadequate input validation in the default configuration. It allows external parties to execute HTTP requests remotely and potentially manipulate saved data. The vulnerability exists due to insufficient restrictions on URL inputs meant for model loading, leaving the system exposed to unauthorized external requests. Exploiting this SSRF vulnerability could lead to unauthorized actions being performed on the affected server. The problem is prevalent in TorchServe versions ranging from 0.1.0 to 0.8.1.

TorchServe's SSRF vulnerability allows attackers to trick the server into making unwanted requests to an unintended location. This is facilitated through insufficient validation of user-supplied input for model URLs. Due to this, a malicious user can manipulate the model loading mechanism to request URLs that serve an attacker's purpose. The successful exploitation of this vulnerability could lead to data exposure or data manipulation. TorchServe lacks the ability to distinguish between authorized and unauthorized network resources effectively. Users must ensure scrutinized configuration settings to mitigate the potential misuse.

Exploitation of this vulnerability could compromise sensitive data and the integrity of the entire system. Attackers could misuse the SSRF flaw to escalate their privileges or even perform data exfiltration. Moreover, unauthorized access to network resources can lead to service disruptions or the execution of unauthorized operations. There is the possibility of subsequent internal attacks due to obtained internal network details. Some extreme consequences may include affecting the overall availability and reliability of TorchServe applications. As the system's boundary is bypassed, it raises significant security concerns requiring prompt corrective measures.

REFERENCES

• https://github.com/pytorch/serve/pull/2534
• https://github.com/pytorch/serve/releases/tag/v0.8.2
• https://github.com/OligoCyberSecurity/ShellTorchChecker