Introduction
Attackers can force AI infrastructure to access sensitive internal resources simply by submitting a crafted file upload request. This is the real-world impact of CVE-2025-54381, a critical Server-Side Request Forgery (SSRF) vulnerability in BentoML, a Python library widely used for deploying and serving machine learning models. The flaw affects versions 1.4.0 through 1.4.19 and is exploitable without authentication, making it a significant risk for any exposed BentoML deployment.
About BentoML: BentoML is an open-source Python framework designed to streamline the deployment and serving of machine learning models. It is used by organizations ranging from startups to large enterprises to operationalize AI workloads. Its popularity in the AI infrastructure space means vulnerabilities in BentoML can have broad consequences for organizations relying on automated model inference and serving.
Technical Information
CVE-2025-54381 is rooted in how BentoML processes file uploads via both multipart form data and JSON request handlers. In affected versions (1.4.0 through 1.4.19), these handlers accept user-supplied URLs and automatically download files from them. Critically, there is no robust validation to determine whether the provided URLs resolve to internal network addresses, cloud metadata endpoints, or other protected resources.
- Multipart Handler: This code path performs almost no validation. Any URL provided by the user is fetched by the server, regardless of whether it points to an internal service, localhost, or a cloud metadata endpoint.
- JSON Handler: This path includes some basic scheme checks (such as requiring
httporhttps), but these are insufficient. Attackers can bypass them using encoding tricks, redirects, or by exploiting gaps in the validation logic.
The vulnerability is particularly dangerous because the official BentoML documentation promoted this URL-based upload feature, making SSRF exposure a default risk for all deployments using these versions. Attackers can craft requests that cause the BentoML server to access internal APIs, cloud provider metadata endpoints (such as AWS 169.254.169.254), or other sensitive resources. This can lead to information disclosure, lateral movement, or further compromise of the infrastructure.
Patch Information
To address the Server-Side Request Forgery (SSRF) vulnerability in BentoML's file upload processing system, the development team has implemented a comprehensive patch in version 1.4.19. This patch introduces stringent validation mechanisms to prevent unauthorized internal network access during file uploads.
Key Enhancements in the Patch:
-
URL Scheme Validation: The patch enforces strict checks on the URL schemes permitted for file uploads. Only URLs with explicitly allowed schemes (e.g.,
http,https) are processed, effectively blocking potentially harmful schemes that could be exploited for SSRF attacks. -
Internal Network Address Restriction: To prevent unauthorized access to internal resources, the patch includes a validation step that checks the destination IP addresses of provided URLs. If a URL resolves to an internal IP address (such as those in the
10.0.0.0/8,172.16.0.0/12, or192.168.0.0/16ranges), the upload is denied. This measure ensures that the application cannot be used as a conduit to access internal services. -
Enhanced Deserialization Security: The deserialization handlers for multipart form data and JSON requests have been fortified to include these validation steps. This ensures that all file upload pathways are secured against SSRF vulnerabilities.
Code Implementation:
import ipaddress import urllib.parse ALLOWED_SCHEMES = {"http", "https"} INTERNAL_IP_RANGES = [ ipaddress.ip_network("10.0.0.0/8"), ipaddress.ip_network("172.16.0.0/12"), ipaddress.ip_network("192.168.0.0/16"), ipaddress.ip_network("127.0.0.0/8"), ipaddress.ip_network("::1/128"), ] def is_internal_address(url): parsed_url = urllib.parse.urlparse(url) if parsed_url.scheme not in ALLOWED_SCHEMES: return True try: host_ip = ipaddress.ip_address(socket.gethostbyname(parsed_url.hostname)) return any(host_ip in net for net in INTERNAL_IP_RANGES) except ValueError: return True return False # Usage in file upload handling if is_internal_address(user_provided_url): raise ValueError("Access to internal addresses is not allowed.")
By integrating these validation steps, BentoML ensures that file uploads are processed securely, mitigating the risk of SSRF attacks. Users are strongly encouraged to upgrade to version 1.4.19 to benefit from these security enhancements.
References:
Detection Methods
Detecting Server-Side Request Forgery (SSRF) vulnerabilities, such as the one identified in BentoML's file upload processing system, requires a comprehensive approach that includes both proactive and reactive measures. Here's how you can effectively identify and monitor for potential SSRF exploits:
1. Code Review and Static Analysis:
Begin by conducting a thorough review of your application's codebase, focusing on areas where external inputs are processed, especially those involving URL handling. Utilize static analysis tools designed to detect insecure coding patterns that could lead to SSRF vulnerabilities. These tools can help identify functions or methods that make HTTP requests based on user input without proper validation.
2. Input Validation and Whitelisting:
Implement strict input validation by defining a whitelist of acceptable URL schemes and domains. This ensures that the application only processes requests to trusted and predefined destinations. For instance, in the context of BentoML, ensure that the serialization/deserialization handlers for multipart form data and JSON requests do not automatically download files from untrusted sources without proper validation.
3. Dynamic Application Security Testing (DAST):
Employ DAST tools to simulate real-world attacks on your application. These tools can identify SSRF vulnerabilities by attempting to exploit potential weaknesses in the application's handling of external requests. Regularly performing such tests helps in uncovering vulnerabilities that might be missed during static analysis.
4. Monitoring and Logging:
Set up comprehensive logging for all outgoing HTTP requests initiated by the server. Monitor these logs for unusual patterns, such as requests to internal network addresses, cloud metadata endpoints, or localhost services. Anomalies in these logs can indicate potential SSRF attempts. Implementing alerting mechanisms that trigger when suspicious requests are detected can provide real-time awareness of potential exploits.
5. Network Segmentation and Firewall Rules:
Configure network segmentation to restrict the server's ability to make requests to internal resources that should not be accessible externally. Implement firewall rules that prevent the server from initiating connections to sensitive internal services. This adds an additional layer of defense against SSRF attacks.
6. Regular Security Audits and Penetration Testing:
Conduct regular security audits and penetration testing to identify and remediate SSRF vulnerabilities. Engaging with security professionals who can perform in-depth testing will help uncover complex vulnerabilities that automated tools might miss.
By integrating these detection methods into your security practices, you can effectively identify and mitigate SSRF vulnerabilities, thereby enhancing the overall security posture of your application.
References:
Affected Systems and Versions
- Product: BentoML (Python library for AI model serving)
- Affected Versions: 1.4.0 through 1.4.19 (inclusive)
- Vulnerable Configurations: Any deployment using the file upload feature that processes user-supplied URLs via multipart form data or JSON handlers in these versions
Vendor Security History
BentoML has experienced multiple critical vulnerabilities in recent releases:
- CVE-2025-27520: Remote code execution via insecure deserialization (previously patched issue reintroduced)
- CVE-2025-32375: Remote code execution in runner server component
- Recurring issues with insecure network request handling and insufficient validation
The vendor has responded with timely patches, but the recurrence of critical flaws suggests a need for improved secure development and regression testing practices.