CVE-2025-71003 Overview
CVE-2025-71003 is an input validation vulnerability affecting the flow.arange() component of OneFlow v0.9.0, an open-source deep learning framework. This vulnerability allows attackers to cause a Denial of Service (DoS) condition by providing specially crafted input to the vulnerable function. The flaw stems from improper input validation (CWE-20), which fails to adequately sanitize user-supplied data before processing.
Critical Impact
Attackers can exploit this vulnerability remotely over the network without authentication to crash OneFlow applications, disrupting machine learning workloads and model training operations.
Affected Products
- OneFlow v0.9.0
- Applications and services built on OneFlow v0.9.0 utilizing the flow.arange() function
Discovery Timeline
- 2026-01-28 - CVE-2025-71003 published to NVD
- 2026-01-29 - Last updated in NVD database
Technical Details for CVE-2025-71003
Vulnerability Analysis
The vulnerability resides in the flow.arange() function within OneFlow, a deep learning framework designed for distributed computing. The arange() function is commonly used to generate tensors containing evenly spaced values within a given interval—a fundamental operation in machine learning workflows for creating indices, ranges, and sequential data structures.
When processing user-supplied parameters, the function fails to properly validate input boundaries and data types. This lack of input sanitization allows an attacker to provide malformed or unexpected values that trigger undefined behavior within the framework, ultimately causing the application to crash or become unresponsive.
The network-based attack vector means that any service exposing OneFlow functionality to remote inputs—such as ML inference APIs, model serving endpoints, or distributed training nodes—could be targeted without requiring prior authentication.
Root Cause
The root cause is improper input validation (CWE-20) in the flow.arange() component. The function does not adequately verify that input parameters fall within acceptable ranges or conform to expected data types before attempting to process them. This allows boundary conditions or edge-case inputs to propagate through the system and trigger failure conditions.
Attack Vector
The attack vector is network-based, requiring no user interaction or special privileges. An attacker can remotely send crafted input to any application or service utilizing the vulnerable flow.arange() function. Successful exploitation results in a Denial of Service condition, affecting application availability but not compromising data confidentiality or integrity.
The vulnerability can be exploited by submitting malformed parameters to the flow.arange() function. When the function receives input values that fall outside expected boundaries or contain malformed data types, it fails to handle these edge cases gracefully. Technical details regarding the specific exploitation mechanism can be found in the GitHub Issue Discussion.
Detection Methods for CVE-2025-71003
Indicators of Compromise
- Unexpected application crashes or service restarts in OneFlow-based workloads
- Abnormal input patterns targeting flow.arange() function calls in application logs
- Increased error rates or exceptions originating from tensor creation operations
- Memory allocation failures or resource exhaustion events coinciding with external requests
Detection Strategies
- Implement input validation monitoring at application boundaries to detect malformed tensor parameters
- Deploy anomaly detection for unusual patterns in API requests to ML serving endpoints
- Monitor system logs for recurring crashes or exceptions in OneFlow processes
- Configure alerts for unexpected service restarts in ML pipeline components
Monitoring Recommendations
- Enable verbose logging for OneFlow operations to capture detailed error information
- Implement rate limiting on public-facing ML inference APIs to reduce DoS impact
- Monitor resource utilization metrics (CPU, memory) for abnormal spikes during request processing
- Set up health checks and automated recovery mechanisms for OneFlow-based services
How to Mitigate CVE-2025-71003
Immediate Actions Required
- Audit all deployments to identify systems running OneFlow v0.9.0
- Implement input validation at the application layer before passing data to flow.arange()
- Consider isolating vulnerable OneFlow instances from untrusted network access
- Monitor the OneFlow GitHub repository for official patches
Patch Information
No official vendor patch has been confirmed at the time of this writing. Users should monitor the OneFlow project's GitHub repository and release notes for security updates addressing this vulnerability. The issue has been documented in the GitHub Issue Discussion.
Workarounds
- Implement strict input validation for all parameters passed to flow.arange() before invocation
- Deploy network-level controls to restrict access to OneFlow-based services from untrusted sources
- Consider upgrading to newer OneFlow versions if available and verified to be unaffected
- Implement application-level exception handling to gracefully recover from crashes
# Example: Input validation wrapper for flow.arange()
# Validate parameters before passing to the vulnerable function
# Implement boundary checks for start, end, and step parameters
# Ensure data types match expected tensor parameter specifications
# Log and reject requests with invalid input patterns
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
