CVE-2024-36734 in Oneflow
Summary
by MITRE • 06/06/2024
Improper input validation in OneFlow-Inc. Oneflow v0.9.1 allows attackers to cause a Denial of Service (DoS) via inputting a negative value into the dim parameter.
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Analysis
by VulDB Data Team • 08/20/2024
The vulnerability identified as CVE-2024-36734 represents a critical input validation flaw within the OneFlow framework version 0.9.1, specifically affecting the dim parameter handling mechanism. This issue stems from inadequate sanitization and validation of user-provided inputs, creating a pathway for malicious actors to exploit the system's processing logic. The vulnerability is categorized under CWE-20, which denotes improper input validation, and falls within the broader scope of software security weaknesses that can lead to system instability and operational disruption.
The technical implementation of this vulnerability occurs when the system processes tensor operations where the dim parameter is expected to represent a valid dimension index for tensor manipulation. When attackers provide negative values to this parameter, the framework fails to properly validate the input range, leading to unexpected behavior in the underlying computational graph execution. This flaw manifests as a denial of service condition where the system becomes unresponsive or crashes during tensor operations, effectively preventing legitimate users from accessing the framework's computational capabilities.
From an operational perspective, this vulnerability presents significant risks to organizations relying on OneFlow for machine learning workloads and deep learning computations. The denial of service impact can result in complete system unavailability, requiring manual intervention to restore normal operations. Attackers can exploit this weakness by crafting malicious input sequences that trigger the DoS condition, potentially causing cascading failures in distributed computing environments where OneFlow serves as a core component. The vulnerability affects the availability aspect of the CIA triad, compromising the system's ability to provide continuous service to authorized users.
The attack surface for this vulnerability extends across all applications utilizing OneFlow v0.9.1 where tensor operations are performed with user-provided dimension parameters. This includes machine learning model training environments, inference systems, and data processing pipelines that depend on the framework's tensor manipulation capabilities. The ATT&CK framework categorizes this issue under T1499, which covers resource hijacking through denial of service, as the vulnerability enables attackers to consume system resources in a manner that prevents legitimate operations from completing successfully.
Mitigation strategies for CVE-2024-36734 should focus on implementing robust input validation mechanisms that enforce proper bounds checking on the dim parameter. Organizations should update to the latest stable version of OneFlow where this vulnerability has been addressed through proper input sanitization and validation routines. Additionally, deploying defensive programming practices such as input parameter bounds checking, exception handling, and comprehensive logging can help detect and prevent exploitation attempts. Network-level protections including rate limiting and input filtering mechanisms can provide additional layers of defense against automated exploitation attempts targeting this vulnerability. Security teams should also consider implementing monitoring solutions that can detect unusual patterns of tensor operation requests that may indicate attempted exploitation of this denial of service condition.