CVE-2024-36745 in Oneflow
Summary
by MITRE • 06/06/2024
An issue in OneFlow-Inc. Oneflow v0.9.1 allows attackers to cause a Denial of Service (DoS) via inputting a negative value into the oneflow.index_select parameter.
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Analysis
by VulDB Data Team • 09/18/2024
The vulnerability identified as CVE-2024-36745 affects OneFlow-Inc. Oneflow version 0.9.1 and represents a denial of service condition that can be triggered through improper input validation within the oneflow.index_select function. This issue demonstrates a classic lack of input sanitization that allows malicious actors to disrupt normal system operations by providing unexpected parameter values.
The technical flaw manifests when the oneflow.index_select parameter receives negative values as input, which the system fails to properly validate or handle. This function is designed to select elements from tensors along a specified dimension using provided indices, but the absence of proper boundary checking for negative indices creates an exploitable condition. When negative values are passed, the system likely attempts to interpret these as valid tensor indices, leading to undefined behavior that can result in system crashes or resource exhaustion.
From an operational impact perspective, this vulnerability enables attackers to perform denial of service attacks against systems running affected OneFlow versions. The attack vector is relatively simple to execute, requiring only the submission of negative values to the index_select function, making it accessible to threat actors with basic technical knowledge. The consequences extend beyond mere service disruption, potentially affecting machine learning workflows, data processing pipelines, and AI model deployment environments where OneFlow is utilized.
The vulnerability aligns with CWE-129, which addresses improper validation of input boundaries, and represents a specific instance of insufficient input validation that can lead to system instability. From an ATT&CK framework perspective, this issue maps to T1499.004, specifically the technique of network denial of service, as the vulnerability can be leveraged to disrupt services through resource exhaustion or system crashes. The attack surface is particularly concerning in cloud-based machine learning environments where OneFlow might be used in production workflows, as it could be exploited to interrupt critical AI processing tasks.
Mitigation strategies should focus on implementing comprehensive input validation for all tensor indexing operations, including proper bounds checking for negative indices. System administrators should immediately upgrade to patched versions of OneFlow where available, while developers should incorporate defensive programming practices such as validating parameter ranges before processing. Additionally, monitoring systems should be configured to detect unusual patterns of index selection that might indicate exploitation attempts, and access controls should be implemented to limit exposure of vulnerable functions to untrusted inputs. The fix should include explicit handling of negative indices according to standard tensor indexing conventions, ensuring that invalid values either produce appropriate error messages or are converted to valid ranges rather than causing system instability.