CVE-2023-52305 in Paddle
Summary
by MITRE • 01/03/2024
FPE in paddle.topk in PaddlePaddle before 2.6.0. This flaw can cause a runtime crash and a denial of service.
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Analysis
by VulDB Data Team • 01/23/2024
The vulnerability identified as CVE-2023-52305 affects PaddlePaddle, an open-source deep learning framework, specifically within the paddle.topk function implementation. This issue represents a floating point exception that occurs when processing certain input parameters, leading to unexpected runtime termination and potential system instability. The flaw exists in versions prior to 2.6.0, indicating that users operating within this version range face significant operational risks when utilizing the topk functionality for sorting or selecting highest-value elements from tensors.
The technical root cause of this vulnerability stems from improper handling of edge cases within the paddle.topk function's computational logic. When the function encounters specific combinations of input parameters, particularly those involving invalid tensor dimensions or unexpected numerical values, it triggers a floating point exception that crashes the executing process. This type of flaw falls under the category of improper input validation and exception handling, which aligns with CWE-369 and CWE-755 vulnerability classifications. The exception occurs during the execution phase of the software lifecycle, making it a runtime vulnerability that can be exploited by malicious actors or inadvertently triggered by malformed inputs.
The operational impact of CVE-2023-52305 extends beyond simple application crashes to encompass broader system availability concerns. Organizations relying on PaddlePaddle for machine learning workloads may experience service disruption when the topk function is invoked with problematic inputs, potentially affecting model training pipelines, inference services, or data processing workflows. This vulnerability directly maps to the attack pattern described in MITRE ATT&CK framework under T1499.004 for network denial of service, as the crash conditions can be leveraged to cause system unavailability. The vulnerability affects both local and remote execution scenarios, making it particularly concerning for cloud-based machine learning platforms where multiple users might interact with the same system components.
Mitigation strategies for this vulnerability primarily focus on immediate version upgrades to PaddlePaddle 2.6.0 or later, which contain the necessary patches to address the floating point exception handling. System administrators should implement comprehensive testing procedures to validate that all applications utilizing paddle.topk functionality are operating with patched versions before deployment. Additionally, input validation mechanisms should be strengthened at the application level to prevent malformed parameters from reaching the vulnerable function, though this represents a secondary defense measure. Organizations should also establish monitoring protocols to detect and respond to potential exploitation attempts, particularly in environments where the affected functionality is exposed to untrusted inputs. The vulnerability serves as a reminder of the importance of proper exception handling in mathematical computing libraries, where numerical edge cases can lead to system instability and availability issues.