CVE-2023-52304 in Paddle
Summary
by MITRE • 01/03/2024
Stack overflow in paddle.searchsorted in PaddlePaddle before 2.6.0. This flaw can lead to a denial of service, or even more damage.
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Analysis
by VulDB Data Team • 01/23/2024
The vulnerability identified as CVE-2023-52304 represents a critical stack overflow condition within the paddle.searchsorted function of the PaddlePaddle deep learning framework. This flaw exists in versions prior to 2.6.0 and stems from insufficient input validation and memory management within the searchsorted implementation. The stack overflow occurs when the function processes malformed or excessively large input parameters that exceed expected bounds, causing the program to overwrite adjacent memory locations on the call stack. Such behavior fundamentally compromises the application's memory integrity and can result in unpredictable program termination or arbitrary code execution in favorable conditions.
The technical implementation of this vulnerability manifests through improper bounds checking during the searchsorted operation where the function fails to validate array dimensions and input parameters before performing memory allocations. When malicious input is provided, the function attempts to allocate stack memory based on unvalidated user-supplied values, leading to buffer overflow conditions. This type of vulnerability aligns with CWE-121 Stack-based Buffer Overflow, which specifically addresses buffer overflows occurring in stack memory regions where insufficient bounds checking allows attackers to overwrite adjacent stack data. The flaw operates at the intersection of memory safety and algorithmic robustness, where the searchsorted function's recursive or iterative processing logic does not adequately sanitize input parameters before proceeding with memory operations.
From an operational perspective, this vulnerability presents significant risk to systems utilizing PaddlePaddle for machine learning workloads, particularly in production environments where the framework handles untrusted input data. The potential impact extends beyond simple denial of service to include possible remote code execution if an attacker can control the input parameters and manipulate the execution flow through stack corruption. In environments where PaddlePaddle is used for data processing pipelines, model training, or inference services, this vulnerability could enable attackers to disrupt operations, gain unauthorized access to system resources, or compromise the integrity of machine learning models and datasets. The vulnerability's severity classification reflects its potential to be exploited in both local and remote attack scenarios, making it particularly dangerous for cloud-based machine learning platforms and enterprise deployments.
Mitigation strategies for CVE-2023-52304 should prioritize immediate patching of affected PaddlePaddle installations to version 2.6.0 or later where the stack overflow protections have been implemented. Organizations should implement input validation measures at application boundaries to sanitize all data passed to the searchsorted function, including parameter length checks and boundary validation. Network segmentation and access controls should be enforced to limit exposure of systems running vulnerable PaddlePaddle versions. Security monitoring should include detection of anomalous memory usage patterns and unusual function call sequences that might indicate exploitation attempts. Additionally, developers should follow secure coding practices as outlined in the ATT&CK framework's software development security domains, particularly focusing on preventing buffer overflow conditions through proper input validation and memory management. Regular security assessments and vulnerability scanning should be conducted to identify similar issues in other components of the machine learning pipeline and ensure comprehensive protection against similar attack vectors.