CVE-2019-15788 in Clara Genomics Analysis
Summary
by MITRE
Clara Genomics Analysis before 0.2.0 has an integer overflow for cudapoa memory management in allocate_block.cpp.
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Analysis
by VulDB Data Team • 12/07/2023
The vulnerability identified as CVE-2019-15788 resides within the Clara Genomics Analysis software suite, specifically affecting versions prior to 0.2.0. This issue manifests as an integer overflow within the cudapoa memory management component, which operates through the allocate_block.cpp file. The flaw represents a critical security weakness that could potentially be exploited to compromise system integrity and availability. Clara Genomics Analysis is a software framework designed for genomic data processing and analysis, commonly used in high-performance computing environments for DNA sequencing and variant calling operations. The integer overflow vulnerability specifically impacts the memory allocation mechanisms that are essential for processing large-scale genomic datasets through GPU-accelerated computing techniques.
The technical implementation of this vulnerability occurs within the cudapoa (CUDA-based POA - Partial Order Alignment) functionality that handles memory management for genomic sequence alignment operations. When processing genomic data, the software allocates memory blocks to store intermediate computational results during the alignment process. The integer overflow condition arises during the calculation of memory requirements for these blocks, where an arithmetic operation exceeds the maximum value that can be represented by the integer data type being used. This overflow results in the allocation of insufficient memory space, potentially causing memory corruption, application crashes, or unpredictable behavior during genomic analysis workflows. The vulnerability is particularly concerning because it operates at the memory management level, which is fundamental to the software's ability to process large datasets efficiently.
The operational impact of this vulnerability extends beyond simple application instability to potentially compromise the integrity of genomic research data and computational workflows. When the integer overflow occurs, it can lead to memory corruption that may cause incorrect genomic sequence alignments, data loss, or complete application failure during critical analysis phases. In high-throughput genomic sequencing environments where large datasets are processed continuously, such a vulnerability could result in significant downtime and data integrity issues. The affected system behavior may manifest as application crashes, segmentation faults, or memory allocation failures that halt critical research processes. Given that genomic analysis often involves multi-day computational jobs and massive datasets, a memory management failure could result in substantial computational resource waste and research delays.
Mitigation strategies for this vulnerability should focus on immediate software updates to version 0.2.0 or later, which includes patches addressing the integer overflow condition in the cudapoa memory management code. Organizations should also implement monitoring systems to detect anomalous memory allocation patterns that might indicate exploitation attempts. The vulnerability aligns with CWE-190, Integer Overflow or Wraparound, which represents a common class of memory safety issues in software systems. From an attack surface perspective, this vulnerability could be leveraged by adversaries to perform denial-of-service attacks against genomic analysis systems or potentially escalate privileges if the software runs with elevated permissions. Security teams should also consider implementing input validation measures and memory protection techniques such as address space layout randomization and stack canaries to reduce the effectiveness of potential exploitation attempts. Additionally, regular security assessments of genomic analysis pipelines should be conducted to identify similar memory management vulnerabilities that could affect other components in the computational genomics stack.