CVE-2018-18482 in libpg_query
Summary
by MITRE
An issue was discovered in libpg_query 10-1.0.2. There is a memory leak in pg_query_raw_parse in pg_query_parse.c, which might lead to a denial of service.
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Analysis
by VulDB Data Team • 05/30/2023
The vulnerability identified as CVE-2018-18482 affects libpg_query version 10-1.0.2, a library designed to parse postgresql queries and provide structured access to query information. This memory leak occurs within the pg_query_raw_parse function located in the pg_query_parse.c source file, representing a critical flaw in the library's resource management capabilities. The issue manifests when the library processes postgresql query strings and fails to properly release allocated memory blocks, leading to gradual memory consumption over time. Such a flaw is particularly concerning in applications that repeatedly parse postgresql queries, as it can lead to system instability and resource exhaustion. The vulnerability directly impacts the library's ability to maintain consistent performance and reliability, especially in high-throughput environments where query parsing occurs frequently.
The technical root cause of this memory leak stems from improper memory deallocation within the pg_query_raw_parse function, which is responsible for parsing raw postgresql query strings into internal data structures. When the function processes complex or malformed queries, it allocates memory for various internal representations but fails to consistently free this memory upon function completion. This pattern of memory allocation without corresponding deallocation creates a gradual accumulation of unreleased memory blocks that persist throughout the application's runtime. The flaw represents a classic memory leak vulnerability classified under CWE-401, which specifically addresses improper management of memory resources. The issue is particularly insidious because it operates silently, with memory consumption increasing incrementally until system resources are exhausted, making it difficult to detect during routine testing and monitoring.
The operational impact of this vulnerability extends beyond simple resource consumption, creating potential denial of service conditions that can severely disrupt application functionality. In production environments where libpg_query is utilized for query analysis, parsing, or validation, the memory leak can cause applications to gradually slow down, eventually leading to complete system crashes or unresponsiveness. This risk is amplified in server applications, web services, or database management tools that rely heavily on query parsing capabilities, as these systems may experience progressive degradation of performance over time. The vulnerability can be exploited by attackers who submit carefully crafted query strings designed to trigger the memory leak pattern, potentially causing sustained resource exhaustion that results in service disruption. From an attacker perspective, this vulnerability aligns with ATT&CK technique T1499.004, which involves resource exhaustion attacks targeting memory management, making it a significant concern for cybersecurity teams responsible for maintaining system availability.
Mitigation strategies for CVE-2018-18482 should prioritize immediate patching of affected libpg_query installations to version 10-1.0.3 or later, which contains the necessary memory management fixes. Organizations should implement comprehensive monitoring of memory usage patterns in applications utilizing this library to detect early signs of memory leak progression. Additionally, defensive programming practices should be implemented including regular memory leak scanning using tools like valgrind or address sanitizers during development and testing phases. Application-level safeguards such as query timeout mechanisms and resource limits can help mitigate the impact if the vulnerability is exploited, while code reviews should specifically focus on memory allocation patterns within the pg_query_raw_parse function. System administrators should also consider implementing automated alerts for unusual memory consumption patterns that could indicate the presence of this vulnerability in deployed applications.