CVE-2023-32636 in glib
Summary
by MITRE • 09/14/2023
A flaw was found in glib, where the gvariant deserialization code is vulnerable to a denial of service introduced by additional input validation added to resolve CVE-2023-29499. The offset table validation may be very slow. This bug does not affect any released version of glib but does affect glib distributors who followed the guidance of glib developers to backport the initial fix for CVE-2023-29499.
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Analysis
by VulDB Data Team • 05/22/2026
The vulnerability in glib represents a sophisticated denial of service condition that emerged from the implementation of input validation measures intended to address CVE-2023-29499. This flaw specifically targets the gvariant deserialization functionality within the glib library, which serves as a fundamental component in numerous applications and systems that rely on structured data handling. The issue arose when developers introduced additional validation checks to resolve the initial vulnerability, inadvertently creating a performance degradation that manifests as a denial of service attack vector.
The technical root cause lies within the offset table validation mechanism that processes gvariant data structures during deserialization operations. When malformed input data is processed through this validation routine, the algorithm exhibits exponential time complexity characteristics that cause significant processing delays. The validation logic becomes computationally expensive as it traverses through corrupted offset tables, creating a scenario where legitimate processing time is consumed by validating maliciously crafted input. This behavior directly aligns with CWE-778, which categorizes insufficient resource consumption as a vulnerability that can lead to denial of service conditions.
The operational impact of this vulnerability is particularly concerning for systems that depend on glib's gvariant functionality for data processing and communication. Applications utilizing glib's serialization features may experience complete service unavailability when processing maliciously crafted input data, as the validation routines consume excessive computational resources. This affects not only individual applications but also entire system infrastructures that rely on glib as a foundational library, potentially leading to cascading failures across interconnected services. The vulnerability specifically targets environments where glib is used for inter-process communication, configuration file parsing, or any scenario involving structured data deserialization.
Systems that have implemented the backport of the initial fix for CVE-2023-29499 are particularly at risk, as the remediation introduced additional validation overhead without addressing the underlying performance issues. This creates a paradoxical situation where security improvements inadvertently introduce performance degradation that can be exploited for denial of service attacks. The vulnerability demonstrates how security patches can sometimes introduce new attack surfaces when not carefully evaluated for performance implications. Organizations should consider this issue in the context of ATT&CK technique T1499.004, which covers network denial of service attacks, and recognize that the vulnerability represents a software-level denial of service vector that can be exploited through crafted input data.
Mitigation strategies should focus on implementing rate limiting and input validation controls that prevent excessive processing of malformed data while maintaining system responsiveness. Organizations should consider deploying input sanitization layers that can identify and reject suspicious gvariant structures before they reach the vulnerable deserialization code. Additionally, monitoring systems should be configured to detect unusual processing patterns that may indicate exploitation attempts. The most effective approach involves updating to patched versions of glib that address both the original vulnerability and the performance degradation introduced by the validation changes. Security teams should also implement defensive programming practices that include timeouts for deserialization operations and resource consumption limits to prevent exploitation of this denial of service condition.