CVE-2026-18772 in rlottie
Summary
by MITRE • 08/04/2026
Improper input validation vulnerability in Samsung Open Source rlottie allows Oversized Serialized Data Payloads.
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Analysis
by VulDB Data Team • 08/04/2026
The vulnerability under discussion represents an improper input validation flaw within Samsung's open source rlottie library, which serves as a vector for oversized serialized data payloads. This issue stems from insufficient validation mechanisms that fail to properly sanitize or limit the size of incoming serialized data structures. The rlottie library, designed for rendering lottie animations in various applications, processes complex serialized data formats containing animation parameters, keyframes, and graphical instructions. When malformed or excessively large payload data reaches the library's parsing functions, the lack of proper input validation creates a potential exploitation vector that could lead to resource exhaustion or arbitrary code execution.
The technical nature of this vulnerability can be classified as a CWE-20 issue, representing improper input validation within software systems. The flaw manifests when the rlottie parser encounters serialized data that exceeds predetermined size limits or contains malformed structures that trigger unexpected behavior in memory allocation routines. This type of vulnerability aligns with ATT&CK technique T1059.007 which involves the execution of malicious code through manipulation of input parameters and data serialization processes. The underlying mechanism typically involves heap-based buffer overflows, integer overflows, or memory allocation failures that occur when the library attempts to process oversized data structures without proper bounds checking.
The operational impact of this vulnerability extends across various Samsung applications and systems that utilize the rlottie library for animation rendering capabilities. Mobile devices, tablets, and other consumer electronics running Samsung's operating systems may be susceptible to resource exhaustion attacks or denial of service conditions when processing maliciously crafted lottie files. Attackers could potentially exploit this weakness by crafting oversized serialized data payloads that cause memory allocation failures or trigger stack-based buffer overflows during animation processing. The vulnerability becomes particularly concerning in environments where untrusted user input is processed through the rlottie library, as it creates opportunities for remote code execution or system instability.
Mitigation strategies for this vulnerability should encompass multiple layers of protection including immediate code-level fixes that implement strict input validation and size limitation mechanisms within the rlottie parser. Organizations utilizing the rlottie library must ensure proper bounds checking and memory allocation limits are enforced during data processing operations, with particular attention to validating serialized data structures before parsing. Security patches should address the root cause by implementing comprehensive input sanitization routines that reject oversized payloads or malformed data structures. Additionally, system administrators should consider deploying runtime monitoring solutions that can detect anomalous memory usage patterns or unexpected behavior in animation processing components. The implementation of strict content filtering and sandboxing mechanisms around lottie file processing can provide additional defense-in-depth measures against exploitation attempts targeting this specific vulnerability class.