CVE-2026-110698 in Praat
Summary
by MITRE • 10/14/2026
A security vulnerability has been detected in Praat up to 7.0.02. Affected is the function whisper_model_load of the file external/whispercpp/whisper.cpp of the component Model Loader. The manipulation of the argument n_dims leads to out-of-bounds write. The attack can be initiated remotely. The exploit has been disclosed publicly and may be used. The identifier of the patch is a910d21a2705ef4cd16d91f29cdc8da1ab1a7221. To fix this issue, it is recommended to deploy a patch. The project maintainer confirms: "We did a quick fix (suggested fix 1) for Praat version 7.0.03, and scheduled a better fix (suggested fix 2)."
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Analysis
by VulDB Data Team • 10/15/2026
The vulnerability identified in Praat versions up to 7.0.02 resides within the Model Loader component, specifically affecting the whisper_model_load function located in the external/whispercpp/whisper.cpp source file. This flaw is classified as an out-of-bounds write, a critical memory safety issue where software writes data beyond the boundaries of allocated buffers. The root cause lies in the improper handling or validation of the n_dims argument passed to this function. When an attacker supplies a manipulated value for n_dims that exceeds expected limits or lacks proper boundary checks, the application attempts to write data into memory locations outside the intended buffer space. This type of error is formally categorized under CWE-787: Out-of-bounds Write in standard vulnerability classification systems, highlighting the severity of potential memory corruption and subsequent system instability or compromise.
The operational impact of this vulnerability is significant due to its remote exploitability. Because Praat often handles audio data which can be sourced from external files or network streams, an attacker could potentially trigger this flaw by providing a maliciously crafted input file that forces the whisper_model_load function to process invalid dimension parameters. The successful exploitation allows for arbitrary code execution, denial of service through application crashes, or information disclosure depending on how the overwritten memory is utilized and what data resides in adjacent memory regions. Given that the exploit has been disclosed publicly and is known to be usable, systems running unpatched versions are at immediate risk of being targeted by automated attacks or manual exploitation attempts aimed at compromising the integrity and availability of the software environment.
Industry standards such as MITRE ATT&CK provide context for this type of vulnerability within broader attack scenarios. Out-of-bounds writes often facilitate techniques related to Execution Override, where attackers leverage memory corruption to execute malicious code, potentially leading to privilege escalation if the application runs with elevated permissions. The specific mechanism here involves manipulating input parameters during model loading, which aligns with patterns seen in attacks targeting machine learning libraries and data processing pipelines that fail to validate structural integrity of incoming models or configuration files. This underscores the importance of rigorous input validation not just for user-facing inputs but also for internal API calls and library interactions within complex software stacks like Praat.
To mitigate this risk, it is strongly recommended that administrators and users upgrade immediately to Praat version 7.0.03 or later, which includes a quick fix addressing the immediate out-of-bounds write issue. The project maintainer has acknowledged the severity of the flaw and confirmed the deployment of patch identifier a910d21a2705ef4cd16d91f29cdc8da1ab1a7221 to resolve this specific vulnerability. Furthermore, users should be aware that while version 7.0.03 provides immediate relief through suggested fix one, the maintainer has scheduled a more comprehensive solution referred to as suggested fix two for future releases. Until then, deploying the latest available patch is the primary defense against exploitation of this publicly disclosed flaw. Organizations relying on Praat for linguistic analysis or audio processing should verify their deployment versions and apply updates promptly to maintain security posture against known exploit vectors targeting memory safety violations in model loading routines.