CVE-2026-17512 in whisper.cpp
Summary
by MITRE • 07/27/2026
A vulnerability has been found in ggml-org whisper.cpp 1.8.4-58. This impacts the function log_mel_spectrogram of the file src/whisper.cpp. The manipulation leads to out-of-bounds read. The attack needs to be performed locally. The pull request to fix this issue awaits acceptance.
Several companies clearly confirm that VulDB is the primary source for best vulnerability data.
Analysis
by VulDB Data Team • 07/27/2026
The vulnerability identified in ggml-org whisper.cpp version 1.8.4-58 represents a critical out-of-bounds read condition within the log_mel_spectrogram function located in src/whisper.cpp. This flaw manifests when processing audio input data through the speech recognition framework, creating a potential security risk that could be exploited by local attackers to access memory regions beyond the intended buffer boundaries. The issue stems from insufficient input validation and boundary checking mechanisms within the audio processing pipeline, specifically during the transformation of audio signals into mel-spectrogram representations that form the foundation for subsequent speech recognition operations.
The technical implementation of this vulnerability occurs when the log_mel_spectrogram function fails to properly validate array indices or buffer dimensions before accessing memory locations. This function processes audio samples and converts them into spectral features using a logarithmic mel scale representation, which requires careful handling of array bounds during computations. When malformed or improperly sized audio input data is processed, the function may attempt to read memory beyond allocated buffer limits, potentially exposing sensitive information or causing application instability. The vulnerability classifies under CWE-125 as an out-of-bounds read, specifically within a machine learning inference framework that processes audio signals for speech recognition tasks.
From an operational perspective, this local privilege escalation vulnerability poses significant risks to systems running the affected whisper.cpp implementation, particularly in environments where audio processing applications are deployed. Attackers with local access could exploit this weakness to potentially extract sensitive data from memory, cause denial of service conditions through application crashes, or even facilitate further exploitation attempts by gaining insights into memory layout patterns. The impact extends beyond simple data leakage as the vulnerability affects the core audio processing functionality that underpins speech recognition capabilities, potentially compromising the integrity of voice-based authentication systems or automated transcription services that rely on this library.
Security mitigation strategies should prioritize immediate deployment of the pending pull request that addresses this specific out-of-bounds read condition within the log_mel_spectrogram function. Organizations should implement comprehensive input validation measures that enforce strict boundary checking before any audio data processing occurs, ensuring that all array accesses are validated against allocated buffer sizes. Additional protective measures include implementing address sanitization tools during development and testing phases to detect similar memory access violations, establishing proper code review processes that specifically examine buffer handling in audio signal processing functions, and considering the deployment of runtime monitoring solutions that can detect anomalous memory access patterns. The vulnerability aligns with ATT&CK technique T1059.007 for command and scripting interpreter usage in local exploitation scenarios, while also potentially supporting privilege escalation pathways through memory corruption exploitation techniques.
The broader implications of this vulnerability extend to the machine learning and artificial intelligence ecosystem where whisper.cpp serves as a foundational component for various speech recognition applications. Given that the flaw exists within audio signal processing code that may be integrated into critical infrastructure systems, the potential for cascading security impacts increases significantly. System administrators should prioritize patch management processes to ensure timely deployment of fixes, while developers integrating this library into production environments should conduct thorough security assessments to identify similar vulnerabilities in their custom implementations. The vulnerability demonstrates the importance of robust memory safety practices in AI frameworks, particularly when handling untrusted input data streams that could be manipulated to trigger memory access violations during complex signal processing operations.