CVE-2026-108954 in sherpa-onnx
Summary
by MITRE • 10/13/2026
A vulnerability was found in k2-fsa sherpa-onnx up to 1.13.8. This issue affects the function OfflineTransducerGreedySearchDecoder::Decode of the file sherpa-onnx/csrc/offline-transducer-greedy-search-decoder.cc of the component Offline Transducer Greedy Search Decoder. The manipulation of the argument vocab_size results in buffer overflow. The attack can be launched remotely. The exploit has been made public and could be used. If only official model packs are used and they always keep metadata consistent with the joiner, the bug may not be reachable in normal supported usage. However, from a security and robustness standpoint, the code still contains the vulnerability because it trusts unvalidated model metadata and can be triggered by a malformed or tampered model pack.
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Analysis
by VulDB Data Team • 10/13/2026
The identified vulnerability resides within the k2-fsa sherpa-onnx library, specifically affecting versions up to 1.13.8 in the Offline Transducer Greedy Search Decoder component. The flaw is located in the Decode function of the file sherpa-onnx/csrc/offline-transducer-greedy-search-decoder.cc and involves a critical buffer overflow condition triggered by improper handling of the vocab_size argument. This type of memory corruption error occurs when the application writes data beyond the boundaries of an allocated buffer, potentially allowing for arbitrary code execution or system crashes depending on how the overwritten memory is utilized. The vulnerability stems from insufficient validation of model metadata, particularly regarding vocabulary size parameters that dictate array dimensions and loop bounds during decoding operations.
From a technical perspective, this issue aligns with CWE-120, which classifies buffer copy without checking size limits, as well as CWE-787, indicating out-of-bounds write vulnerabilities. The root cause lies in the assumption that model metadata provided by external sources is always consistent and correctly formatted relative to the internal joiner logic. When a malformed or tampered model pack is processed, the vocab_size value may exceed expected limits, causing the decoder to access memory locations outside its allocated space. This lack of input validation represents a significant deviation from secure coding practices where all external inputs must be rigorously checked before being used in sensitive operations such as memory allocation and array indexing.
The operational impact of this vulnerability is severe due to its remote exploitability. Attackers can launch attacks over the network by supplying specially crafted model files or metadata that trigger the buffer overflow condition upon decoding. Since the exploit has been made public, automated exploitation tools may already exist in threat actor arsenals. In environments where sherpa-onnx is used for speech recognition services exposed to untrusted users, this vulnerability could lead to complete system compromise. Even if official model packs are typically trusted and maintain consistent metadata, the existence of a reachable code path means that any deviation from expected input formats can destabilize the application or provide an entry point for further exploitation steps such as privilege escalation or data exfiltration.
Mitigation strategies must focus on both immediate remediation and long-term architectural improvements. The primary solution is to upgrade sherpa-onnx to version 1.13.9 or later where this vulnerability has been addressed through enhanced input validation mechanisms. For systems that cannot immediately update, implementing strict model file integrity checks before processing can reduce risk. This includes verifying metadata consistency against known good baselines and rejecting any inputs with anomalous vocab_size values. Additionally, deploying runtime protection technologies such as Address Space Layout Randomization (ASLR) and Data Execution Prevention (DEP) can mitigate the impact of buffer overflow attempts by making code execution more difficult for attackers.
From a threat intelligence standpoint, this vulnerability maps to MITRE ATT&CK technique T1203, which covers Exploitation for Client Execution, as well as potential paths leading to Command and Control if used in conjunction with other exploits. Security teams should monitor for indicators of compromise related to unusual model file uploads or decoding requests that exhibit patterns consistent with fuzzing activities. Regular security audits focusing on input validation across all components handling external data are essential to prevent similar issues. Ensuring that all third-party libraries receive timely updates and patches is critical in maintaining a robust defense posture against memory corruption vulnerabilities that remain prevalent in complex software ecosystems involving audio processing and machine learning inference engines.