CVE-2026-119339 in Tesseract
Summary
by MITRE • 10/30/2026
Tesseract is an open source OCR engine. Prior to 5.5.4, WeightMatrix::DeSerialize trusts a mode byte from a traineddata LSTM network and calls WeightMatrix::DeSerializeOld when kDoubleFlag is clear. In default FAST_FLOAT builds, DeSerializeOld invokes ASSERT_HOST instead of returning an error, so loading a crafted model through TessBaseAPI::Init terminates the process and causes denial of service. This issue is fixed in version 5.5.4.
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Analysis
by VulDB Data Team • 10/30/2026
The vulnerability identified in Tesseract OCR engine prior to version 5.5.4 represents a critical failure in input validation within the LSTM neural network deserialization logic, specifically affecting how weight matrices are processed from trained data files. The core technical flaw resides in the WeightMatrix::DeSerialize function, which is responsible for reconstructing model weights during the initialization of an OCR engine instance via TessBaseAPI::Init. When processing a serialized mode byte that indicates specific floating-point precision settings, the code checks if the kDoubleFlag is clear to determine whether to invoke legacy deserialization routines. In this scenario, the system calls WeightMatrix::DeSerializeOld without adequately verifying the integrity or validity of the input data structure itself. This lack of robust boundary checking allows a crafted model file containing malformed or unexpected mode bytes to trigger an internal assertion failure rather than handling the error gracefully through standard exception mechanisms or return codes.
The operational impact of this flaw is severe, resulting in a direct denial of service condition for applications relying on Tesseract for optical character recognition tasks. In default builds configured with FAST_FLOAT optimizations, the WeightMatrix::DeSerializeOld function relies on an ASSERT_HOST macro to validate internal assumptions about data structures. When a maliciously crafted traineddata file is loaded, this assertion fails because the input does not conform to expected structural constraints. Since assertions are typically compiled into fatal error checks in release builds or optimized configurations, their failure causes the application process to terminate immediately and ungracefully. This behavior effectively crashes any service or desktop application that initializes Tesseract with user-supplied model files, disrupting availability without requiring remote code execution privileges, although it could potentially be leveraged for resource exhaustion attacks if triggered repeatedly in a server environment.
From a classification perspective, this vulnerability aligns closely with CWE-20 Improper Input Validation and CWE-754: Improper Check for Unusual or Exceptional Conditions. The failure to validate the mode byte against expected ranges before proceeding with deserialization operations exemplifies a classic input validation weakness where trust is placed in external data sources without sufficient sanitization. Furthermore, this attack vector can be mapped to MITRE ATT&CK technique T1496: Resource Hijacking or more specifically denial of service vectors under impact categories that target availability. The exploitation does not require complex privilege escalation but rather the ability to supply a specific file format input during the initialization phase of the OCR engine, making it particularly dangerous in scenarios where users can influence model selection or configuration parameters.
Mitigation strategies primarily involve upgrading to Tesseract version 5.5.4 or later, which addresses this issue by implementing proper error handling instead of relying on assertion failures for invalid inputs. For environments unable to upgrade immediately, defensive programming practices should be adopted within the application layer wrapping Tesseract. This includes isolating OCR initialization calls in separate processes with strict resource limits and timeouts, ensuring that a crash in one instance does not take down the entire service. Additionally, input validation at the file ingestion stage can help filter out malformed model files before they reach the deserialization logic. Security teams should also review their use of third-party libraries to ensure that error handling paths are robust across different build configurations and optimization levels, particularly when dealing with binary data formats like trained neural network models.