CVE-2026-88048

Analyzed Analyzed - Analysis Complete

Tesseract OCR Heap Out-of-Bounds Write in LSTM Layer

Publication date: 2026-09-10

Last updated on: 2026-09-14

Assigner: GitHub, Inc.

Description

Tesseract is an open source OCR engine. In version 5.5.3 and earlier, FullyConnected::DeSerialize in src/lstm/fullyconnected.cpp does not validate the deserialized layer scalars ni_ and no_ against the weight-matrix dimensions. During FullyConnected::Forward, MatrixDotVector in src/lstm/weightmatrix.cpp writes w.dim1() results into temp_line, which is sized from no_, and reads w.dim2() minus one inputs from curr_input, which is sized from ni_. A crafted .traineddata NT_SOFTMAX layer can therefore use inconsistent dimensions to cause a heap out-of-bounds write and read on the default LSTM engine, resulting in heap corruption, a crash, information disclosure, or potentially controlled corruption. No fixed release is available as of this review.

CVSS Scores

EPSS Scores

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Meta Information

Published

2026-09-10

Last Modified

2026-09-14

Generated

2026-10-01

AI Q&A

2026-09-10

EPSS Evaluated

2026-09-29

NVD

CVE-2026-88048

EUVD

EUVD-2026-75591

Affected Vendors & Products

Vendor	Product	Version / Range
tesseract-ocr	tesseract_ocr	to 5.5.3 (inc)

Helpful Resources

Exploitability

CWE

KEV

CWE ID	Description
CWE-787	The product writes data past the end, or before the beginning, of the intended buffer.
CWE-125	The product reads data past the end, or before the beginning, of the intended buffer.

Attack-Flow Graph

Executive Summary

CVE-2026-88048 is a heap out-of-bounds write and read vulnerability in the Tesseract OCR engine affecting versions 5.5.3 and earlier. It occurs in the FullyConnected::Forward function due to unvalidated layer scalars ni_ and no_ against weight matrix dimensions during deserialization. A crafted .traineddata file with mismatched dimensions can trigger heap corruption, crashes, or information disclosure.

Detection Guidance

This vulnerability is specific to the Tesseract OCR engine and requires examining the .traineddata files used by the system. Check for files with mismatched dimensions in the FullyConnected layer by inspecting the weight matrix and layer scalars (ni_ and no_). No direct network or system commands detect this without code analysis.

Impact Analysis

This vulnerability can lead to heap corruption, application crashes, information disclosure, or controlled memory corruption. Attackers could exploit it by providing a specially crafted .traineddata file, potentially compromising confidentiality, integrity, and availability of affected systems.

Compliance Impact

This vulnerability could lead to information disclosure or controlled corruption, which may violate data protection requirements under GDPR (e.g., unauthorized access to personal data) and HIPAA (e.g., exposure of protected health information). The potential for heap corruption and crashes may also disrupt systems handling sensitive data, impacting compliance with integrity and availability requirements.

Mitigation Strategies

Immediately stop using any .traineddata files from untrusted sources. Only use traineddata files from official Tesseract repositories or trusted providers. Monitor for crashes or memory corruption during OCR processing as potential indicators of exploitation.

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