CVE-2026-73067

A commercial quality OCR engine originally developed at HP between 1985 and 1995. In 1995, this engine was among the top 3 evaluated by UNLV. It was open-sourced by HP and UNLV in 2005.

Security Fix(es):

Tesseract is an open source OCR engine. Prior to 5.5.3, a crafted .traineddata LSTM model component loaded through Tesseract's deserializer can cause an unchecked signed integer multiplication in Convolve::DeSerialize in src/lstm/convolve.cpp to wrap the convolution output-channel count, undersizing the forward-pass output buffer while writes use the unwrapped element count and causing a heap out-of-bounds write during OCR recognition. This issue is fixed in version 5.5.3.(CVE-2026-73066)

Tesseract is an open source OCR engine. Prior to 5.5.3, a crafted .traineddata model loaded through TessBaseAPI::Init can cause SquishedDawg::read_squished_dawg in src/dict/dawg.cpp to accept an unterminated forward-edge run, after which SquishedDawg::Load calls num_forward_edges(0) and last_edge in src/dict/dawg.h reads beyond edges_, causing a heap out-of-bounds read and process crash before image processing. This issue is fixed in version 5.5.3.(CVE-2026-73067)

A commercial quality OCR engine originally developed at HP between 1985 and 1995. In 1995, this engine was among the top 3 evaluated by UNLV. It was open-sourced by HP and UNLV in 2005.

Security Fix(es):

Tesseract is an open source OCR engine. Prior to 5.5.3, a crafted .traineddata LSTM model component loaded through Tesseract's deserializer can cause an unchecked signed integer multiplication in Convolve::DeSerialize in src/lstm/convolve.cpp to wrap the convolution output-channel count, undersizing the forward-pass output buffer while writes use the unwrapped element count and causing a heap out-of-bounds write during OCR recognition. This issue is fixed in version 5.5.3.(CVE-2026-73066)

Tesseract is an open source OCR engine. Prior to 5.5.3, a crafted .traineddata model loaded through TessBaseAPI::Init can cause SquishedDawg::read_squished_dawg in src/dict/dawg.cpp to accept an unterminated forward-edge run, after which SquishedDawg::Load calls num_forward_edges(0) and last_edge in src/dict/dawg.h reads beyond edges_, causing a heap out-of-bounds read and process crash before image processing. This issue is fixed in version 5.5.3.(CVE-2026-73067)

A commercial quality OCR engine originally developed at HP between 1985 and 1995. In 1995, this engine was among the top 3 evaluated by UNLV. It was open-sourced by HP and UNLV in 2005.

Security Fix(es):

Tesseract is an open source OCR engine. Prior to 5.5.3, a crafted .traineddata LSTM model component loaded through Tesseract's deserializer can cause an unchecked signed integer multiplication in Convolve::DeSerialize in src/lstm/convolve.cpp to wrap the convolution output-channel count, undersizing the forward-pass output buffer while writes use the unwrapped element count and causing a heap out-of-bounds write during OCR recognition. This issue is fixed in version 5.5.3.(CVE-2026-73066)

Tesseract is an open source OCR engine. Prior to 5.5.3, a crafted .traineddata model loaded through TessBaseAPI::Init can cause SquishedDawg::read_squished_dawg in src/dict/dawg.cpp to accept an unterminated forward-edge run, after which SquishedDawg::Load calls num_forward_edges(0) and last_edge in src/dict/dawg.h reads beyond edges_, causing a heap out-of-bounds read and process crash before image processing. This issue is fixed in version 5.5.3.(CVE-2026-73067)

Tesseract is an open source OCR engine. Prior to 5.5.3, a crafted .traineddata model loaded through TessBaseAPI::Init can cause SquishedDawg::read_squished_dawg in src/dict/dawg.cpp to accept an unterminated forward-edge run, after which SquishedDawg::Load calls num_forward_edges(0) and last_edge in src/dict/dawg.h reads beyond edges_, causing a heap out-of-bounds read and process crash before image processing. This issue is fixed in version 5.5.3.

This is a security update. It fixes nine vulnerabilities in tesseract, the OCR (text recognition) engine. All nine can be triggered only by feeding tesseract a maliciously crafted input file: either a specially crafted recognition/language data file (.traineddata) or a crafted word-list file. Depending on the specific flaw, this can cause a crash (denial of service) or memory corruption. CVE-2026-73067: out-of-bounds read when loading a crafted word-list file. CVE-2026-73066: out-of-bounds write when loading a crafted recognition model file. CVE-2026-88047: stack buffer overflow when loading a crafted language-normalisation file. CVE-2026-88048: out-of-bounds read/write from a crafted neural-network layer in a recognition model file. CVE-2026-88049: out-of-bounds write from a crafted neural-network layer in a recognition model file. CVE-2026-88050: out-of-bounds write from invalid character-encoding values in a crafted recognition model file. CVE-2026-88051: out-of-bounds write when loading a malformed internal data structure from a crafted recognition model file. CVE-2026-88052: out-of-bounds write when loading a crafted character-set file. CVE-2026-88053: out-of-bounds write when loading a crafted legacy recognition template file. CVE-2026-88054: crash (denial of service) when loading a crafted recognition model with an empty internal network. The tesseract package is updated to version 5.5.3, which on its own fixes CVE-2026-73067 and CVE-2026-73066. The remaining seven issues (CVE-2026-88047 to CVE-2026-88054) are not yet fixed in any upstream release, so nine patches taken from upstream's main development branch are also applied. Reported by Tristan Madani (CVE-2026-88047) and Zhixi "Jace" Sun (CVE-2026-88048 to CVE-2026-88054); see the individual advisories below for full credits, including CVE-2026-73067 and CVE-2026-73066.

These are all security issues fixed in the libtesseract5-5.5.3-2.1 package on the GA media of openSUSE Tumbleweed.

This update for tesseract-ocr fixes the following issues:

Changes in tesseract-ocr:

• Update to version 5.5.3 (sync with Factory):
  • CVE-2026-73067: heap out-of-bounds read in SquishedDawg on crafted model (boo#1275623)
  • CVE-2026-88047: stack buffer overflow in Classify::ReadNormProtos on crafted traineddata (boo#1280925)
  • CVE-2026-88048: heap out-of-bounds write/read in FullyConnected::Forward via dimension mismatch (boo#1280929)
  • CVE-2026-88049: heap out-of-bounds write in LSTM::Forward via na_/gate-matrix dimension mismatch (boo#1280930)
  • CVE-2026-88050: out-of-bounds write in UnicharCompress via unvalidated recoder code values (boo#1280931)
  • CVE-2026-88051: heap out-of-bounds write in GenericVector<T>::read via reserved/size_used_ mismatch (boo#1280932)
  • CVE-2026-88052: heap out-of-bounds write in UNICHARSET::load_via_fgets via count/insert desynchronization (boo#1280933)
  • CVE-2026-88053: heap out-of-bounds write in Classify::ReadIntTemplates via unvalidated counts in crafted traineddata (boo#1280934)
  • CVE-2026-88054: denial of service via empty-stack dereference at model load (boo#1280935)

It was discovered that Tesseract incorrectly handled crafted .traineddata models. An attacker could possibly use this issue to cause Tesseract to crash, resulting in a denial of service. (CVE-2026-73067)

It was discovered that Tesseract did not properly validate dimensions in crafted .traineddata models. An attacker could possibly use this issue to execute arbitrary code. (CVE-2026-73066)

It was discovered that Tesseract did not properly limit token lengths in crafted .traineddata models. An attacker could possibly use this issue to execute arbitrary code. (CVE-2026-88047)

It was discovered that Tesseract did not properly validate layer dimensions in crafted .traineddata models. An attacker could possibly use this issue to execute arbitrary code. (CVE-2026-88048)

It was discovered that Tesseract did not properly validate layer sizes in crafted .traineddata models. An attacker could possibly use this issue to execute arbitrary code. (CVE-2026-88049)

It was discovered that Tesseract incorrectly handled negative character codes in crafted .traineddata models. An attacker could possibly use this issue to cause Tesseract to crash, resulting in a denial of service. (CVE-2026-88050)

It was discovered that Tesseract did not properly validate vector sizes in crafted .traineddata models. An attacker could possibly use this issue to execute arbitrary code. (CVE-2026-88051)

It was discovered that Tesseract did not properly validate character IDs in crafted .traineddata models. An attacker could possibly use this issue to execute arbitrary code. (CVE-2026-88052)

It was discovered that Tesseract did not properly validate classifier counts in crafted .traineddata models. An attacker could possibly use this issue to execute arbitrary code. (CVE-2026-88053)

It was discovered that Tesseract incorrectly handled empty network layers in crafted .traineddata models. An attacker could possibly use this issue to cause Tesseract to crash, resulting in a denial of service. (CVE-2026-88054)