OPENSUSE-SU-2026:21957-1
Vulnerability from csaf_opensuse - Published: 2026-09-24 15:02 - Updated: 2026-10-01 16:39Summary
Security update for tesseract-ocr
Severity
Important
Notes
Title of the patch: Security update for tesseract-ocr
Description of the patch: 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)
Patchnames: openSUSE-Leap-16.0-packagehub-626
Terms of use: CSAF 2.0 data is provided by SUSE under the Creative Commons License 4.0 with Attribution (CC-BY-4.0).
5.5 (Medium)
Affected products
Recommended
14 products
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: openSUSE Leap 16.0:libtesseract5-0:5.5.3-bp160.1.1.aarch64 | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Leap 16.0:libtesseract5-0:5.5.3-bp160.1.1.ppc64le | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Leap 16.0:libtesseract5-0:5.5.3-bp160.1.1.s390x | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Leap 16.0:libtesseract5-0:5.5.3-bp160.1.1.x86_64 | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Leap 16.0:libtesseract5-x86-64-v3-0:5.5.3-bp160.1.1.x86_64 | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Leap 16.0:tesseract-ocr-0:5.5.3-bp160.1.1.aarch64 | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Leap 16.0:tesseract-ocr-0:5.5.3-bp160.1.1.ppc64le | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Leap 16.0:tesseract-ocr-0:5.5.3-bp160.1.1.s390x | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Leap 16.0:tesseract-ocr-0:5.5.3-bp160.1.1.x86_64 | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Leap 16.0:tesseract-ocr-common-0:5.5.3-bp160.1.1.noarch | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Leap 16.0:tesseract-ocr-devel-0:5.5.3-bp160.1.1.aarch64 | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Leap 16.0:tesseract-ocr-devel-0:5.5.3-bp160.1.1.ppc64le | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Leap 16.0:tesseract-ocr-devel-0:5.5.3-bp160.1.1.s390x | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Leap 16.0:tesseract-ocr-devel-0:5.5.3-bp160.1.1.x86_64 | — |
Vendor Fix
|
Threats
Impact
moderate
8.4 (High)
Affected products
Recommended
14 products, the same list as for
CVE-2026-73067
Threats
Impact
important
7.8 (High)
Affected products
Recommended
14 products, the same list as for
CVE-2026-73067
Threats
Impact
important
7.8 (High)
Affected products
Recommended
14 products, the same list as for
CVE-2026-73067
Threats
Impact
important
5.5 (Medium)
Affected products
Recommended
14 products, the same list as for
CVE-2026-73067
Threats
Impact
moderate
7.8 (High)
Affected products
Recommended
14 products, the same list as for
CVE-2026-73067
Threats
Impact
important
7.8 (High)
Affected products
Recommended
14 products, the same list as for
CVE-2026-73067
Threats
Impact
important
8.4 (High)
Affected products
Recommended
14 products, the same list as for
CVE-2026-73067
Threats
Impact
important
5.5 (Medium)
Affected products
Recommended
14 products, the same list as for
CVE-2026-73067
Threats
Impact
moderate
References
41 references
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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