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CVE-2026-67211 Apache Software Foundation CVE debrief

A denial-of-service vulnerability exists in Apache OpenNLP's SymSpellModelSerializer. An attacker can cause an OutOfMemoryError by crafting a malicious .bin model file with large count fields, leading to a potential crash of the JVM. This issue affects versions 3.0.0-M4 and 3.0.0-M5. Users should upgrade to 3.0.0-M6 or apply mitigations. The vulnerability allows an attacker to cause a denial of service against processes that load SymSpell model files from untrusted or semi-trusted origins.

Vendor
Apache Software Foundation
Product
Apache OpenNLP
CVSS
Unknown
CISA KEV
Not listed in stored evidence
Original CVE published
2026-09-11
Original CVE updated
2026-09-11
Advisory published
2026-09-11
Advisory updated
2026-09-11

Who should care

Defenders and developers using Apache OpenNLP versions 3.0.0-M4 and 3.0.0-M5 should assess exposure and prioritize upgrading to 3.0.0-M6 or applying mitigations. Defenders and developers should also review compensating controls for exposed systems while remediation is scheduled and verified.

Why it matters

This vulnerability allows an attacker to cause a denial of service against processes that load SymSpell model files from untrusted or semi-trusted origins. Defenders and developers using affected versions should prioritize upgrading or applying mitigations to prevent potential crashes of the JVM.

  • Denial of service against processes that load SymSpell model files from untrusted or semi-trusted origins
  • Potential crash of the JVM due to OutOfMemoryError

Technical summary

The SymSpellModelSerializer.create() method reads two 32-bit signed integer count fields from a binary SymSpell model stream and passes each value directly to LinkedHashMap.newLinkedHashMap() after validating only that it is non-negative. No upper bound is applied, so the count is fully attacker-controlled when the model file originates from an untrusted source. A crafted .bin model file in which either count field is set to Integer.MAX_VALUE (or any value large enough to exhaust the available heap) causes the map to be pre-sized to a capacity of 2^30 entries.

Defensive priority

High

Recommended defensive actions

  • Upgrade to version 3.0.0-M6 or later
  • Treat all SymSpell .bin model files as untrusted input unless their provenance is verified
  • Avoid loading models supplied by end users or fetched from third-party repositories without integrity checks
  • Confirm whether affected product deployments exist in managed environments and assign an owner for follow-up.
  • Review the supplied official advisory or CVE record to validate affected scope, severity, and vendor guidance.
  • Plan vendor-supported updates or mitigations through normal change control where exposure is confirmed.
  • Review compensating controls for exposed systems while remediation is scheduled and verified.

Evidence notes

The CVE record and NVD entry provide details on the vulnerability, including its description, affected versions, and mitigation strategies. The SymSpellModelSerializer.create() method reads two 32-bit signed integer count fields from a binary SymSpell model stream and passes each value directly to LinkedHashMap.newLinkedHashMap() after validating only that it is non-negative. No upper bound is applied, so the count is fully attacker-controlled when the model file originates from an untrusted source. A crafted .bin model file in which

Sources and references

Verified primary and authoritative sources

  • CVE-2026-67211 CVE Program record

    Publisher, destination, and source semantics verified

    URL: https://www.cve.org/CVERecord?id=CVE-2026-67211

    CVE Program - Official CVE Program record with source-provided CVE metadata.

  • CVE-2026-67211 NVD vulnerability detail

    Publisher, destination, and source semantics verified

    URL: https://nvd.nist.gov/vuln/detail/CVE-2026-67211

    NIST National Vulnerability Database - Official NIST NVD detail page and source-specific vulnerability assessment.

Supplemental references

Methodology and review provenance

AI-assisted synthesis based on stored public vulnerability evidence. System validation, approval state, and publication status do not by themselves establish human review of this revision. PatchSiren helps prioritize defensive review and does not prove exposure or remediation on any system.