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High7.5Apache Software Foundation

High [CVE-2026-42440] Apache OpenNLP: OOM DoS via Unbounded Array Allocation in AbstractModelReader

This high-severity Apache Software Foundation advisory covers CVE-2026-42440 affecting Apache OpenNLP AbstractModelReader.

CVE-2026-42440 Published May 4, 2026Updated by vendor Jul 30, 2026
Affected products & platforms
Apache Software FoundationUnclassified
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Summary

OOM Denial of Service via Unbounded Array Allocation in Apache OpenNLP AbstractModelReader Versions Affected: before 1.9.5 before 2.5.9 before 3.0.0-M3 Description: The AbstractModelReader methods getOutcomes(), getOutcomePatterns(), and getPredicates() each read a 32-bit signed integer count field from a binary model stream and pass that value directly to an array allocation (new String[numOutcomes], new int[numOCTypes][], new String[NUM_PREDS]) without validating that the value is non-negative or within a reasonable bound.

The count is therefore fully attacker-controlled when the model file originates from an untrusted source.

A crafted.bin model file in which any of these count fields is set to Integer.MAX_VALUE (or any value large enough to exhaust the available heap) triggers an OutOfMemoryError at the array allocation itself, before the corresponding label or pattern data is consumed from the stream.

The error occurs very early in deserialization: for a GIS model, getOutcomes() is reached after only the model-type string, the correction constant, and the correction parameter have been read; so the attacker pays no meaningful size cost to weaponize a payload, and a single small file can crash a JVM that loads it.

Affected versions
  • Apache OpenNLP 2.0 before 2.5.9
  • Apache OpenNLP 3.0.0-M1 before 3.0.0-M3
  • Apache OpenNLP before 1.9.5

Official advisory · high-confidence parse· fetched 1 month ago·verify at source

Fixed versions
  • 2.5.9
  • 3.0.0-M3
  • 1.9.5

Official advisory · high-confidence parse· fetched 1 month ago·verify at source

Mitigation checklist

Recommended fix / mitigation
  • Mitigation: * 2.x users should upgrade to 2.5.9. * 3.x users should upgrade to 3.0.0-M3.
  • Deployments that legitimately need to load models with more entries than the default can raise the limit at JVM startup by setting the OPENNLP_MAX_ENTRIES system property to the desired positive integer (e.g. -DOPENNLP_MAX_ENTRIES=50000000); invalid or non-positive values fall back to the default.
  • Users who cannot upgrade immediately should treat all.bin model files as untrusted input unless their provenance is verified, and should avoid loading models supplied by end users or fetched from third-party repositories without integrity checks.

Official advisory · high-confidence parse· fetched 1 month ago·verify at source

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