CVE-2026-42440 (GCVE-0-2026-42440)
Vulnerability from cvelistv5 – Published: 2026-05-04 16:40 – Updated: 2026-05-05 16:03
VLAI?
Title
Apache OpenNLP: OOM DoS via Unbounded Array Allocation in AbstractModelReader
Summary
OOM Denial of Service via Unbounded Array Allocation in Apache OpenNLP AbstractModelReader
Versions Affected:
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. Any code path that deserializes a .bin model is affected, including direct use of GenericModelReader and any higher-level component that delegates to it during model load.
The practical impact is denial of service against processes that load model files from untrusted or semi-trusted origins.
Mitigation:
* 2.x users should upgrade to 2.5.9.
* 3.x users should upgrade to 3.0.0-M3.
Note: The fix introduces an upper bound on each of the three count fields, checked before array allocation; counts that are negative or exceed the bound cause an IllegalArgumentException to be thrown and the read to fail fast with no large allocation. The default bound is 10,000,000, which is well above the entry counts of legitimate OpenNLP models but far below any value that would threaten heap exhaustion. 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.
Severity ?
No CVSS data available.
CWE
- CWE-789 - Memory Allocation with Excessive Size Value
Assigner
References
| URL | Tags | ||||
|---|---|---|---|---|---|
|
|||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| Apache Software Foundation | Apache OpenNLP |
Affected:
0 , < 2.5.9
(semver)
Affected: 3.0 , < 3.0.0-M3 (semver) |
Credits
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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.\\n\\n\\nUsers 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.\", \"supportingMedia\": [{\"type\": \"text/html\", \"value\": \"\u003cp\u003e\u003cb\u003eOOM Denial of Service via Unbounded Array Allocation in Apache OpenNLP AbstractModelReader\u0026nbsp;\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eVersions Affected:\u003c/b\u003e\u0026nbsp;\u003c/p\u003e\u003cp\u003ebefore 2.5.9\u003c/p\u003e\u003cp\u003ebefore 3.0.0-M3\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cb\u003eDescription:\u003c/b\u003e\u003c/p\u003e\\n\u003cp\u003eThe \u003ccode\u003eAbstractModelReader\u003c/code\u003e methods \u003ccode\u003egetOutcomes()\u003c/code\u003e, \u003ccode\u003egetOutcomePatterns()\u003c/code\u003e, and \u003ccode\u003egetPredicates()\u003c/code\u003e each read a 32-bit signed integer count field from a binary model stream and pass that value directly to an array allocation (\u003ccode\u003enew String[numOutcomes]\u003c/code\u003e, \u003ccode\u003enew int[numOCTypes][]\u003c/code\u003e, \u003ccode\u003enew String[NUM_PREDS]\u003c/code\u003e) 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.\u003c/p\u003e\\n\u003cp\u003eA crafted \u003ccode\u003e.bin\u003c/code\u003e model file in which any of these count fields is set to \u003ccode\u003eInteger.MAX_VALUE\u003c/code\u003e (or any value large enough to exhaust the available heap) triggers an \u003ccode\u003eOutOfMemoryError\u003c/code\u003e 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, \u003ccode\u003egetOutcomes()\u003c/code\u003e 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. Any code path that deserializes a \u003ccode\u003e.bin\u003c/code\u003e model is affected, including direct use of \u003ccode\u003eGenericModelReader\u003c/code\u003e and any higher-level component that delegates to it during model load.\u003c/p\u003e\\n\u003cp\u003eThe practical impact is denial of service against processes that load model files from untrusted or semi-trusted origins.\u0026nbsp;\u0026nbsp;\u003c/p\u003e\\n\u003cp\u003e\u003cb\u003eMitigation:\u003c/b\u003e\u003c/p\u003e\\n\u003cul\u003e\\n\u003cli\u003e2.x users should upgrade to 2.5.9.\u003c/li\u003e\\n\u003cli\u003e3.x users should upgrade to 3.0.0-M3.\u003c/li\u003e\\n\u003c/ul\u003e\\n\u003cp\u003e\u003cb\u003eNote:\u003c/b\u003e The fix introduces an upper bound on each of the three count fields, checked before array allocation; counts that are negative or exceed the bound cause an \u003ccode\u003eIllegalArgumentException\u003c/code\u003e to be thrown and the read to fail fast with no large allocation. The default bound is 10,000,000, which is well above the entry counts of legitimate OpenNLP models but far below any value that would threaten heap exhaustion. Deployments that legitimately need to load models with more entries than the default can raise the limit at JVM startup by setting the \u003ccode\u003eOPENNLP_MAX_ENTRIES\u003c/code\u003e system property to the desired positive integer (e.g. \u003ccode\u003e-DOPENNLP_MAX_ENTRIES=50000000\u003c/code\u003e); invalid or non-positive values fall back to the default.\u003c/p\u003e\\n\u003cp\u003eUsers who cannot upgrade immediately should treat all \u003ccode\u003e.bin\u003c/code\u003e 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.\u0026nbsp;\u003c/p\u003e\", \"base64\": false}]}], \"problemTypes\": [{\"descriptions\": [{\"lang\": \"en\", \"type\": \"CWE\", \"cweId\": \"CWE-789\", \"description\": \"CWE-789: Memory Allocation with Excessive Size Value\"}]}], \"providerMetadata\": {\"orgId\": \"f0158376-9dc2-43b6-827c-5f631a4d8d09\", \"shortName\": \"apache\", \"dateUpdated\": \"2026-05-04T16:40:32.503Z\"}}}",
"cveMetadata": "{\"cveId\": \"CVE-2026-42440\", \"state\": \"PUBLISHED\", \"dateUpdated\": \"2026-05-05T16:03:03.237Z\", \"dateReserved\": \"2026-04-27T12:43:14.347Z\", \"assignerOrgId\": \"f0158376-9dc2-43b6-827c-5f631a4d8d09\", \"datePublished\": \"2026-05-04T16:40:32.503Z\", \"assignerShortName\": \"apache\"}",
"dataType": "CVE_RECORD",
"dataVersion": "5.2"
}
}
}
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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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