CVE-2026-67211: Apache OpenNLP: OOM DoS via Unbounded Array Allocation in SymSpellModelSerializer

Published Sep 11, 2026
·
Updated

OOM Denial of Service via Unbounded Map Pre-Sizing in Apache OpenNLP SymSpellModelSerializer

Versions Affected:

- 3.0.0-M4 - 3.0.0-M5

(The opennlp-spellcheck extension was introduced in 3.0.0-M4. Releases 1.x and 2.x do not contain the affected code.)

Description:

The SymSpellModelSerializer.create() method reads two 32-bit signed integer count fields (unigramCount and bigramCount) 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.MAXVALUE (or any value large enough to exhaust the available heap) causes the map to be pre-sized to a capacity of 2^30 entries. The oversized backing array is allocated on the first put() into that map, requesting 4–8 GB depending on whether compressed oops are in effect, and the load fails with an OutOfMemoryError. Because the count fields sit immediately after a fixed-size header (magic, format version, three UTF strings, the configuration fields, and the edit-distance identifier) the attacker pays no meaningful size cost to weaponize a payload: a file of well under 100 bytes plus a single real entry is sufficient to crash a JVM that loads it.

Any code path that deserializes a SymSpell model is affected, including SymSpellModels.deserialize(InputStream), SymSpellModels.fromBytes(byte[]), classpath model loading via SymSpellModelResolver.resolveByLanguage(String), the CorrectTextTool command-line tool, and model-archive loading through the registered ArtifactSerializer. The opennlp-spellcheck extension ships in the official OpenNLP binary distribution.

The practical impact is denial of service against processes that load SymSpell model files from untrusted or semi-trusted origins.

Mitigation:

- 3.x users should upgrade to 3.0.0-M6.

Note: The fix applies an upper bound to both count fields, checked before the map is pre-sized; counts that are negative or exceed the bound cause an IOException to be thrown and the read to fail fast with no large allocation. The bound is the existing AbstractModelReader.MAXENTRIES limit introduced earlie, which the current change promotes to public visibility so that serializers implementing their own binary format can share it. The default bound is 10,000,000, which is well above the entry counts of legitimate SymSpell dictionaries but far below any value that would threaten heap exhaustion. Deployments that legitimately need to load larger dictionaries can raise the limit at JVM startup by setting the OPENNLPMAXENTRIES system property to the desired positive integer (e.g. -DOPENNLPMAXENTRIES=50000000); invalid or non-positive values fall back to the default. Note that this property is shared with the model-reader limit and raising it relaxes both.

Users who cannot upgrade immediately should treat all SymSpell .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.

Affected Software

1 affected component
Apache OpenNLP>=3.0.0-M4<3.0.0-M6

Remediation

Recommended actions to resolve this vulnerability, in priority order.

  1. Upgrade

    Upgrade apache/opennlp to a version that resolves this vulnerability.

    Fixed in 3.0.0-M6
  2. Configuration

    If you must load larger SymSpell dictionaries, set the OPENNLP_MAX_ENTRIES system property to the desired positive integer at JVM startup. Note: this property is shared with the model-reader limit, and raising it relaxes both.

    Apache OpenNLP (JVM) OPENNLP_MAX_ENTRIES = desired positive integer
  3. Compensating control

    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.

Event History

Sep 11, 2026
CVE Published
via MITRE·05:49 PM
Data Sourced
via MITRE·05:49 PM
DescriptionWeakness

Frequently Asked Questions

1

Which deployments are exposed?

Deployments using the opennlp-spellcheck extension in Apache OpenNLP 3.0.0-M4 or 3.0.0-M5 are affected when they load SymSpell binary model files from untrusted sources. Apache OpenNLP 1.x and 2.x do not contain the affected code.

2

What must an attacker control to trigger the denial of service?

An attacker needs to supply or cause the application to load a crafted SymSpell .bin model file. The file can set either serialized count field to a value large enough to exhaust the JVM heap during model loading.

3

What is the practical impact when exploitation succeeds?

The first insertion into the pre-sized map can request a 4–8 GB backing array, depending on compressed-oops behavior. Model loading then fails with an OutOfMemoryError, producing a denial of service.

4

What can be done if an update is not immediately available?

Do not load SymSpell model files from untrusted sources. Restrict model-file upload, replacement, and retrieval paths so that only trusted, validated model artifacts can be loaded.

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