Apache OpenNLP up to 3.0.0-M5 opennlp-spellcheck extension SymSpellModelSerializer.create unigramCount/bigramCount allocation of resources

Summaryinfo

A vulnerability, which was classified as problematic, was found in Apache OpenNLP up to 3.0.0-M5. This impacts the function SymSpellModelSerializer.create of the component opennlp-spellcheck extension. Such manipulation of the argument unigramCount/bigramCount leads to allocation of resources. This vulnerability is listed as CVE-2026-67211. The attack may be performed from remote. There is no available exploit. You should upgrade the affected component.

Detailsinfo

A vulnerability was found in Apache OpenNLP up to 3.0.0-M5. It has been rated as problematic. Affected by this issue is the function SymSpellModelSerializer.create of the component opennlp-spellcheck extension. The manipulation of the argument unigramCount/bigramCount with an unknown input leads to a allocation of resources vulnerability. Using CWE to declare the problem leads to CWE-770. The product allocates a reusable resource or group of resources on behalf of an actor without imposing any restrictions on the size or number of resources that can be allocated, in violation of the intended security policy for that actor. Impacted is availability. CVE summarizes:

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.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. 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.MAX_ENTRIES 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 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. 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.

The advisory is shared for download at lists.apache.org. This vulnerability is handled as CVE-2026-67211 since 07/28/2026. The exploitation is known to be easy. The attack may be launched remotely. No form of authentication is required for exploitation. There are known technical details, but no exploit is available. The current price for an exploit might be approx. USD $0-$5k (estimation calculated on 09/11/2026). The MITRE ATT&CK project declares the attack technique as T1499.

Upgrading to version 3.0.0-M6 eliminates this vulnerability.

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Productinfo

Vendor

• Apache

Name

• OpenNLP

Version

• 3.0.0-M5

License

• open-source

Website

• Vendor: https://www.apache.org/

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