CVE-2026-67211 in OpenNLP
Summary
by MITRE • 09/11/2026
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.
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Analysis
by VulDB Data Team • 09/11/2026
The vulnerability identified in Apache OpenNLP versions 3.0.0-M4 through 3.0.0-M5 represents a critical resource exhaustion flaw within the SymSpellModelSerializer component, specifically affecting the opennlp-spellcheck extension introduced in version 3.0.0-M4. This issue is classified as an Out-of-Memory (OOM) Denial of Service vulnerability resulting from unbounded map pre-sizing during binary model deserialization. The core technical flaw resides in the SymSpellModelSerializer.create() method, which processes two thirty-two-bit signed integer fields representing unigram and bigram counts extracted directly from a binary SymSpell model stream. While the implementation performs a basic validation to ensure these values are non-negative, it fails to enforce any upper bound on their magnitude before passing them as initial capacity arguments to LinkedHashMap.newLinkedHashMap(). This lack of constraint allows an attacker who controls the input data to dictate the size of the internal backing array allocated by the Java Virtual Machine.
When a crafted binary model file is provided with either count field set to Integer.MAX_VALUE or another sufficiently large value, the application attempts to pre-size the linked hash map accordingly. The subsequent allocation of this oversized backing array occurs during the first put operation into the map and can request between four to eight gigabytes of heap space depending on whether compressed ordinary object pointers are enabled in the JVM configuration. This massive memory allocation inevitably leads to an OutOfMemoryError, causing the process to crash or become unresponsive. The exploitability of this vulnerability is heightened by its minimal payload size; because the vulnerable count fields appear immediately after a fixed-size header comprising magic numbers, format versions, UTF strings, and configuration data, an attacker can trigger the denial of service with a binary file under one hundred bytes plus a single valid entry. This efficiency makes it particularly dangerous in scenarios involving automated processing or high-throughput ingestion of model files from untrusted sources.
The operational impact extends to any code path within Apache OpenNLP that deserializes SymSpell models, including methods such as SymSpellModels.deserialize and SymSpellModels.fromBytes, as well as classpath-based loading via SymSpellModelResolver.resolveByLanguage and command-line tools like CorrectTextTool. Since the opennlp-spellcheck extension is part of the official binary distribution, systems relying on this functionality for natural language processing tasks are directly exposed to remote or local denial-of-service attacks if they accept model files from external parties without rigorous validation. This vulnerability aligns with CWE-400 Uncontrolled Resource Consumption and maps to MITRE ATT&CK technique T1496 Resource Hijacking, where an attacker consumes system resources to degrade service availability for legitimate users. The absence of input sanitization regarding resource allocation limits is a common pattern in legacy serialization logic that assumes all inputs are trusted, which poses significant risks in modern cloud-native and microservice architectures where component isolation is critical.
To mitigate this vulnerability, organizations using Apache OpenNLP version 3.x must upgrade to release 3.0.0-M6 or later. The fix implemented in the patched versions introduces a strict upper bound check on both unigramCount and bigramCount fields prior to any map pre-sizing operations. If either value is negative or exceeds the defined limit, an IOException is thrown immediately, ensuring that no large memory allocations occur during deserialization of malicious inputs. The default maximum entry count is set to ten million, a threshold sufficiently high for legitimate SymSpell dictionaries while remaining far below levels that would threaten heap exhaustion. For deployments requiring larger dictionaries due to specific linguistic requirements or enterprise-scale data processing needs, the limit can be adjusted at JVM startup by setting the OPENNLP_MAX_ENTRIES system property to a desired positive integer value. Invalid or non-positive values provided via this property will automatically fall back to the safe default of ten million entries.
Organizations unable to upgrade immediately should adopt strict input validation practices as an interim mitigation strategy. All SymSpell binary model files must be treated as untrusted input unless their provenance is verified through cryptographic integrity checks such as digital signatures or hash verification against known-good repositories. Loading models supplied by end users, downloaded from third-party sources, or generated dynamically without prior sanitization should be strictly prohibited in production environments until the patch is applied. Additionally, administrators may consider configuring JVM memory limits and garbage collection policies to provide an additional layer of defense-in-depth against potential resource exhaustion attacks, although this does not replace the need for fixing the underlying application logic vulnerability.