CVE-2026-67211 — OOM Denial of Service via Unbounded Map Pre-Sizing in Apache OpenNLP…
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.
Who exploits it and how urgent it is
A 0–100 score combining confirmed exploitation, ransomware and APT use, public exploits, EPSS and severity.
How the score was calculated
- Technical severity (high)+6
The score orders the patch queue; it does not replace your asset context (internet exposure, criticality, compensating controls).
Triage
Is it exploited, how likely is exploitation, what does it touch, and how severe do the sources call it.
Remediation
Fixes and mitigations documented by the sources — linked to the original advisory.
No fix documented by the sources we poll. That does not mean none exists: check the vendor advisory.
First 24 hours
Ordered from the record’s own fields — exposure first, because you cannot patch what you have not found. A rule-generated starting point.
- Identify exposed assets running the affected vendor/product/version combinations (Apache Software Foundation Opennlp).
- The attack is network-based and needs no privileges: restrict exposure (ACLs, VPN, WAF) until patched.
- No fix documented in the sources: reduce exposure and monitor the vendor advisory.
- Search available logs for exploit probes, errors and authentication anomalies.
Affected scope
Vendor, product and versions as the sources word them.
| Vendor | Product | Versions | Status | Source |
|---|---|---|---|---|
| Apache Software Foundation | Opennlp | version 3.0.0-m4version 3.0.0-m5 | Vulnerable | NIST National Vulnerability Database |
Attack characteristics
The CVSS vector, decoded. It describes the attack, not your exposure to it.
Every base score collected
Sources score independently and may disagree; each row says who scored it and under which version.
| Score | Version | Severity | Expl. | Impact | Scored by |
|---|---|---|---|---|---|
| 7.5 | CVSS 3.1 | High | 3.9 | 3.6 | 134c704f-9b21-4f2e-91b3-4a467353bcc0 via NIST National Vulnerability Database |
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H (score shown at the top: 134c704f-9b21-4f2e-91b3-4a467353bcc0) Weakness MITRE
Weakness class according to MITRE’s CWE catalog.
Detection
Read off the CVSS vector and the weakness class. Starting points, not tested rules.
- Prioritize edge telemetry for the network-reachable component (proxy, WAF, IDS/IPS).
Timeline
What happened to this CVE, newest first.
- 2026
- Record published
- CVSS changed · 7.5
- Source status changed · → Analyzed
- Record created in this database
- CVE published
Source activity
Repeated readings are counted rather than listed.
- 1× Official description updated · Oct 15, 2026 – Oct 15, 2026
References
2 on the record
Elsewhere on this site
Related CVEs (same products)
- Critical CVE-2026-82617 Inefficient Regular Expression Complexity (ReDoS) in Apache Software Foundation Opennlp Sep 11, 2026
- Medium CVE-2026-63317 Apache OpenNLP: Arbitrary Class Instantiation in GeneratorFactory via Feature Descriptor XML Jul 24, 2026
- High CVE-2026-43825 Apache OpenNLP :: Core :: ML :: LibSVM: Unsafe Java Deserialization in SvmDoccatModel Jul 6, 2026
- High CVE-2026-42440 Apache OpenNLP: OOM DoS via Unbounded Array Allocation in AbstractModelReader May 4, 2026
- Critical CVE-2026-42027 Apache OpenNLP: Arbitrary Class Instantiation via Model Manifest in ExtensionLoader May 4, 2026
- Critical CVE-2026-40682 Apache OpenNLP: XXE via Dictionary Parsing in DictionaryEntryPersistor May 4, 2026
Not in any source we poll
Listed rather than left blank: an empty field and an unmeasured one look identical on screen, and only one is a reason to look elsewhere.
- No FIRST EPSS score.
- No fixed version, patch or mitigation documented.
- No CISA SSVC assessment.
- No confirmed IOCs, IP addresses, domains, file hashes, or malware artifacts supplied.
- No organization-specific asset inventory, compensating-control status, or patch deployment evidence supplied.
- No exploit packet captures, log samples, or incident case IDs supplied.
Answered from this record
Text assembled only from the fields above — no additional claims.
What should defenders know first?
CVE-2026-67211 is "OOM Denial of Service via Unbounded Map Pre-Sizing in Apache OpenNLP…", rated High. It affects Opennlp (Apache Software Foundation); versions are in the scope table. It is not in CISA KEV, which does not rule out exploitation. CVSS 7.5 describes technical severity. First verify whether exposed or business-critical assets run the affected versions, apply vendor fixes or mitigations, and preserve logs for review.
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