CVE-2026-42440 — Apache OpenNLP: OOM DoS via Unbounded Array Allocation in AbstractModelReader
OOM Denial of Service via Unbounded Array Allocation in Apache OpenNLP AbstractModelReader Versions Affected: before 1.9.5 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.
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
- EPSS 1.1% (30-day exploitation probability)+10
- Technical severity (high)+6
- Automatable exploitation (SSVC)+3
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.
CISA SSVC assessment CISA
Three decision points CISA publishes for the CVEs it assesses (SSVC 2.0.3). A stakeholder decision, not a severity score.
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 | < 2.5.9version 3.0.0-m1version 3.0.0-m2 | Vulnerable | NIST National Vulnerability Database |
| Apache Software Foundation | Opennlp | ≥ 2.0 and < 2.5.9≥ 3.0.0-M1 and < 3.0.0-M3< 1.9.5 | Vulnerable | CVE Program (cve.org) e CISA ADP |
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 |
| 7.5 | CVSS 3.1 | High | 3.9 | 3.6 | 0b0ca135-0b70-47e7-9f44-1890c2a1c46c via NIST National Vulnerability Database |
| 7.5 | CVSS 3.1 | High | — | — | CISA-ADP via CVE Program (cve.org) e CISA ADP |
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.
EPSS over time FIRST
Oct 13, 2026 → Oct 14, 2026 · 2 reading(s) · max 1.07%
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
- Affected products added · apache software foundation/apache opennlp
- SSVC assessment updated
- Record published
- CVSS changed · 7.5
- Source status changed · → Modified
- Record created in this database
- CVE published
Source activity
Repeated readings are counted rather than listed.
- 1× Official description updated · Oct 13, 2026 – Oct 13, 2026
References
5 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
- High CVE-2026-67211 OOM Denial of Service via Unbounded Map Pre-Sizing in Apache 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
- 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 fixed version, patch or mitigation documented.
- 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-2026-42440 is "Apache OpenNLP: OOM DoS via Unbounded Array Allocation in AbstractModelReader", 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. EPSS 1.07% estimates the probability of exploitation in 30 days; neither replaces your asset context. 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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