CVE-2026-42440 Apache OpenNLP: OOM DoS via Unbounded Array Allocation in AbstractModelReader
Attacker can cause denial of service via crafted model file triggering out-of-memory error in Apache OpenNLP library.
- Exploited in the wild
- Not known
- Login needed
- Not needed
- Attackable
- Over the internet
- Victim action needed
- Not needed
- Fix
- Available
What is CVE-2026-42440?
CVE-2026-42440 is a high-severity denial of service vulnerability in Apache OpenNLP, published on 4 May 2026. An attacker can exploit it over the internet without logging in, with no action from the victim.
Official description
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.
Which versions are affected by CVE-2026-42440?
4 open source packages ship an affected version, including org.apache.opennlp:opennlp-tools. It affects 1 product.
| Package | Affected | Fixed in |
|---|---|---|
org.apache.opennlp:opennlp-tools Maven | All versions | 2.5.9 |
org.apache.opennlp:opennlp-tools Maven | All versions | 2.5.9 |
org.apache.opennlp:opennlp-tools Maven | From 3.0.0-M1 | 3.0.0-M3 |
org.apache.opennlp:opennlp-tools Maven | From 3.0.0-M1 | 3.0.0-M3 |
Products include Apache Opennlp.
How do I fix CVE-2026-42440?
Upgrade org.apache.opennlp:opennlp-tools to a fixed version for your release line: 2.5.9 or 3.0.0-M3.
Upgrade, then put limits on request size, rate and nesting depth so one client cannot exhaust the service. More about Denial of service.
Is CVE-2026-42440 being exploited?
There is no known exploitation of CVE-2026-42440 in the wild and no public exploit code.
Other vulnerabilities in Apache OpenNLP
- CVE-2026-82617 Apache OpenNLP, Apache OpenNLP: ReDoS / stack exhaustion in RegexNameFinderFactory built-in EMAIL and URL patterns
- CVE-2026-42027 Apache OpenNLP: Arbitrary Class Instantiation via Model Manifest in ExtensionLoader
- CVE-2017-12620 Improper Restriction of XML External Entity Reference in Apache OpenNLP
- CVE-2026-40682 Apache OpenNLP: XXE via Dictionary Parsing in DictionaryEntryPersistor
- CVE-2026-67211 Apache OpenNLP: OOM DoS via Unbounded Array Allocation in SymSpellModelSerializer
Sources
Also known as DEBIAN-CVE-2026-42440, GHSA-659w-93r5-9j6m, UBUNTU-CVE-2026-42440.