CVE-2026-42440
HighCVSS 7.5Exploitation Probability (EPSS)
Low risk45th percentile - higher than 45% of all known CVEs
Summary
In Apache OpenNLP before versions 1.9.5, 2.5.9, and 3.0.0-M3, a denial-of-service vulnerability exists due to unbounded array allocation in AbstractModelReader. The methods getOutcomes(), getOutcomePatterns(), and getPredicates() allocate arrays based on a 32-bit integer from the model file without validation, allowing an attacker to set the value to Integer.MAX_VALUE and trigger an OutOfMemoryError.
Risk Assessment
The risk is that a single small .bin file can crash the entire JVM process, causing service disruption. This is especially dangerous in environments where models are loaded from untrusted sources, such as end users or third-party repositories.
Recommendation
Upgrade immediately to version 2.5.9 (for 2.x users) or 3.0.0-M3 (for 3.x users). If an upgrade is not possible, treat all .bin model files as untrusted and avoid loading models from unverified sources.
Other vulnerabilities in Apache OpenNLP
See all- CVE-2026-43825High
In Apache OpenNLP, the libsvm document categorization module (3.x line) is vulnerable to untrusted Java deserialization. The SvmDoccatModel.deserialize(InputStream) method uses java.io.ObjectInputStream without a filter, allowing an attacker to execute arbitrary code via a crafted stream.
- CVE-2026-42027Critical
In Apache OpenNLP before versions 1.9.5, 2.5.9, and 3.0.0-M3, the ExtensionLoader.instantiateExtension() method loads a class by name from a model archive's manifest.properties using Class.forName() before checking type compatibility. This allows an attacker to execute the static initializer of any class on the classpath during model loading, potentially causing harmful side effects.
- CVE-2026-40682Critical
An XML External Entity (XXE) vulnerability was found in Apache OpenNLP's DictionaryEntryPersistor class. The class initializes a SAX parser without enabling secure processing or disabling DTD handling, allowing an attacker to inject a malicious DOCTYPE declaration in a dictionary file. This can lead to local file disclosure via file:// entity references or server-side request forgery via http:// entity references during XML parsing.
- CVE-2026-63317Medium
In Apache OpenNLP before versions 2.5.10 and 3.0.0-M5, a vulnerability allows arbitrary class instantiation. An attacker can supply a crafted model or format name, leading to loading and executing code from dangerous classes.
Original NVD description (English source)
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.