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.

There are packages installed that are affected by a vulnerability referenced in the following CVE:

  - 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. (CVE-2026-42440)

The Linux/Unix host has one or more packages installed that are impacted by a vulnerability without a vendor supplied patch available.

  - 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. (CVE-2026-42440)

Note that Nessus relies on the presence of the package as reported by the vendor.

ID	Name	Product	Family	Severity
441551	SCA: security update for org.apache.opennlp:opennlp-tools (GHSA-659w-93r5-9j6m)	Tenable Cloud Security	SCA Checks	high
441551	SCA: security update for org.apache.opennlp:opennlp-tools (GHSA-659w-93r5-9j6m)	Tenable Self-Hosted Container Security	SCA Checks	high
312196	Linux Distros Unpatched Vulnerability : CVE-2026-42440	Nessus	Misc.	high

Detections

Analytics

CVE-2026-42440

high

Tenable Plugins

high

high

high