SCA: security update for org.apache.opennlp:opennlp-tools (GHSA-659w-93r5-9j6m)

high Tenable Cloud Security Plugin ID 441551

Description

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)

Solution

Update the org.apache.opennlp:opennlp-tools library and its related packages to version 2.5.9 or later.

Plugin Details

Severity: High

ID: 441551

Version: Revision 1.6

Type: Local

Family: SCA Checks

Published: 5/8/2026

Updated: 7/1/2026

Supported Sensors: Tenable Cloud Security, Tenable Self-Hosted Container Security

Risk Information

VPR

Risk Factor: Low

Score: 3

Percentile: 23.73

Vendor

Vendor Severity: High

CVSS v2

Risk Factor: High

Base Score: 7.8

Temporal Score: 5.8

Vector: CVSS2#AV:N/AC:L/Au:N/C:N/I:N/A:C

CVSS Score Source: CVE-2026-42440

CVSS v3

Risk Factor: High

Base Score: 7.5

Temporal Score: 6.5

Vector: CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

Temporal Vector: CVSS:3.0/E:U/RL:O/RC:C

Vulnerability Information

Exploit Ease: No known exploits are available

Patch Publication Date: 5/4/2026

Vulnerability Publication Date: 5/4/2026

Reference Information

CVE: CVE-2026-42440

cwe: CWE-789

Detections

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