CVE-2026-42440 (GCVE-0-2026-42440)

Vulnerability from cvelistv5 – Published: 2026-05-04 16:40 – Updated: 2026-07-30 12:05
VLAI
Title
Apache OpenNLP: OOM DoS via Unbounded Array Allocation in AbstractModelReader
Summary
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.
Severity
7.5 (High)
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
SSVC
Exploitation: none Automatable: yes Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-05-05 16:00 UTC
CWE
  • CWE-789 - Memory Allocation with Excessive Size Value
  • CWE-770 - Allocation of Resources Without Limits or Throttling
Assigner
apache CNA under the mitre root
CNA scorecard B 76/100 over 1085 records in the last 180 days details
References
Impacted products
Vendor Product Version CPE status
Apache Software Foundation Apache OpenNLP Affected: 2.0 , < 2.5.9 (semver)
Affected: 3.0.0-M1 , < 3.0.0-M3 (semver)
Affected: 0 , < 1.9.5 (semver)
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Red Hat Red Hat Data Grid 8     cpe:/a:redhat:jboss_data_grid:8
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Red Hat Red Hat Fuse 7     cpe:/a:redhat:jboss_fuse:7
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Red Hat Red Hat JBoss Enterprise Application Platform Expansion Pack     cpe:/a:redhat:jbosseapxp
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Red Hat Red Hat OpenShift AI (RHOAI)     cpe:/a:redhat:openshift_ai
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Credits
Subramanian S
Show details on NVD website
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              "value": "OOM Denial of Service via Unbounded Array Allocation in Apache OpenNLP AbstractModelReader\u00a0\n\nVersions Affected:\u00a0\n\nbefore 1.9.5\nbefore 2.5.9\n\nbefore 3.0.0-M3\u00a0\n\nDescription:\n\n\nThe 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.\n\n\nA 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.\n\n\nThe practical impact is denial of service against processes that load model files from untrusted or semi-trusted origins.\u00a0\u00a0\n\n\nMitigation:\n\n\n\n  *  2.x users should upgrade to 2.5.9.\n\n  *  3.x users should upgrade to 3.0.0-M3.\n\n\n\n\nNote: 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.\n\n\nUsers 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."
            }
          ],
          "metrics": [
            {
              "other": {
                "content": {
                  "text": "moderate"
                },
                "type": "Textual description of severity"
              }
            }
          ],
          "problemTypes": [
            {
              "descriptions": [
                {
                  "cweId": "CWE-789",
                  "description": "CWE-789: Memory Allocation with Excessive Size Value",
                  "lang": "en",
                  "type": "CWE"
                }
              ]
            }
          ],
          "providerMetadata": {
            "dateUpdated": "2026-06-30T07:17:38.376Z",
            "orgId": "f0158376-9dc2-43b6-827c-5f631a4d8d09",
            "shortName": "apache"
          },
          "references": [
            {
              "tags": [
                "vendor-advisory"
              ],
              "url": "https://lists.apache.org/thread/s8xlkx1gqbxfsq48py5h6jphjvgqp1jo"
            }
          ],
          "source": {
            "defect": [
              "OPENNLP-1821"
            ],
            "discovery": "UNKNOWN"
          },
          "title": "Apache OpenNLP: OOM DoS via Unbounded Array Allocation in AbstractModelReader",
          "x_generator": {
            "engine": "Vulnogram 0.2.0"
          }
        }
      },
      "cveMetadata": {
        "assignerOrgId": "f0158376-9dc2-43b6-827c-5f631a4d8d09",
        "assignerShortName": "apache",
        "cveId": "CVE-2026-42440",
        "datePublished": "2026-05-04T16:40:32.503Z",
        "dateReserved": "2026-04-27T12:43:14.347Z",
        "dateUpdated": "2026-07-30T12:05:07.579Z",
        "state": "PUBLISHED"
      },
      "dataType": "CVE_RECORD",
      "dataVersion": "5.2"
    }
  }
}



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