fix(deps): update dependency org.apache.opennlp:opennlp-tools to v2 [security]

[renovate]

This PR contains the following updates:

Package	Type	Update	Change	OpenSSF
org.apache.opennlp:opennlp-tools (source)	compile	major	1.9.5 → 2.5.9

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Apache OpenNLP DictionaryEntryPersistor Vulnerable to XML External Entity (XXE) via Unsanitized Dictionary Parsing

CVE-2026-40682 / GHSA-4v8g-86x5-3vrc

More information

Details

XML External Entity (XXE) via Unsanitized Dictionary Parsing in Apache OpenNLP DictionaryEntryPersistor

Versions Affected: before 2.5.9, before 3.0.0-M3

Description: The DictionaryEntryPersistor class initializes a static SAXParserFactory at class-load time without enabling FEATURE_SECURE_PROCESSING or disabling DTD processing. When create(InputStream, EntryInserter) is invoked, the only feature set on the XMLReader is namespace support — external entity resolution and DOCTYPE declarations remain fully enabled. An attacker who can supply a crafted dictionary file (e.g., a stop-word list or domain dictionary) containing a malicious DOCTYPE declaration can trigger local file disclosure via file:// entity references or server-side request forgery via http:// entity references during SAX parsing, before the application processes a single dictionary entry. This is inconsistent with the project's own XmlUtil.createSaxParser() helper, which correctly sets FEATURE_SECURE_PROCESSING and disallow-doctype-decl and is used by all other XML parsing paths in the codebase. The public Dictionary(InputStream) constructor delegates directly to this method and is the documented API for loading user-supplied dictionaries, making untrusted input a realistic scenario.

Mitigation: 2.x users should upgrade to 2.5.9. 3.x users should upgrade to 3.0.0-M3. Users who cannot upgrade immediately should ensure that all dictionary files are sourced from trusted origins and should consider wrapping the Dictionary(InputStream) constructor with input validation that rejects any XML containing a DOCTYPE declaration before it reaches the parser.

Severity

• CVSS Score: 9.1 / 10 (Critical)
• Vector String: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:N

References

• https://nvd.nist.gov/vuln/detail/CVE-2026-40682
• https://lists.apache.org/thread/r6jpt0qr9nj67gqhppqg7jxf8vsbo0w6
• http://www.openwall.com/lists/oss-security/2026/05/01/19
• https://github.com/advisories/GHSA-4v8g-86x5-3vrc

This data is provided by the GitHub Advisory Database (CC-BY 4.0).

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Apache OpenNLP AbstractModelReader has an OOM Denial of Service via Unbounded Array Allocation

CVE-2026-42440 / GHSA-659w-93r5-9j6m

More information

Details

OOM Denial of Service via Unbounded Array Allocation in Apache OpenNLP AbstractModelReader 

Versions Affected: 

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

• CVSS Score: 7.5 / 10 (High)
• Vector String: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

References

• https://nvd.nist.gov/vuln/detail/CVE-2026-42440
• https://lists.apache.org/thread/s8xlkx1gqbxfsq48py5h6jphjvgqp1jo
• http://www.openwall.com/lists/oss-security/2026/05/01/21
• https://github.com/advisories/GHSA-659w-93r5-9j6m

This data is provided by the GitHub Advisory Database (CC-BY 4.0).

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Apache OpenNLP DictionaryEntryPersistor Vulnerable to XML External Entity (XXE) via Unsanitized Dictionary Parsing

CVE-2026-40682 / GHSA-4v8g-86x5-3vrc

More information

Details

XML External Entity (XXE) via Unsanitized Dictionary Parsing in Apache OpenNLP DictionaryEntryPersistor

Versions Affected: before 2.5.9, before 3.0.0-M3

Description: The DictionaryEntryPersistor class initializes a static SAXParserFactory at class-load time without enabling FEATURE_SECURE_PROCESSING or disabling DTD processing. When create(InputStream, EntryInserter) is invoked, the only feature set on the XMLReader is namespace support — external entity resolution and DOCTYPE declarations remain fully enabled. An attacker who can supply a crafted dictionary file (e.g., a stop-word list or domain dictionary) containing a malicious DOCTYPE declaration can trigger local file disclosure via file:// entity references or server-side request forgery via http:// entity references during SAX parsing, before the application processes a single dictionary entry. This is inconsistent with the project's own XmlUtil.createSaxParser() helper, which correctly sets FEATURE_SECURE_PROCESSING and disallow-doctype-decl and is used by all other XML parsing paths in the codebase. The public Dictionary(InputStream) constructor delegates directly to this method and is the documented API for loading user-supplied dictionaries, making untrusted input a realistic scenario.

Mitigation: 2.x users should upgrade to 2.5.9. 3.x users should upgrade to 3.0.0-M3. Users who cannot upgrade immediately should ensure that all dictionary files are sourced from trusted origins and should consider wrapping the Dictionary(InputStream) constructor with input validation that rejects any XML containing a DOCTYPE declaration before it reaches the parser.

Severity

• CVSS Score: 9.1 / 10 (Critical)
• Vector String: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:N

References

• https://nvd.nist.gov/vuln/detail/CVE-2026-40682
• https://github.com/apache/opennlp
• https://lists.apache.org/thread/r6jpt0qr9nj67gqhppqg7jxf8vsbo0w6
• http://www.openwall.com/lists/oss-security/2026/05/01/19

This data is provided by OSV and the GitHub Advisory Database (CC-BY 4.0).

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Apache OpenNLP AbstractModelReader has an OOM Denial of Service via Unbounded Array Allocation

CVE-2026-42440 / GHSA-659w-93r5-9j6m

More information

Details

OOM Denial of Service via Unbounded Array Allocation in Apache OpenNLP AbstractModelReader 

Versions Affected: 

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

• CVSS Score: 7.5 / 10 (High)
• Vector String: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

References

• https://nvd.nist.gov/vuln/detail/CVE-2026-42440
• https://github.com/apache/opennlp
• https://lists.apache.org/thread/s8xlkx1gqbxfsq48py5h6jphjvgqp1jo
• http://www.openwall.com/lists/oss-security/2026/05/01/21

This data is provided by OSV and the GitHub Advisory Database (CC-BY 4.0).

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