GHSA-G4FV-7MF3-543R

Vulnerability from github – Published: 2026-07-06 18:31 – Updated: 2026-07-06 18:31
VLAI
Details

Untrusted Java Deserialization in Apache OpenNLP SvmDoccatModel

Versions Affected:   before 3.0.0-M4 (libsvm document categorization module; introduced in   OPENNLP-1808 and only present on the 3.x line)

Description: SvmDoccatModel.deserialize(InputStream) reads an attacker-controlled stream with java.io.ObjectInputStream and calls readObject() without an ObjectInputFilter installed. ObjectInputStream materialises every class referenced in the stream before the resulting object is cast to SvmDoccatModel, so the cast that follows readObject() executes only after the foreign object graph has already been deserialised in full.

If a Java deserialization gadget chain is available on the consumer's classpath, a crafted payload supplied to deserialize() executes arbitrary code in the JVM that loads it. Apache OpenNLP itself does not ship a known gadget chain, so the realistic risk is to downstream applications that embed the libsvm module alongside vulnerable transitive dependencies. The method is public and static, so any caller can pass an untrusted stream to it directly.

The practical impact is remote code execution against processes that load SvmDoccatModel instances from untrusted or semi-trusted origins.

Mitigation:

3.x users should upgrade to 3.0.0-M4.

Users who cannot upgrade immediately should treat all serialized SvmDoccatModel streams as untrusted input unless their provenance is verified, and should avoid invoking SvmDoccatModel.deserialize() on streams supplied by end users or fetched from third-party sources without integrity checks.

Show details on source website

{
  "affected": [],
  "aliases": [
    "CVE-2026-43825"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-502"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2026-07-06T17:16:31Z",
    "severity": null
  },
  "details": "Untrusted Java Deserialization in Apache OpenNLP SvmDoccatModel\n\nVersions Affected:\n\u00a0 before 3.0.0-M4 (libsvm document categorization module; introduced in\n\u00a0 OPENNLP-1808 and only present on the 3.x line)\n\nDescription:\nSvmDoccatModel.deserialize(InputStream) reads an attacker-controlled\nstream with java.io.ObjectInputStream and calls readObject() without an\nObjectInputFilter installed. ObjectInputStream materialises every class\nreferenced in the stream before the resulting object is cast to\nSvmDoccatModel, so the cast that follows readObject() executes only\nafter the foreign object graph has already been deserialised in full.\n\nIf a Java deserialization gadget chain is available on the consumer\u0027s\nclasspath, a crafted payload supplied to\ndeserialize() executes arbitrary code in the JVM that loads it. Apache\nOpenNLP itself does not ship a known gadget chain, so the realistic\nrisk is to downstream applications that embed the libsvm module\nalongside vulnerable transitive dependencies. The method is public and\nstatic, so any caller can pass an untrusted stream to it directly.\n\nThe practical impact is remote code execution against processes that\nload SvmDoccatModel instances from untrusted or semi-trusted origins.\n\nMitigation:\n\n3.x users should upgrade to 3.0.0-M4.\n\nUsers who cannot upgrade immediately should treat all serialized\nSvmDoccatModel streams as untrusted input unless their provenance is\nverified, and should avoid invoking SvmDoccatModel.deserialize() on\nstreams supplied by end users or fetched from third-party sources\nwithout integrity checks.",
  "id": "GHSA-g4fv-7mf3-543r",
  "modified": "2026-07-06T18:31:15Z",
  "published": "2026-07-06T18:31:15Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-43825"
    },
    {
      "type": "WEB",
      "url": "https://lists.apache.org/thread/c7kom0pgk9cbpfnbooh5m3g85ndf50hn"
    }
  ],
  "schema_version": "1.4.0",
  "severity": []
}


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Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.

Sightings

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Nomenclature

  • Seen: The vulnerability was mentioned, discussed, or observed by the user.
  • Confirmed: The vulnerability has been validated from an analyst's perspective.
  • Published Proof of Concept: A public proof of concept is available for this vulnerability.
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