Untrusted Java Deserialization in OpenNLP <3.0.0-M4 (SvmDoccatModel)
CVE-2026-43825 Published on July 6, 2026

Apache OpenNLP :: Core :: ML :: LibSVM: Unsafe Java Deserialization in SvmDoccatModel
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.

Vendor Advisory NVD

Vulnerability Analysis

CVE-2026-43825 can be exploited with network access, and does not require authorization privileges or user interaction. This vulnerability is considered to have a low attack complexity. The potential impact of an exploit of this vulnerability is considered to be low. considered to have a small impact on confidentiality and integrity and availability.

Attack Vector:
NETWORK
Attack Complexity:
LOW
Privileges Required:
NONE
User Interaction:
NONE
Scope:
UNCHANGED
Confidentiality Impact:
LOW
Integrity Impact:
LOW
Availability Impact:
LOW

Weakness Type

What is a Marshaling, Unmarshaling Vulnerability?

The application deserializes untrusted data without sufficiently verifying that the resulting data will be valid.

CVE-2026-43825 has been classified to as a Marshaling, Unmarshaling vulnerability or weakness.


Affected Versions

Apache Software Foundation Apache OpenNLP :: Core :: ML :: LibSVM: