What is the CVSS score of CVE-2026-43825?

CVE-2026-43825 has a CVSS 3.1 base score of 7.3 (High). CVSS vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:L. EPSS exploitation probability: 4.8%.

Which versions of Apache OpenNLP are affected by CVE-2026-43825?

Apache OpenNLP versions 3.0.0-M1 through M4 contain an unsafe Java deserialization flaw in the SvmDoccatModel module that can enable remote code execution when processing untrusted serialized input,…. A patch is available.

How to fix or mitigate CVE-2026-43825?

Within 24 hours: Identify all internal services and applications using Apache OpenNLP; document current versions via dependency scanning (Maven, Gradle, or SCA tools). Within 7 days: Upgrade to OpenNLP 3.0.0-M4 or later in staging environments, validate functionality, and begin staged rollout to production starting with non-critical systems. Within 30 days: Complete production deployment of patched versions across all affected applications; audit transitive dependencies for gadget chain libraries (e.g., commons-beanutils, ROME, Spring Framework); document any systems unable to patch and implement interim controls.

Apache OpenNLP CVE-2026-43825

EUVD-2026-41885 HIGH

Deserialization of Untrusted Data (CWE-502)

2026-07-06 apache

GHSA-g4fv-7mf3-543r

Deserialization Java RCE Apache Apache Opennlp Red Hat Suse

7.3

CVSS 3.1 · Vendor: apache

Severity by source

Vendor (apache) PRIMARY

7.3 MEDIUM

AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:L

vuln.today AI

8.1 HIGH

Public static entry point gives AV:N/PR:N/UI:N; full RCE impact (C/I/A:H) when exploitable, but dependence on a classpath gadget outside attacker control justifies AC:H.

3.1 AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H

4.0 AV:N/AC:L/AT:P/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

SUSE

HIGH

qualitative

Red Hat

7.3 MEDIUM

qualitative

Primary rating from Vendor (apache).

CVSS VectorVendor: apache

CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:L

Attack Vector

Network

Attack Complexity

Low

Privileges Required

None

User Interaction

None

Scope

Unchanged

Confidentiality

Low

Integrity

Low

Availability

Low

Lifecycle Timeline

Analysis Updated

Jul 06, 2026 - 21:23 vuln.today

v3 (cvss_changed)

Analysis Updated

Jul 06, 2026 - 21:22 vuln.today

v2 (cvss_changed)

Re-analysis Queued

Jul 06, 2026 - 20:22 vuln.today

cvss_changed

CVSS changed

Jul 06, 2026 - 20:22 NVD

7.3 (HIGH)

Patch available

Jul 06, 2026 - 18:02 EUVD

Analysis Generated

Jul 06, 2026 - 17:01 vuln.today

DescriptionCVE.org

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.

Articles & Coverage 1

News 6 New Java Vulnerabilities - 6 High Severity, No POC Available Jul 07, 2026

AnalysisAI

Untrusted Java deserialization in Apache OpenNLP's SvmDoccatModel (libsvm document categorization module, versions 3.0.0-M1 through before 3.0.0-M4) lets an attacker who supplies a crafted serialized stream to the public static SvmDoccatModel.deserialize(InputStream) trigger deserialization of an arbitrary object graph before the SvmDoccatModel cast occurs. Where a usable gadget chain exists on the consuming application's classpath, this yields remote code execution in the loading JVM; OpenNLP ships no gadget itself, so realistic risk falls on downstream apps that embed the module alongside vulnerable transitive dependencies. No public exploit identified at time of analysis and the flaw is not in CISA KEV, though the SSVC assessment marks it automatable with partial technical impact.

Technical ContextAI

The root cause is CWE-502 (Deserialization of Untrusted Data). SvmDoccatModel.deserialize() wraps an attacker-controlled stream in java.io.ObjectInputStream and invokes readObject() with no ObjectInputFilter installed. Because ObjectInputStream materializes every class referenced in the stream during readObject() - before the returned object is cast to SvmDoccatModel - the type check that would reject a foreign object happens too late to prevent instantiation of arbitrary classes. Java deserialization RCE is not self-contained: it requires a 'gadget chain' (classes whose readObject/side-effect logic can be chained to execute code, e.g. from Commons-Collections, Spring, or similar libraries). The affected component is the optional libsvm-backed document categorization module (CPE: apache_software_foundation apache_opennlp core ml libsvm), introduced under OPENNLP-1808 and present only on the OpenNLP 3.x line.

RemediationAI

Vendor-released patch: 3.0.0-M4 - 3.x users of the libsvm module should upgrade to 3.0.0-M4 as the primary fix, per the Apache advisory at https://lists.apache.org/thread/c7kom0pgk9cbpfnbooh5m3g85ndf50hn (see also https://seclists.org/oss-sec/2026/q3/66). For those who cannot upgrade immediately, treat all serialized SvmDoccatModel streams as untrusted and only deserialize them when provenance is verified through integrity checks such as signatures or checksums on the model artifact; concretely, avoid calling SvmDoccatModel.deserialize() on any stream supplied by end users or fetched from third-party sources. Additional compensating controls include installing a strict JVM-wide or per-stream ObjectInputFilter (jdk.serialFilter) that allow-lists only the expected OpenNLP/libsvm classes - this blocks foreign gadget classes at deserialization time but must be tuned carefully or it will reject legitimate model loads - and auditing/removing known-vulnerable gadget libraries (e.g. outdated Commons-Collections) from the classpath, which reduces exploitability but may affect components that depend on them. Restrict model-loading code paths so only trusted internal artifact stores can supply serialized models.