Apache OpenNLP ExtensionLoader Arbitrary Class Instantiation (<=v2.5.9, v3.0.0-M3)
CVE-2026-42027 Published on May 4, 2026

Apache OpenNLP: Arbitrary Class Instantiation via Model Manifest in ExtensionLoader
Arbitrary Class Instantiation via Model Manifest in Apache OpenNLP ExtensionLoader Versions Affected: before 1.9.5, before 2.5.9, before 3.0.0-M3 Description:  The ExtensionLoader.instantiateExtension(Class, String) method loads a class by its fully-qualified name via Class.forName() and invokes its no-arg constructor, with the class name sourced from the manifest.properties entry of a model archive. The existing isAssignableFrom check correctly rejects classes that are not subtypes of the expected extension interface (BaseToolFactory for factory=, ArtifactSerializer for serializer-class-*), but the check runs after Class.forName() has already loaded and initialized the named class. Class.forName() with default initialization semantics executes the target class's static initializer before returning, so an attacker who can supply a crafted model archive can cause the static initializer of any class on the classpath to run during model loading, regardless of whether that class passes the subsequent type check. Exploitation requires a class with attacker-useful side effects in its static initializer (for example, JNDI lookup, outbound network I/O, or filesystem access) to be present on the classpath, so this is not a drop-in remote code execution; however, the attack surface grows as third-party model distribution becomes more common (community model repositories, Hugging Face-style sharing), where users routinely load model files from origins they do not control. A secondary, narrower vector affects deployments that ship legitimate BaseToolFactory or ArtifactSerializer subclasses with side-effecting no-arg constructors: a malicious manifest can name such a class and force its constructor to run during model load. Mitigation:  * 2.x users should upgrade to 2.5.9. * 3.x users should upgrade to 3.0.0-M3. Note: The fix introduces a package-prefix allowlist that is consulted before Class.forName() is invoked, so the static initializer of a disallowed class is never executed. Classes under the opennlp. prefix remain permitted by default. Deployments that load models referencing factories or serializers outside opennlp.* must opt those packages in, either programmatically via ExtensionLoader.registerAllowedPackage(String) before the first model load, or by setting the OPENNLP_EXT_ALLOWED_PACKAGES system property to a comma-separated list of allowed package prefixes. Users who cannot upgrade immediately should ensure that all model files are sourced from trusted origins and should audit their classpath for classes with side-effecting static initializers or constructors, particularly any that perform JNDI lookups, network requests, or filesystem operations during class initialization.

Vendor Advisory NVD

Vulnerability Analysis

CVE-2026-42027 can be exploited with network access, requires user interaction. This vulnerability is consided to have a high level of attack complexity. The potential impact of an exploit of this vulnerability is considered to be very high.

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

Weakness Types

What is a Reflection Injection Vulnerability?

The application uses external input with reflection to select which classes or code to use, but it does not sufficiently prevent the input from selecting improper classes or code. If the application uses external inputs to determine which class to instantiate or which method to invoke, then an attacker could supply values to select unexpected classes or methods. If this occurs, then the attacker could create control flow paths that were not intended by the developer. These paths could bypass authentication or access control checks, or otherwise cause the application to behave in an unexpected manner. This situation becomes a doomsday scenario if the attacker can upload files into a location that appears on the application's classpath (CWE-427) or add new entries to the application's classpath (CWE-426). Under either of these conditions, the attacker can use reflection to introduce new, malicious behavior into the application.

CVE-2026-42027 has been classified to as a Reflection Injection vulnerability or weakness.

What is a Marshaling, Unmarshaling Vulnerability?

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

CVE-2026-42027 has been classified to as a Marshaling, Unmarshaling vulnerability or weakness.


Products Associated with CVE-2026-42027

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Affected Versions

Apache Software Foundation Apache OpenNLP: Red Hat Fuse 7: Red Hat OpenShift AI (RHOAI): Red Hat build of Apache Camel for Spring Boot 4: Red Hat Data Grid 8: Red Hat JBoss Enterprise Application Platform 8: Red Hat JBoss Enterprise Application Platform Expansion Pack:

Exploit Probability

EPSS
0.69%
Percentile
48.05%

EPSS (Exploit Prediction Scoring System) scores estimate the probability that a vulnerability will be exploited in the wild within the next 30 days. The percentile shows you how this score compares to all other vulnerabilities.