VulnerabilityPublished 4 May 2026Updated 9 Sep 2026

CVE-2026-42027: Apache OpenNLP · Unsafe Reflection

Affects Apache OpenNLPSource CVE Database V5

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Not known to be exploited · CVSS 9.8 Critical · fixed in 3.0.0-M3 : patch Apache OpenNLP as a priority.

Data as of 1 Nov 2026, 22:58 UTC

CVSS 3.1 base score

9.8Critical

How severe the flaw is on the 0–10 CVSS scale.

EPSS: 0.7% chance of exploitation in 30 days – higher than 53% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch: Available – fixed in 3.0.0-M3, 2.5.9, 1.9.5

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

Attack Vector: Network
Attack Complexity: Low
Privileges Required: None
User Interaction: None
Scope: Unchanged
Confidentiality: High
Integrity: High
Availability: High

Bold values are the worst case: easiest to exploit or largest impact.

Attack Vector: Network: The vulnerable component is bound to the network stack and the set of possible attackers extends beyond the other options listed, up to and including the entire Internet. Such a vulnerability is often termed 'remotely exploitable' and can be thought of as an attack being exploitable at the protocol level one or more network hops away (e.g., across one or more routers).
Attack Complexity: Low: Specialized access conditions or extenuating circumstances do not exist. An attacker can expect repeatable success against the vulnerable component.
Privileges Required: None: The attacker is unauthorized prior to attack, and therefore does not require any access to settings or files to carry out an attack.
User Interaction: None: The vulnerable system can be exploited without any interaction from any user.
Scope: Unchanged: An exploited vulnerability can only affect resources managed by the same security authority. In this case, the vulnerable component and the impacted component are either the same, or both are managed by the same security authority.
Confidentiality: High: There is total loss of confidentiality, resulting in all resources within the impacted component being divulged to the attacker. Alternatively, access to only some restricted information is obtained, but the disclosed information presents a direct, serious impact.
Integrity: High: There is a total loss of integrity, or a complete loss of protection. For example, the attacker is able to modify any/all files protected by the impacted component. Alternatively, only some files can be modified, but malicious modification would present a direct, serious consequence to the impacted component.
Availability: High: There is total loss of availability, resulting in the attacker being able to fully deny access to resources in the impacted component; this loss is either sustained (while the attacker continues to deliver the attack) or persistent (the condition persists even after the attack has completed). Alternatively, the attacker has the ability to deny some availability, but the loss of availability presents a direct, serious consequence to the impacted component (e.g., the attacker cannot disrupt existing connections, but can prevent new connections; the attacker can repeatedly exploit a vulnerability that, in each instance of a successful attack, leaks a only small amount of memory, but after repeated exploitation causes a service to become completely unavailable).

Definitions: FIRST CVSS v3.1 specification.

Exploitation evidence

Disclosed: evidence found.
CVE published 4 May 2026
Detectable: no evidence on record.
No public nuclei template indexed
Weaponized: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by Mirage: no evidence on record.
No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 1 Nov 2026.

Disclosed: The CVE record is public on cve.org. Its publication is t = 0.
Detectable: A public nuclei template can find exposed instances, for defenders and attackers alike.
Weaponized: A Metasploit exploit module or Exploit-DB entry exists (Metasploit scanner modules do not count), or CISA SSVC or the CVSS exploit maturity reports a public proof of concept.
Exploited in the wild: CISA KEV lists it, CISA SSVC rates exploitation active, or a source reports attacks.
Observed by OffSeq Mirage: OffSeq Mirage honeypot sensors recorded attack traffic for this CVE, or found exposed hosts without attack traffic.

Moments are spaced evenly, not to scale; a dashed line marks a gap of six months or more. Deltas count from t = 0. Sources that give only a date (CISA KEV, Exploit-DB, or a time of exactly 00:00Z) read “same day” rather than hours. Metasploit modules carry the flaw’s disclosure date, not their release date, and nuclei templates carry no date, so both appear above but not on the timeline.

What to do

Upgrade Apache OpenNLP and Apache Software Foundation/org.apache.opennlp:opennlp-tools to 3.0.0-M3, 2.5.9 or 1.9.5; org.apache.opennlp/opennlp to 2.5.9.

Affected software

Product	Affected	Fixed in
Apache Software FoundationApache OpenNLP	Affected >=3.0.0-M1 <3.0.0-M3	Fixed in 3.0.0-M3
Apache Software FoundationApache OpenNLP	Affected >=2.0 <2.5.9	Fixed in 2.5.9
Apache Software FoundationApache OpenNLP	Affected <1.9.5	Fixed in 1.9.5
Mavenorg.apache.opennlp/opennlp`pkg:maven/org.apache.opennlp/opennlp`Other threats	Affected <2.5.9 =3.0.0-M3	Fixed in 2.5.9
MavenApache Software Foundation/org.apache.opennlp:opennlp-tools`pkg:maven/Apache Software Foundation/org.apache.opennlp:opennlp-tools`Other threats	Affected >=2.0 <2.5.9 >=3.0.0-M1 <3.0.0-M3 <1.9.5	Fixed in 2.5.9 3.0.0-M3 1.9.5

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AI-suggested

A fix is available in Apache OpenNLP versions 2.5.9 and 3.0.0-M3, which implement a package-prefix allowlist to prevent execution of static initializers for disallowed classes. Users should upgrade to these fixed versions. For users who cannot upgrade immediately, it is recommended to only load model files from trusted sources and audit the classpath for classes with side-effecting static initializers or constructors, especially those performing JNDI lookups, network I/O, or filesystem access. Deployments that require loading classes outside the opennlp.* package must explicitly register allowed packages via ExtensionLoader.registerAllowedPackage(String) or the OPENNLP_EXT_ALLOWED_PACKAGES system property before model loading.

Description

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.

Impact

An attacker supplying a malicious model archive can cause the static initializer of any class on the classpath to execute during model loading, potentially triggering side effects like network requests or filesystem operations. This can lead to confidentiality, integrity, and availability impacts depending on the classes present. The vulnerability is rated critical with a CVSS score of 9.8, reflecting high impact on confidentiality, integrity, and availability. However, exploitation requires the presence of classes with side-effecting static initializers or constructors, so it is not a straightforward remote code execution.

Summary

The vulnerability in Apache OpenNLP's ExtensionLoader.instantiateExtension(Class, String) method arises because it loads classes by name using Class.forName() before verifying that the loaded class is a subtype of the expected interface. Since Class.forName() initializes the class, its static initializer runs before the type check, allowing an attacker who controls the model manifest to trigger static initializers of arbitrary classes on the classpath. This can lead to unintended side effects such as network communication or filesystem access if such classes exist. The vulnerability is not direct remote code execution but increases attack surface especially when loading models from untrusted sources. The fix in versions 2.5.9 and 3.0.0-M3 adds a package-prefix allowlist to block unauthorized class loading. Users must explicitly allow packages outside the default opennlp.* prefix if needed.

CWE-470Use of Externally-Controlled Input to Select Classes or Code (Reflection Injection) (opens in a new tab)
Base weaknessOWASP Top 10 A05:2025: Injection (opens in a new tab)
The product 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.
Architecture and Design
Refactor your code to avoid using reflection.
Architecture and Design
Do not use user-controlled inputs to select and load classes or code.
Implementation
Apply strict input validation by using allowlists or indirect selection to ensure that the user is only selecting allowable classes or code.
OWASP Cheat Sheets
XML Security (opens in a new tab)
Mass Assignment (opens in a new tab)
Injection Prevention (opens in a new tab)
SQL Injection Prevention (opens in a new tab)
Injection Prevention in Java (opens in a new tab)
OWASP ASVS 4.0.3
V13.3.1 (opens in a new tab)
V5.1.4 (opens in a new tab)
V5.1.3 (opens in a new tab)
Attack patterns
Reflection Injection (opens in a new tab)
From MITRE CWE, MITRE CAPEC and OWASP (Top 10, OpenCRE).
CWE-502Deserialization of Untrusted Data (opens in a new tab)
Base weaknessLikelihood of exploit: MediumCWE Top 25 (2025): #15OWASP Top 10 A08:2025: Software or Data Integrity Failures (opens in a new tab)
The product deserializes untrusted data without sufficiently ensuring that the resulting data will be valid.
Architecture and Design · Implementation
If available, use the signing/sealing features of the programming language to assure that deserialized data has not been tainted. For example, a hash-based message authentication code (HMAC) could be used to ensure that data has not been modified.
Implementation
When deserializing data, populate a new object rather than just deserializing. The result is that the data flows through safe input validation and that the functions are safe.
Implementation
Explicitly define a final object() to prevent deserialization.
OWASP Cheat Sheets
Abuse Case (opens in a new tab)
Deserialization (opens in a new tab)
XML Security (opens in a new tab)
XML External Entity Prevention (opens in a new tab)
Software Supply Chain Security (opens in a new tab)
OWASP ASVS 4.0.3
V1.5.2 (opens in a new tab)
V5.5.3 (opens in a new tab)
V5.5.1 (opens in a new tab)
Attack patterns
Object Injection (opens in a new tab)
From MITRE CWE, MITRE CAPEC and OWASP (Top 10, OpenCRE).

References

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Exploitation evidence

What to do

Affected software

Description

AI analysisMachine-generated, updated 15 Aug 2026, 17:17 UTC

WeaknessesCWE-470, CWE-502

Technical details

References

Community reviews

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