VulnerabilityPublished 4 May 2026Updated 30 Jul 2026

CVE-2026-42440: Apache OpenNLP · Excessive Memory Allocation

Affects Apache OpenNLPSource CVE Database V5

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Not known to be exploited · CVSS 7.5 High · fixed in 3.0.0-M3 : patch Apache OpenNLP soon.

Data as of 31 Oct 2026, 20:20 UTC

CVSS 3.1 base score

7.5High

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

EPSS: 0.6% chance of exploitation in 30 days – higher than 48% 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:N/I:N/A:H

Attack Vector: Network
Attack Complexity: Low
Privileges Required: None
User Interaction: None
Scope: Unchanged
Confidentiality: None
Integrity: None
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: None: There is no loss of confidentiality within the impacted component.
Integrity: None: There is no loss of integrity within 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 31 Oct 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

Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.

AI-suggested

Fixed versions are available: users of the 2.x branch should upgrade to Apache OpenNLP 2.5.9 or later, and users of the 3.x branch should upgrade to 3.0.0-M3 or later. The fix enforces upper bounds on array allocation sizes to prevent excessive memory use. For users unable to upgrade immediately, it is recommended to treat all .bin model files as untrusted input unless their provenance is verified and to avoid loading models from untrusted or third-party sources without integrity checks. No vendor advisory indicates that no action is required or that the issue is already mitigated without upgrade.

Description

OOM Denial of Service via Unbounded Array Allocation in Apache OpenNLP AbstractModelReader Versions Affected: before 1.9.5 before 2.5.9 before 3.0.0-M3 Description: The AbstractModelReader methods getOutcomes(), getOutcomePatterns(), and getPredicates() each read a 32-bit signed integer count field from a binary model stream and pass that value directly to an array allocation (new String[numOutcomes], new int[numOCTypes][], new String[NUM_PREDS]) without validating that the value is non-negative or within a reasonable bound. The count is therefore fully attacker-controlled when the model file originates from an untrusted source. A crafted .bin model file in which any of these count fields is set to Integer.MAX_VALUE (or any value large enough to exhaust the available heap) triggers an OutOfMemoryError at the array allocation itself, before the corresponding label or pattern data is consumed from the stream. The error occurs very early in deserialization: for a GIS model, getOutcomes() is reached after only the model-type string, the correction constant, and the correction parameter have been read; so the attacker pays no meaningful size cost to weaponize a payload, and a single small file can crash a JVM that loads it. Any code path that deserializes a .bin model is affected, including direct use of GenericModelReader and any higher-level component that delegates to it during model load. The practical impact is denial of service against processes that load model files from untrusted or semi-trusted origins. Mitigation: * 2.x users should upgrade to 2.5.9. * 3.x users should upgrade to 3.0.0-M3. Note: The fix introduces an upper bound on each of the three count fields, checked before array allocation; counts that are negative or exceed the bound cause an IllegalArgumentException to be thrown and the read to fail fast with no large allocation. The default bound is 10,000,000, which is well above the entry counts of legitimate OpenNLP models but far below any value that would threaten heap exhaustion. Deployments that legitimately need to load models with more entries than the default can raise the limit at JVM startup by setting the OPENNLP_MAX_ENTRIES system property to the desired positive integer (e.g. -DOPENNLP_MAX_ENTRIES=50000000); invalid or non-positive values fall back to the default. Users who cannot upgrade immediately should treat all .bin model files as untrusted input unless their provenance is verified, and should avoid loading models supplied by end users or fetched from third-party repositories without integrity checks.

Impact

This vulnerability enables denial of service by causing the Java Virtual Machine to exhaust heap memory during model deserialization, crashing processes that load untrusted or semi-trusted .bin model files. There is no impact on confidentiality or integrity reported. The denial of service can be triggered with a small crafted model file, making it a practical threat to availability of applications using affected OpenNLP versions.

CWE-789Memory Allocation with Excessive Size Value (opens in a new tab)
Variant weakness
The product allocates memory based on an untrusted, large size value, but it does not ensure that the size is within expected limits, allowing arbitrary amounts of memory to be allocated.
Implementation · Architecture and Design
Perform adequate input validation against any value that influences the amount of memory that is allocated. Define an appropriate strategy for handling requests that exceed the limit, and consider supporting a configuration option so that the administrator can extend the amount of memory to be used if necessary.
Operation
Run your program using system-provided resource limits for memory. This might still cause the program to crash or exit, but the impact to the rest of the system will be minimized.
OWASP Cheat Sheets
Abuse Case (opens in a new tab)
OWASP ASVS 4.0.3
V8.1.4 (opens in a new tab)
V13.4.1 (opens in a new tab)
V11.1.4 (opens in a new tab)
From MITRE CWE, MITRE CAPEC and OWASP (Top 10, OpenCRE).
CWE-770Allocation of Resources Without Limits or Throttling (opens in a new tab)
Base weaknessLikelihood of exploit: HighCWE Top 25 (2025): #25
The product allocates a reusable resource or group of resources on behalf of an actor without imposing any intended restrictions on the size or number of resources that can be allocated.
Requirements
Clearly specify the minimum and maximum expectations for capabilities, and dictate which behaviors are acceptable when resource allocation reaches limits.
Architecture and Design
Limit the amount of resources that are accessible to unprivileged users. Set per-user limits for resources. Allow the system administrator to define these limits. Be careful to avoid CWE-410.
Architecture and Design
Design throttling mechanisms into the system architecture. The best protection is to limit the amount of resources that an unauthorized user can cause to be expended.
OWASP Cheat Sheets
Abuse Case (opens in a new tab)
OWASP ASVS 4.0.3
V8.1.4 (opens in a new tab)
V13.4.1 (opens in a new tab)
V11.1.4 (opens in a new tab)
Attack patterns
XML Ping of the Death (opens in a new tab)
Exponential Data Expansion (opens in a new tab)
Serialized Data Parameter Blowup (opens in a new tab)
Serialized Data with Nested Payloads (opens in a new tab)
Oversized Serialized Data Payloads (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 30 Jul 2026, 21:40 UTC

WeaknessesCWE-789, CWE-770

Technical details

References

Community reviews

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