HighCVE-2026-42440

    Apache OpenNLP: OOM DoS via Unbounded Array Allocation in AbstractModelReader (CVE-2026-42440)

    Published Last modified Sources: CVE.org, OSV, EUVD

    What is affected, what to do

    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.

    Update Apache OpenNLP to version 2.5.9, Apache OpenNLP to version 3.0.0-M3, Apache OpenNLP to version 1.9.5.

    Affected
    • Apache OpenNLP lt 2.0..2.5.9
    • Apache OpenNLP lt 3.0.0-M1..3.0.0-M3
    • Apache OpenNLP lt *..1.9.5
    Fixed in
    • Apache OpenNLP 2.5.9
    • Apache OpenNLP 3.0.0-M3
    • Apache OpenNLP 1.9.5

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    Metrics

    7.5

    Priority

    XIEM Effective CVSS: highest available CVSS rating from NVD and vendor data.

    Source: NVD, CVSS v3

    7.5

    CVSS v3

    Severity High on the scale from 0 to 10.

    Source: CVE.org, OSV, EUVD

    0.6 %

    EPSS score

    Low — model estimates < 1% exploitation likelihood.

    Source: FIRST EPSS, percentile 48.8

    Show all metrics
    Severity
    High
    Priority (0–325)

    The priority score is a consolidated metric that condenses several individual signals into a single, action-guiding ranking: technical severity (CVSS), exploitation likelihood (EPSS), known-exploited status (KEV) and the context of the affected asset. The aim is to answer the only truly important question in vulnerability management: what first?

    Read in glossary
    73.52 Methodology
    Exploit maturity

    An exploit is code or a technique that specifically uses a concrete vulnerability to force unintended behaviour — such as executing code or escalating privileges. It is the tool that turns a theoretical flaw into a practical attack.

    Read in glossary
    no public PoC known
    XIEM Effective CVSS

    XIEM® is the tiered managed-security operating model from NEOSEC and a registered trademark of NEOSEC GmbH. It extends classic SIEM — the central collection, normalisation and correlation of security-relevant events — with endpoint forensics, network visibility and deception technology, and consolidates them into three building tiers: Sentry, Orchestrate and Command.

    Read in glossary
    7.5 Methodology
    CVSS v3

    CVSS is the globally established, open standard for assessing the technical severity of a vulnerability on a scale from 0 to 10. It describes how easily a flaw can be exploited and what damage it can cause — but not how likely its actual exploitation is. CVSS and the complementary EPSS deliberately answer different questions.

    Read in glossary
    7.5
    EPSS score

    The EPSS score is the absolute model output for an individual vulnerability: the estimated probability of real-world exploitation within the next 30 days, expressed as a value between 0 and 1 (i.e. 0 % to 100 %). A score of 0.42 means „the model estimates a 42 % chance of real-world exploitation within 30 days“.

    Read in glossary
    0.6 %
    Low — model estimates < 1% exploitation likelihood.
    EPSS percentile

    The EPSS percentile is the relative position of a vulnerability within all EPSS-scored CVEs, expressed as a value between 0 and 1. A percentile of 0.90 means „this CVE has a higher EPSS score than 90 % of all other scored CVEs“. Unlike the EPSS score, it is not a probability — but a positioning within a ranking.

    Read in glossary
    48.8
    High — this CVE ranks above the median of all CVEs scored today (rank ≥ 36%).
    KEV status

    The KEV catalogue is a list maintained by the US authority CISA of vulnerabilities for which active exploitation in the wild has been confirmed. It answers not the question „how severe?“ but „is it already being attacked?“ — thereby sharply prioritising patching.

    Read in glossary
    —
    Published
    CISA assessment (SSVC)
    Exploitation: none known · automatable · Technical impact: partial
    Source: CISA Vulnrichment, as of 5 May 2026
    CWE

    CWE is a catalogue of typical weakness types in software and hardware — such as flawed input validation, unsafe memory handling or poor authentication. Where a CVE names a concrete vulnerability in a concrete product, a CWE describes the underlying class of defect.

    Read in glossary
    CWE-789, CWE-770

    Detection rules

    No NEOSEC detection rule is available for this vulnerability yet.

    Classification under NIS2 and CRA

    Rule-based classification from exploitation status and severity. It does not replace a case-by-case assessment.

    NIS2

    NIS2 is EU Directive (EU) 2022/2555 to raise the level of cybersecurity across the Union. It substantially broadens obligations compared with the first NIS directive: more sectors, mandatory risk management, staged reporting duties and personal accountability of senior management.

    Read in glossary
    High technical severity: assess and remediate the vulnerability in vulnerability handling under Art. 21(2)(e) NIS2 and document the decision.
    CRA

    The CRA is EU Regulation (EU) 2024/2847 setting horizontal cybersecurity requirements for „products with digital elements“ — from industrial controllers and software to connected consumer devices. For the first time manufacturers must ensure security across the entire product lifecycle, handle vulnerabilities and provide security updates.

    Read in glossary
    From 11.12.2027, Annex I Part II CRA requires manufacturers of affected products with digital elements to handle vulnerabilities without delay and provide security updates.
    ISO/IEC 27001

    ISO/IEC 27001 is the internationally leading standard for an information security management system (ISMS). It describes a risk-based, process-oriented framework to systematically plan, implement, review and improve information security.

    Read in glossary
    Control A.8.8, management of technical vulnerabilities: assess relevance to your systems, plan remediation and keep evidence.

    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.

    Source: NVD (NIST), cvelistv5, CVE.org (MITRE), OSV (Open Source Vulnerabilities), ENISA EUVD

    Affected products

    The following versions are affected. Older releases of the same product line are also vulnerable unless stated otherwise.

    Affected products
    Affected versionsFixed in
    Apache Software FoundationApache OpenNLP
    • 2.0 – < 2.5.9
    • 3.0.0-M1 – < 3.0.0-M3
    • < 1.9.5
    • 2.5.9
    • 3.0.0-M3
    • 1.9.5
    apacheopennlp
    • 3.0.0
    • < 2.5.9
    • —
    • 2.5.9
    mavenorg.apache.opennlp:opennlp-tools
    • 1.8.4
    • 2.5.1
    • 1.8.1
    • 2.3.1
    • 2.5.5
    • 1.6.0
    • 2.5.6
    • 1.7.1
    • 1.9.2
    • 1.9.4
    • 1.9.0
    • 2.5.8
    • 2.3.0
    • 2.5.2
    • 1.5.2-incubating
    • 2.2.0
    • 1.5.1-incubating
    • 2.0.0
    • 1.5.3
    • 1.7.2
    • 3.0.0-M1
    • 2.3.3
    • 1.7.0
    • 2.5.7
    • 1.8.2
    • 2.5.6.1
    • 2.5.4
    • 1.9.1
    • 1.8.3
    • 2.5.0
    • 1.8.0
    • 2.1.1
    • 3.0.0-M2
    • 1.9.3
    • 2.4.0
    • 2.3.2
    • 2.5.3
    • 1.9.5
    • 2.1.0
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    Vendors and products

    Weakness classes (CWE)

    • CWE-789Memory Allocation with Excessive Size Value

      Variant

      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.

      Entry at MITRE CWE-789

    • CWE-770Allocation of Resources Without Limits or Throttling

      Base

      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.

      Entry at MITRE CWE-770

    References & sources

    Changes to this CVE

    Who changed this CVE and when: NVD (analysis, affected products, CVSS), the CVE publisher (CNA) and additional providers such as CISA (ADP).

    1. Record modifiedRed Hat ADP

      1 individual change: Affected versions (publisher)

      Show the original change
      Individual changes of this event as published by NVD
      TypeActionBeforeAfter
      Affected versions (publisher)changed
      Red Hat Data Grid 8, Red Hat Fuse 7, Red Hat Fuse 7 (+4)
      Red Hat Data Grid 8, Red Hat Fuse 7, Red Hat Fuse 7 (+4)

      Source identifier: 0b0ca135-0b70-47e7-9f44-1890c2a1c46c

    2. Record modifiedRed Hat ADP

      1 individual change: Affected versions (publisher)

      Show the original change
      Individual changes of this event as published by NVD
      TypeActionBeforeAfter
      Affected versions (publisher)changed
      Red Hat Data Grid 8, Red Hat Fuse 7, Red Hat JBoss Enterprise Application Platform Expansion Pack (+1)
      Red Hat Fuse 7, Red Hat JBoss Enterprise Application Platform Expansion Pack, Red Hat Data Grid 8 (+1)

      Source identifier: 0b0ca135-0b70-47e7-9f44-1890c2a1c46c

    One NVD translation into another language is hidden.

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