Apache OpenNLP: Memory Allocation with Excessive Size Value
CVE-2026-42440 is a high-severity memory allocation with excessive size value vulnerability in Apache OpenNLP from Apache, with a CVSS 3.1 base score of 7.5 out of 10. It can be exploited remotely over the network by an unauthenticated attacker, without any action from a user. The record lists versions 2.5.9, 3.0.0-M3, 1.9.5 as the first without the flaw, so updating Apache OpenNLP to one of those releases or later removes the exposure. There is no confirmed exploitation so far. EPSS estimates a 0.6% chance of exploitation within 30 days, which is low compared with other CVEs.
High severity with no evidence of exploitation so far.
What to do
- Update Apache OpenNLP to 2.5.9 or 3.0.0-M3 or 1.9.5, whichever matches your release line, or a later version.
- Update Opennlp to 2.5.9 or a later version.
- Read the advisory on lists.apache.org for workarounds and exact fixed builds.
- Until you can update, restrict network access to the affected service so it is not reachable from untrusted networks.
Affected products and versions
| Vendor | Product | Affected versions | Fixed in |
|---|---|---|---|
| Apache | Apache OpenNLP | 2.0 up to, but not including, 2.5.9; 3.0.0-M1 up to, but not including, 3.0.0-M3; before 1.9.5 | 2.5.9, 3.0.0-M3, 1.9.5 |
| Red Hat | Red Hat Data Grid 8 | Not specified in the record | Not stated |
| Red Hat | Red Hat Fuse 7 | Not specified in the record | Not stated |
| Red Hat | Red Hat JBoss Enterprise Application Platform Expansion Pack | Not specified in the record | Not stated |
| Red Hat | Red Hat OpenShift AI (RHOAI) | Not specified in the record | Not stated |
| Apache | Opennlp | before 2.5.9; 3.0.0 | 2.5.9 |
Version data comes from the organisation that assigned the CVE. Confirm against the vendor advisory before deciding you are unaffected.
How the severity score breaks down
- Attack vector
- NetworkCan be exploited remotely over a network, including the internet.
- Attack complexity
- LowNo special conditions are needed; the attack works reliably.
- Privileges required
- NoneNo account or login is needed.
- User interaction
- NoneNo action by a victim is required.
- Scope
- UnchangedImpact stays within the vulnerable component.
- Confidentiality
- NoneNo data is disclosed.
- Integrity
- NoneNothing can be modified.
- Availability
- HighThe component can be taken down completely.
Vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H. Scored by CNA.
| Scoring system | Score | Severity | Scored by |
|---|---|---|---|
| CVSS 3.1 | 7.5 | High | CISA (ADP) |
| CVSS 3.1 | 7.5 | High | CNA |
Official 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: the CVE record as published by its numbering authority, via NVD and CVE.org.
The underlying weakness: Memory Allocation with Excessive Size Value
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.
How developers prevent this class of flaw
- 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.
- 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.
More Memory Allocation with Excessive Size Value vulnerabilities (CWE-789)
Timeline
- CVE record published.
- CISA published its SSVC assessment (exploitation: none, automatable: yes, technical impact: partial).
- Record last changed materially (score, affected products, status or references).
References
Advisories
Third-party and government advisories
- openwall.com/lists/oss-security/2026/05/01/21Mailing List, Third Party Advisory
Other references
Other vulnerabilities in Apache OpenNLP
- 9.8 Critical severity
- 7.5 High severity
- 7.5 High severity
- 7.5 High severity
Recent Memory Allocation with Excessive Size Value vulnerabilities
- 7.5 High severity
- 7.5 High severity
- 7.5 High severity
- 7.5 High severity
- 7.5 High severity
- 7.5 High severity
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