CVE-2026-67211VulnerabilityPublished 11 Sep 2026Updated 4 Nov 2026

OOM Denial of Service via Unbounded Map Pre-Sizing in Apache OpenNLP SymSpellModelSerializer Versions Affected: -

Affects apache-opennlpSource GCVE Database

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Authoritative record:CVE-2026-67211· High 7.5

Not known to be exploited · CVSS 7.5 High · fixed in 3.0.0 : patch apache-opennlp soon.

Data as of 5 Nov 2026, 00:01 UTC

CVSS 3.1 base score

7.5High

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

EPSS: 0.7% chance of exploitation in 30 days – higher than 54% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch: Available – fixed in 3.0.0

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 11 Sep 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 5 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

Affected software

Product	Affected	Fixed in
Ubuntu 26.10apache-opennlp`pkg:deb/ubuntu/apache-opennlp?arch=source&distro=stonking`Other threats	Affected =2.5.3-1build1 =2.5.9-1	Fixed in–

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

AI-suggested

Users of Apache OpenNLP 3.x should upgrade to version 3.0.0-M6 or later, where the vulnerability is fixed by enforcing an upper bound on count fields during deserialization. The default upper bound is 10,000,000 entries, which can be increased by setting the OPENNLP_MAX_ENTRIES JVM system property if needed. Until upgrade, treat all SymSpell .bin model files as untrusted input unless their provenance is verified. Avoid loading models supplied by end users or from third-party repositories without integrity checks to prevent denial of service.

Description

Apache OpenNLP versions 3.0.0-M4 and 3.0.0-M5 contain a denial of service vulnerability in the SymSpellModelSerializer component. The vulnerability arises from unbounded pre-sizing of a LinkedHashMap based on attacker-controlled count fields in a binary SymSpell model file. This can cause excessive memory allocation leading to an OutOfMemoryError and process crash when loading crafted model files from untrusted sources. The issue affects any code path that deserializes SymSpell models, including command-line tools and model loading mechanisms. The vulnerability is fixed in version 3.0.0-M6 by applying an upper bound to the count fields. Users unable to upgrade should treat SymSpell model files as untrusted and avoid loading unverified models.

Impact

An attacker can cause denial of service by supplying a crafted SymSpell model file with large count fields, leading to excessive memory allocation and JVM crash due to OutOfMemoryError. This impacts any process that loads SymSpell models from untrusted or semi-trusted sources, potentially disrupting services or tools that rely on OpenNLP's spellcheck functionality. There is no impact on confidentiality or integrity, only availability.

References

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