{"answers":{"affected":"It affects 1 product.","exploited":"There is no known exploitation of CVE-2026-67211 in the wild and no public exploit code.","fix":"No fix has been published for CVE-2026-67211 yet. Limit exposure of the affected software until one is available.","what":"CVE-2026-67211 is a high-severity denial of service vulnerability in Apache OpenNLP, published on 11 Sep 2026. An attacker can exploit it over the internet without logging in, with no action from the victim."},"fix_prompt":"Check this codebase for CVE-2026-67211 and fix it if it is affected.\n\nWhat it is: CVE-2026-67211 is a high-severity denial of service vulnerability in Apache OpenNLP, published on 11 Sep 2026. An attacker can exploit it over the internet without logging in, with no action from the victim.\nExploitation: There is no known exploitation of CVE-2026-67211 in the wild and no public exploit code.\n\nAffected product: Apache OpenNLP. No package-level version data is available, so check the vendor advisory linked from the reference page.\n\nTask:\n1. Search the dependency manifests and lockfiles (for example package.json, package-lock.json, requirements.txt, poetry.lock, uv.lock, pom.xml, build.gradle, go.mod, Cargo.lock, Gemfile.lock) and any Dockerfiles for the affected packages, including transitive dependencies.\n2. If an affected version is in use, upgrade it to the fixed version for its release line with the project's package manager, so the lockfile is regenerated. Prefer the smallest upgrade that fixes it.\n3. If there is no fix, or the upgrade would break the build, do not guess: explain the risk and the safest workaround instead.\n4. Run the tests and summarise what changed and why.\n5. If nothing in the project is affected, say so and change nothing.\n\nReference: https://insights.sbomify.com/CVE-2026-67211/ (Markdown: https://insights.sbomify.com/CVE-2026-67211/index.md)","id":"CVE-2026-67211","markdown":"https://insights.sbomify.com/CVE-2026-67211/index.md","name":"CVE-2026-67211","record":{"aliases":["GHSA-gr8h-7qg5-vh27","UBUNTU-CVE-2026-67211"],"analysis":{"component_type":"library","fix_type":"upgrade","impacts":["denial_of_service"],"network_reachable":"yes","non_default_config":false,"product_category":"Java NLP library","requires_auth":"none","summary":"Attackers can cause denial of service in Apache OpenNLP by providing a crafted model file that triggers excessive memory allocation during deserialization.","user_interaction":"not_required","vuln_class":"denial_of_service"},"analysis_generated_by":"qwen3.8-flash-next","assigner":"apache","cpe":[{"product":"opennlp","vendor":"apache"}],"cvss":{"score":7.5,"severity":"high","source":"cisa-adp","vector":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H","version":"3.1"},"cwes":[{"id":"CWE-789","name":"Memory Allocation with Excessive Size Value"}],"description":"OOM Denial of Service via Unbounded Map Pre-Sizing in Apache OpenNLP SymSpellModelSerializer\n\nVersions Affected: \n\n- 3.0.0-M4\n- 3.0.0-M5\n\n(The opennlp-spellcheck extension was introduced in 3.0.0-M4. Releases 1.x and 2.x do not contain the affected code.)\n\nDescription:\n\nThe SymSpellModelSerializer.create() method reads two 32-bit signed integer count fields (unigramCount and bigramCount) from a binary SymSpell model stream and passes each value directly to LinkedHashMap.newLinkedHashMap() after validating only that it is non-negative. No upper bound is applied, so the count is fully attacker-controlled when the model file originates from an untrusted source.\n\nA crafted .bin model file in which either count field is set to Integer.MAX_VALUE (or any value large enough to exhaust the available heap) causes the map to be pre-sized to a capacity of 2^30 entries. The oversized backing array is allocated on the first put() into that map, requesting 4–8 GB depending on whether compressed oops are in effect, and the load fails with an OutOfMemoryError. Because the count fields sit immediately after a fixed-size header (magic, format version, three UTF strings, the configuration fields, and the edit-distance identifier) the attacker pays no meaningful size cost to weaponize a payload: a file of well under 100 bytes plus a single real entry is sufficient to crash a JVM that loads it.\n\nAny code path that deserializes a SymSpell model is affected, including SymSpellModels.deserialize(InputStream), SymSpellModels.fromBytes(byte[]), classpath model loading via SymSpellModelResolver.resolveByLanguage(String), the CorrectTextTool command-line tool, and model-archive loading through the registered ArtifactSerializer. The opennlp-spellcheck extension ships in the official OpenNLP binary distribution.\n\nThe practical impact is denial of service against processes that load SymSpell model files from untrusted or semi-trusted origins.\n\nMitigation:\n\n- 3.x users should upgrade to 3.0.0-M6.\n\nNote: The fix applies an upper bound to both count fields, checked before the map is pre-sized; counts that are negative or exceed the bound cause an IOException to be thrown and the read to fail fast with no large allocation. The bound is the existing AbstractModelReader.MAX_ENTRIES limit introduced earlie, which the current change promotes to public visibility so that serializers implementing their own binary format can share it. The default bound is 10,000,000, which is well above the entry counts of legitimate SymSpell dictionaries but far below any value that would threaten heap exhaustion. Deployments that legitimately need to load larger dictionaries 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. Note that this property is shared with the model-reader limit and raising it relaxes both.\n\nUsers who cannot upgrade immediately should treat all SymSpell .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.","ecosystems":[],"exploits":{"exploitdb":0,"first_seen":null,"github_poc":0,"max_poc_stars":null,"metasploit":0,"nuclei":0,"refs":[],"tier":"none_known"},"has_fix":false,"headline":"Apache OpenNLP: OOM DoS via Unbounded Array Allocation in SymSpellModelSerializer","id":"CVE-2026-67211","indexable":true,"kev":null,"kind":"cve","links":{"ecosystem":null,"product":null,"products":[],"type":"denial_of_service","year":"2026"},"n_packages":0,"n_references":1,"next":{"headline":"nanoid before 5.1.6 Infinite Loop via Zero Size in customAlphabet and customRandom","id":"CVE-2026-67213"},"packages":[],"prev":{"headline":"Juggle 1.6.0 Unauthenticated RCE via Exposed H2 Console","id":"CVE-2026-67208"},"products":["Apache OpenNLP"],"published":"2026-09-11","related":[{"cvss_score":10,"headline":"Apache OpenNLP, Apache OpenNLP: ReDoS / stack exhaustion in RegexNameFinderFactory built-in EMAIL and URL patterns","id":"CVE-2026-82617","kev_date_added":null,"published":"2026-09-11"},{"cvss_score":9.8,"headline":"Apache OpenNLP: Arbitrary Class Instantiation via Model Manifest in ExtensionLoader","id":"CVE-2026-42027","kev_date_added":null,"published":"2026-05-04"},{"cvss_score":9.8,"headline":"Improper Restriction of XML External Entity Reference in Apache OpenNLP","id":"CVE-2017-12620","kev_date_added":null,"published":"2017-10-02"},{"cvss_score":9.1,"headline":"Apache OpenNLP: XXE via Dictionary Parsing in DictionaryEntryPersistor","id":"CVE-2026-40682","kev_date_added":null,"published":"2026-05-04"},{"cvss_score":7.5,"headline":"Apache OpenNLP: OOM DoS via Unbounded Array Allocation in AbstractModelReader","id":"CVE-2026-42440","kev_date_added":null,"published":"2026-05-04"},{"cvss_score":7.3,"headline":"Apache OpenNLP :: Core :: ML :: LibSVM: Unsafe Java Deserialization in SvmDoccatModel","id":"CVE-2026-43825","kev_date_added":null,"published":"2026-07-06"}],"sources":[{"class":"attribution","licence":"CVE Terms of Use (MITRE)","name":"CVE List","prefix":"CVE"},{"class":"attribution","licence":"US public information, NVD API terms","name":"NVD"},{"class":"attribution","licence":"CWE Terms of Use","name":"CWE (MITRE)"},{"class":"attribution","licence":"FIRST CVSS licence","name":"CVSS (FIRST)"}],"ssvc":{"automatable":"yes","exploitation":"none","technical_impact":"partial"},"state":"PUBLISHED","updated":"2026-09-14","vendors":["Apache Software Foundation"]},"summary":"Attackers can cause denial of service in Apache OpenNLP by providing a crafted model file that triggers excessive memory allocation during deserialization.","url":"https://insights.sbomify.com/CVE-2026-67211/"}