Apache OpenNLP :: Core :: ML :: LibSVM: Deserialization of Untrusted Data
CVE-2026-43825 is a high-severity deserialization of untrusted data vulnerability in Apache OpenNLP :: Core :: ML :: LibSVM from Apache, with a CVSS 3.1 base score of 7.3 out of 10. It can be exploited remotely over the network by an unauthenticated attacker, without any action from a user. The record lists version 3.0.0-M4 as the first without the flaw, so updating Apache OpenNLP :: Core :: ML :: LibSVM to that release or later removes the exposure. There is no confirmed exploitation so far. EPSS estimates a 13.9% chance of exploitation within 30 days, which is in the top 3.4% of all scored CVEs.
EPSS places it among the vulnerabilities most likely to be exploited soon.
What to do
- Update Apache OpenNLP :: Core :: ML :: LibSVM to 3.0.0-M4 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 :: Core :: ML :: LibSVM | 3.0.0-M1 up to, but not including, 3.0.0-M4 | 3.0.0-M4 |
| Apache | Opennlp | 3.0.0 | Not stated |
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
- LowSome restricted data can be read.
- Integrity
- LowLimited modification of data is possible.
- Availability
- LowPerformance or availability is reduced.
Vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:L. Scored by CISA (ADP).
Official description
Untrusted Java Deserialization in Apache OpenNLP SvmDoccatModel Versions Affected: before 3.0.0-M4 (libsvm document categorization module; introduced in OPENNLP-1808 and only present on the 3.x line) Description: SvmDoccatModel.deserialize(InputStream) reads an attacker-controlled stream with java.io.ObjectInputStream and calls readObject() without an ObjectInputFilter installed. ObjectInputStream materialises every class referenced in the stream before the resulting object is cast to SvmDoccatModel, so the cast that follows readObject() executes only after the foreign object graph has already been deserialised in full. If a Java deserialization gadget chain is available on the consumer's classpath, a crafted payload supplied to deserialize() executes arbitrary code in the JVM that loads it. Apache OpenNLP itself does not ship a known gadget chain, so the realistic risk is to downstream applications that embed the libsvm module alongside vulnerable transitive dependencies. The method is public and static, so any caller can pass an untrusted stream to it directly. The practical impact is remote code execution against processes that load SvmDoccatModel instances from untrusted or semi-trusted origins. Mitigation: 3.x users should upgrade to 3.0.0-M4. Users who cannot upgrade immediately should treat all serialized SvmDoccatModel streams as untrusted input unless their provenance is verified, and should avoid invoking SvmDoccatModel.deserialize() on streams supplied by end users or fetched from third-party sources without integrity checks.
Source: the CVE record as published by its numbering authority, via NVD and CVE.org.
The underlying weakness: Deserialization of Untrusted Data
The product deserializes untrusted data without sufficiently ensuring that the resulting data will be valid.
How developers prevent this class of flaw
- If available, use the signing/sealing features of the programming language to assure that deserialized data has not been tainted. For example, a hash-based message authentication code (HMAC) could be used to ensure that data has not been modified.
- When deserializing data, populate a new object rather than just deserializing. The result is that the data flows through safe input validation and that the functions are safe.
- Explicitly define a final object() to prevent deserialization.
More Deserialization of Untrusted Data vulnerabilities (CWE-502)
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/2026/07/06/9Mailing List, Third Party Advisory
Recent Deserialization of Untrusted Data vulnerabilities
- 8.8 High severity
- 9.8 Critical severity
- 7.3 High severity
- 8.1 High severity
- 8.1 High severity
- 8.1 High severity
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