Apache Opennlp
Monthly
Untrusted Java deserialization in Apache OpenNLP's SvmDoccatModel (libsvm document categorization module, versions 3.0.0-M1 through before 3.0.0-M4) lets an attacker who supplies a crafted serialized stream to the public static SvmDoccatModel.deserialize(InputStream) trigger deserialization of an arbitrary object graph before the SvmDoccatModel cast occurs. Where a usable gadget chain exists on the consuming application's classpath, this yields remote code execution in the loading JVM; OpenNLP ships no gadget itself, so realistic risk falls on downstream apps that embed the module alongside vulnerable transitive dependencies. No public exploit identified at time of analysis and the flaw is not in CISA KEV, though the SSVC assessment marks it automatable with partial technical impact.
XML External Entity injection in Apache OpenNLP's DictionaryEntryPersistor allows remote unauthenticated attackers to disclose local files or perform server-side request forgery when processing untrusted dictionary files. The vulnerable SAX parser initialization omits critical security features (FEATURE_SECURE_PROCESSING, DTD disablement) present elsewhere in the codebase, creating an inconsistency exploitable via the public Dictionary(InputStream) API when loading stop-word lists or domain dictionaries. With EPSS at 0.03% (8th percentile) and no active exploitation reported, this represents a code-quality issue in a specific input path rather than an imminent widespread threat, though the CVSS 9.1 reflects maximum theoretical impact given the network-accessible, unauthenticated attack vector.
Apache OpenNLP's model loading mechanism executes arbitrary static initializers through crafted manifest entries, enabling attackers to trigger side effects in any classpath class before type validation occurs. Affects OpenNLP versions before 2.5.9 and 3.0.0-M3. While not direct RCE, exploitation becomes viable when third-party models from untrusted sources (community repositories, model-sharing platforms) are loaded in environments containing classes with JNDI lookups, network I/O, or filesystem operations in static initializers. EPSS score of 0.29% suggests low widespread exploitation probability despite CVSS 9.8, though attack surface grows with model-sharing ecosystem adoption. No public exploit identified at time of analysis; vendor-released patches available.
Remote denial of service in Apache OpenNLP versions before 2.5.9 and 3.0.0-M3 allows unauthenticated attackers to crash JVM processes by uploading malicious .bin model files that trigger OutOfMemoryError through unbounded array allocation. Exploitation requires no authentication (AV:N/AC:L/PR:N) and affects any code path deserializing binary model files from untrusted sources. EPSS score of 0.02% (5th percentile) suggests low widespread exploitation risk, and no active exploitation or public POC has been identified at time of analysis. Vendor-released patches are available with default safeguards limiting count fields to 10 million entries.
Untrusted Java deserialization in Apache OpenNLP's SvmDoccatModel (libsvm document categorization module, versions 3.0.0-M1 through before 3.0.0-M4) lets an attacker who supplies a crafted serialized stream to the public static SvmDoccatModel.deserialize(InputStream) trigger deserialization of an arbitrary object graph before the SvmDoccatModel cast occurs. Where a usable gadget chain exists on the consuming application's classpath, this yields remote code execution in the loading JVM; OpenNLP ships no gadget itself, so realistic risk falls on downstream apps that embed the module alongside vulnerable transitive dependencies. No public exploit identified at time of analysis and the flaw is not in CISA KEV, though the SSVC assessment marks it automatable with partial technical impact.
XML External Entity injection in Apache OpenNLP's DictionaryEntryPersistor allows remote unauthenticated attackers to disclose local files or perform server-side request forgery when processing untrusted dictionary files. The vulnerable SAX parser initialization omits critical security features (FEATURE_SECURE_PROCESSING, DTD disablement) present elsewhere in the codebase, creating an inconsistency exploitable via the public Dictionary(InputStream) API when loading stop-word lists or domain dictionaries. With EPSS at 0.03% (8th percentile) and no active exploitation reported, this represents a code-quality issue in a specific input path rather than an imminent widespread threat, though the CVSS 9.1 reflects maximum theoretical impact given the network-accessible, unauthenticated attack vector.
Apache OpenNLP's model loading mechanism executes arbitrary static initializers through crafted manifest entries, enabling attackers to trigger side effects in any classpath class before type validation occurs. Affects OpenNLP versions before 2.5.9 and 3.0.0-M3. While not direct RCE, exploitation becomes viable when third-party models from untrusted sources (community repositories, model-sharing platforms) are loaded in environments containing classes with JNDI lookups, network I/O, or filesystem operations in static initializers. EPSS score of 0.29% suggests low widespread exploitation probability despite CVSS 9.8, though attack surface grows with model-sharing ecosystem adoption. No public exploit identified at time of analysis; vendor-released patches available.
Remote denial of service in Apache OpenNLP versions before 2.5.9 and 3.0.0-M3 allows unauthenticated attackers to crash JVM processes by uploading malicious .bin model files that trigger OutOfMemoryError through unbounded array allocation. Exploitation requires no authentication (AV:N/AC:L/PR:N) and affects any code path deserializing binary model files from untrusted sources. EPSS score of 0.02% (5th percentile) suggests low widespread exploitation risk, and no active exploitation or public POC has been identified at time of analysis. Vendor-released patches are available with default safeguards limiting count fields to 10 million entries.