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4 actionable advisories, newest first. Withdrawn advisories and news are excluded. Coverage is partial.
Versions Affected: before 1.9.5, before 2.5.9, before 3.0.0-M3 Description: The ExtensionLoader.instantiateExtension(Class, String) method loads a class by its fully-qualified name via Class.forName() and invokes its no-arg constructor, with the class name sourced from the manifest.properties entry of a model archive. The existing isAssignableFrom check correctly rejects classes that are not subtypes of the expected extension interface (BaseToolFactory for factory=, ArtifactSerializer for serializer-class-), but the check runs after Class.forName() has already loaded and initialized the named class. Class.forName() with default initialization semantics executes the target class's static initializer before returning, so an attacker who can supply a crafted model archive can cause the static initializer of any class on the classpath to run during model loading, regardless of whether that class passes the subsequent type check. Exploitation requires a class with attacker-useful side…
Recommended action: Upgrade to 3.0.0-M3 (pre-release), 2.5.9 or 1.9.5 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
OOM Denial of Service via Unbounded Array Allocation in Apache OpenNLP AbstractModelReader Versions Affected: 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…
Recommended action: Upgrade to 3.0.0-M3 (pre-release), 2.5.9 or 1.9.5 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
XML External Entity (XXE) via Unsanitized Dictionary Parsing in Apache OpenNLP DictionaryEntryPersistor Versions Affected: before 2.5.9, before 3.0.0-M3 Description: The DictionaryEntryPersistor class initializes a static SAXParserFactory at class-load time without enabling FEATURESECUREPROCESSING or disabling DTD processing. When create(InputStream, EntryInserter) is invoked, the only feature set on the XMLReader is namespace support — external entity resolution and DOCTYPE declarations remain fully enabled. An attacker who can supply a crafted dictionary file (e.g., a stop-word list or domain dictionary) containing a malicious DOCTYPE declaration can trigger local file disclosure via file:// entity references or server-side request forgery via http:// entity references during SAX parsing, before the application processes a single dictionary entry. This is inconsistent with the project's own XmlUtil.createSaxParser() helper, which correctly sets FEATURESECUREPROCESSING and…
Recommended action: Upgrade to 3.0.0-M3 (pre-release), 2.5.9 or 1.9.5 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
When loading models or dictionaries that contain XML it is possible to perform an XXE attack, since Apache OpenNLP is a library, this only affects applications that load models or dictionaries from untrusted sources. The versions 1.5.0 to 1.5.3, 1.6.0, 1.7.0 to 1.7.2, 1.8.0 to 1.8.1 of Apache OpenNLP are affected.
Recommended action: Upgrade to 1.8.2 or later, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.