CVE-2026-63317
Description détaillée
Arbitrary Class Instantiation via XML Feature Generator Descriptor and Format Name in Apache OpenNLP Versions Affected: - before 2.5.10 - before 3.0.0-M5 Description: Three code paths in Apache OpenNLP load a class by its fully-qualified name via Class.forName() and invoke its no-arg constructor without any prior validation of the class name or its type. The affected paths are: (1) GeneratorFactory, which reads the class attribute of generator elements in an XML feature generator descriptor; such descriptors are embedded as artifacts in model archives (e.g. TokenNameFinder and POSTagger models) and are parsed during model loading, so an attacker who can supply a crafted model archive controls the class name directly. (2) StreamFactoryRegistry.getFactory(Class, String), which falls back to interpreting an unregistered format name as the fully-qualified class name of an ObjectStreamFactory; this is exploitable in applications that pass untrusted format names (e.g. exposing the -format parameter of the command-line tooling to external input). (3) StringInterners, which instantiates the interner implementation named by the opennlp.interner.class system property; this value is normally deployer-controlled, so it is hardened as defense in depth rather than being independently attacker-reachable. Exploitation requires a class with attacker-useful side effects in its static initializer or no-arg constructor (JNDI lookup, outbound network I/O, filesystem access) to be present on the classpath, so this is not drop-in remote code execution. T Mitigation: Upgrade to a fixed release. The fix routes all three paths through ExtensionLoader.instantiateExtension(...), which consults a package-prefix allowlist before Class.forName() is invoked, so a disallowed class is never loaded, initialized, or constructed. Classes under the opennlp. prefix remain permitted by default. Deployments that load models referencing feature generator factories, object stream factories, or string interners outside opennlp.* must opt those packages in, either programmatically via ExtensionLoader.registerAllowedPackage(String) before the first model load, or by setting the OPENNLP_EXT_ALLOWED_PACKAGES system property to a comma-separated list of allowed package prefixes. Users who cannot upgrade immediately should ensure all model files and format names are sourced from trusted origins and should audit their classpath for classes with side-effecting static initializers or constructors.
Références et Patchs
Dernières Vulnérabilités
CVE-2026-56392
GNU coreutils unexpand is vulnerable to a heap-based buffer overflow due to an integer overflow during buffer allocation when processing large tab stop (-t) values. The multiplication used to calculate the allocation size can wrap around, resulting in an undersized buffer. When processing crafted input, subsequent writes exceed the allocated memory, leading to an out‑of‑bounds heap write. When running GNU coreutils unexpand with attacker-provided large tab stop (-t) arguments, this behavior leads to a crash and potentially achieve a heap write primitive depending on memory layout. This issue has been fixed in the commit b60a159fdc5bfcf9988d3a4cb6f53abe8ad5d35d
CVE-2026-56391
GNU coreutils uniq is vulnerable to an out‑of‑bounds read due to incorrect handling of multibyte input when the -w (--check-chars) option is used. The find_field() function miscalculates the byte length of characters by repeatedly processing a fixed pointer instead of advancing through the input, resulting in an inflated length value. This incorrect length is later used in a memcmp operation, causing reads beyond the allocated buffer when processing crafted multibyte input. When running GNU coreutils uniq with attacker-provided arguments, this behavior leads to a crash and potential adjacent heap memory exposure. This issue has been fixed in the commit d64e35a8a4c0e4608321433e0d84d917e4e36371.
CVE-2026-49745
Kernel software installed and running inside a Guest VM may post improper commands to the GPU Firmware to trigger a write of data outside the Guest's virtualised GPU memory. Software installed and run under a Guest VM can send commands to the GPU which result in out of bounds memory accesses. These can be used to escalate privileges.
