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191 advisories across 32 monitored vendors.
Arbitrary Class Instantiation via Model Manifest in Apache OpenNLP ExtensionLoader 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 effects in its static initializer (for example, JNDI lookup, outbound network I/O, or filesystem access) to be present on the classpath, so this is not a drop-in remote code execution; however, the attack surface grows as third-party model distribution becomes more common (community model repositories, Hugging Face-style sharing), where users routinely load model files from origins they do not control. A secondary, narrower vector affects deployments that ship legitimate BaseToolFactory or ArtifactSerializer subclasses with side-effecting no-arg constructors: a malicious manifest can name such a class and force its constructor to run during model load. Mitigation: * 2.x users should upgrade to 2.5.9. * 3.x users should upgrade to 3.0.0-M3. Note: The fix introduces a package-prefix allowlist that is consulted before Class.forName() is invoked, so the static initializer of a disallowed class is never executed. Classes under the opennlp. prefix remain permitted by default. Deployments that load models referencing factories or serializers 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 that all model files are sourced from trusted origins and should audit their classpath for classes with side-effecting static initializers or constructors, particularly any that perform JNDI lookups, network requests, or filesystem operations during class initialization.
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 FEATURE_SECURE_PROCESSING 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 FEATURE_SECURE_PROCESSING and disallow-doctype-decl and is used by all other XML parsing paths in the codebase. The public Dictionary(InputStream) constructor delegates directly to this method and is the documented API for loading user-supplied dictionaries, making untrusted input a realistic scenario. Mitigation: 2.x users should upgrade to 2.5.9. 3.x users should upgrade to 3.0.0-M3. Users who cannot upgrade immediately should ensure that all dictionary files are sourced from trusted origins and should consider wrapping the Dictionary(InputStream) constructor with input validation that rejects any XML containing a DOCTYPE declaration before it reaches the parser.
OOM Denial of Service via Unbounded Array Allocation in Apache OpenNLP AbstractModelReader Versions Affected: before 1.9.5 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 after only the model-type string, the correction constant, and the correction parameter have been read; so the attacker pays no meaningful size cost to weaponize a payload, and a single small file can crash a JVM that loads it. Any code path that deserializes a .bin model is affected, including direct use of GenericModelReader and any higher-level component that delegates to it during model load. The practical impact is denial of service against processes that load model files from untrusted or semi-trusted origins. Mitigation: * 2.x users should upgrade to 2.5.9. * 3.x users should upgrade to 3.0.0-M3. Note: The fix introduces an upper bound on each of the three count fields, checked before array allocation; counts that are negative or exceed the bound cause an IllegalArgumentException to be thrown and the read to fail fast with no large allocation. The default bound is 10,000,000, which is well above the entry counts of legitimate OpenNLP models but far below any value that would threaten heap exhaustion. Deployments that legitimately need to load models with more entries than the default 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. Users who cannot upgrade immediately should treat all .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.
Description: Improper Control of Generation of Code ('Code Injection') vulnerability in Apache Atlas Apache Atlas exposes a DSL search endpoint that accepts user-supplied query strings. Attacker can alter Gremlin traversal logic within grammar-allowed characters to access unintended data Affect Version: This issue affects Apache Atlas: from 0.8 through 2.4.0. For the affect version >= 2.0, vulnerability is only when Atlas is deployed with below non-default configuration. atlas.dsl.executor.traversal=false Mitigation: Users are recommended to upgrade to version 2.5.0, which fixes the issue.
A NULL pointer dereference in mod_dav_lock in Apache HTTP Server 2.4.66 and earlier may allow an attacker to crash the server with a malicious request.mod_dav_lock is not used internally by mod_dav or mod_dav_fs. The only known use-case for mod_dav_lock was mod_dav_svn from Apache Subversion earlier than version 1.2.0. Users are recommended to upgrade to version 2.4.66, which fixes this issue, or remove mod_dav_lock.
Double Free and possible RCE vulnerability in Apache HTTP Server with the HTTP/2 protocol. This issue affects Apache HTTP Server: 2.4.66. Users are recommended to upgrade to version 2.4.67, which fixes the issue.
Buffer Over-read vulnerability in Apache HTTP Server. This issue affects Apache HTTP Server: through 2.4.66. Users are recommended to upgrade to version 2.4.67, which fixes the issue.
An escalation of privilege bug in various modules in Apache HTTP 2.4.66 and earlier allows local .htaccess authors to read files with the privileges of the httpd user. Users are recommended to upgrade to version 2.4.67, which fixes this issue.
HTTP response splitting vulnerability in multiple Apache HTTP Server modules with untrusted or compromised backend servers. This issue affects Apache HTTP Server: from through 2.4.66. Users are recommended to upgrade to version 2.4.67, which fixes the issue.
A NULL pointer dereference in the mod_authn_socache in Apache HTTP Server 2.4.66 and earlier allows an unauthenticated remote user to crash a child process in a caching forward proxy configuration. Users are recommended to upgrade to version 2.4.67, which fixes this issue.
A timing attack against mod_auth_digest in Apache HTTP Server 2.4.66 allows a bypass of Digest authentication by a remote attacker. Users are recommended to upgrade to version 2.4.67, which fixes this issue.
Improper Null Termination, Out-of-bounds Read vulnerability in Apache HTTP Server. This issue affects Apache HTTP Server: through 2.4.66. Users are recommended to upgrade to version 2.4.67, which fixes the issue.
Out-of-bounds Read vulnerability in mod_proxy_ajp of Apache HTTP Server. This issue affects Apache HTTP Server: through 2.4.66. Users are recommended to upgrade to version 2.4.67, which fixes the issue.
The fix for CVE-2026-41635 was not applied to the 2.1.X and 2.2.X branches. Here was the original issue description: Apache MINA's AbstractIoBuffer.resolveClass() contains two branches, one of them (for static classes or primitive types) does not check the class at all, bypassing the classname allowlist and allowing arbitrary code to be executed. The fix checks if the class is present in the accepted class filter before calling Class.forName(). Affected versions are Apache MINA 2.1.0 <= 2.1.11, and 2.2.0 <= 2.2.6. The problem is resolved in Apache MINA 2.1.12, and 2.2.7 by applying the classname allowlist earlier. Affected are applications using Apache MINA that call IoBuffer.getObject(). Applications using Apache MINA are advised to upgrade.
The fix for CVE-2026-41409 was not applied to the 2.1.X and 2.2.X branches. Here was the original issue description: The fix for CVE-2024-52046 in Apache MINA AbstractIoBuffer.getObject() was incomplete. The classname allowlist of classes allowed to be deserialized was applied too late after a static initializer in a class to be read might already have been executed. Affected versions are Apache MINA 2.1.0 <= 2.1.11, and 2.2.0 <= 2.2.6. The problem is resolved in Apache MINA 2.1.12, and 2.2.7 by applying the classname allowlist earlier. Affected are applications using Apache MINA that call IoBuffer.getObject(). Applications using Apache MINA are advised to upgrade The fix for CVE-2024-52046 in Apache MINA AbstractIoBuffer.getObject() was incomplete. The classname allowlist of classes allowed to be deserialized was applied too late after a static initializer in a class to be read might already have been executed. Affected versions are Apache MINA 2.1.0 <= 2.1.110, and 2.2.0 <= 2.2.6. The problem is resolved in Apache MINA 2.1.12, and 2.2.7 by applying the classname allowlist earlier. Affected are applications using Apache MINA that call IoBuffer.getObject(). Applications using Apache MINA are advised to upgrade
Apache Neethi does not properly detect circular references in policy definitions. When a WS-Policy document contains circular policy references (where Policy A references Policy B which references Policy A), the policy normalization process can enter an infinite loop or cause excessive recursion, leading to a stack overflow or application hang. An attacker can craft malicious policy documents with circular references to cause a Denial of Service condition Users are recommended to upgrade to version 3.2.2, which fixes this issue.
Apache Neethi is vulnerable to a Denial of Service attack through algorithmic complexity in policy normalization. Specially crafted WS-Policy documents can trigger an exponential Cartesian cross-product expansion during the normalization process, causing unbounded memory allocation that exhausts the JVM heap. This occurs when the normalization process generates an excessive number of policy alternatives without bounds, leading to runtime memory exhaustion. Users should upgrade to 3.2.2 which limits the maximum number of normalized policy alternatives.
Apache Neethi does not impose any restrictions on URIs when manually fetching remote policy references through the PolicyReference API. When an application explicitly calls the API to retrieve a policy from a remote URI, an outbound request is made for arbitrary protocols and internal IP adddresses. From 3.2.2, only http or https URIs are allowed, and link-local/multicast/any-local addresses are forbidden. Users are recommended to upgrade to version 3.2.2, which fixes this issue.
Apache Airflow's SMTP provider `SmtpHook` called Python's `smtplib.SMTP.starttls()` without an SSL context, so no certificate validation was performed on the TLS upgrade. A man-in-the-middle between the Airflow worker and the SMTP server could present a self-signed certificate, complete the STARTTLS upgrade, and capture the SMTP credentials sent during the subsequent `login()` call. Users are advised to upgrade to the `apache-airflow-providers-smtp` version that contains the fix.
** UNSUPPORTED WHEN ASSIGNED ** Inconsistent Interpretation of HTTP Requests ('HTTP Request/Response Smuggling') vulnerability in Pony Mail leading to admin account takeover. This issue affects all versions of the Lua implementation of Pony Mail. There is a Python implementation under development under the name "Pony Mail Foal" that is not affected by this issue, but hasn't been released yet. As the Lua implementation of this project is retired, we do not plan to release a version that fixes this issue. Users are recommended to find an alternative or restrict access to the instance to trusted users. NOTE: This vulnerability only affects products that are no longer supported by the maintainer.