| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| Server-Side Request Forgery / Local File Inclusion in Apache MyFace Core.
Older unsupported versions may also be affected.
Users are recommended to upgrade to versions 2.3.12, 2.3-next-M9, 3.0.4, 4.0.4, or 4.1.4, which fix this issue. |
| Apache NiFi 2.11.0 disabled support for gzip-encoded HTTP requests for the application REST API and rejected requests that included the standard Content-Encoding header indicating gzip encoding. The framework enforcement filter did not check multiple instances of the Content-Encoding header and did not reject non-standard identifiers for gzip encoding, allowing a malicious client to send crafted requests that could consume excessive amounts of memory. Upgrading to Apache NiFi 2.12.0 is the recommended mitigation, which disables decompression of gzip-encoded HTTP requests regardless of header number or encoding identifiers. |
| A remote attacker could cause excessive resource consumption by supplying specially crafted request parameters, potentially resulting in a denial of service condition.
Older unsupported versions may also be affected.
Users are recommended to upgrade to versions 2.3.12, 2.3-next-M9, 3.0.4, 4.0.4, or 4.1.4, which fix this issue. |
| Apache Airflow FAB provider: changing a user's password through the Admin user-edit PATCH endpoint does not invalidate that user's existing database-backed sessions. An attacker who already holds a copy of the victim's session cookie keeps full access as that user after the password change, so the password reset does not evict them. Affects deployments using the FAB auth manager with database-backed sessions; an administrator (or the user themselves) performing a routine password change is the trigger, and no attacker interaction with the endpoint is needed.
This is a second, independent route to the outcome addressed by CVE-2026-82311, which corrected an identifier comparison in the session-invalidation helper. That fix does not repair this endpoint, because the PATCH path never calls the helper at all. Deployments that applied the CVE-2026-82311 fix must also upgrade for this one.
Users of apache-airflow-providers-fab are recommended to upgrade to version 3.9.0 or later, which fixes the issue. |
| Apache Airflow Apache Kafka provider versions 1.15.0 before 2.0.0 resolve dotted-path strings found in a Kafka connection's `extra` field into Python callables via `import_string`, with no allowlist, and hand them to the confluent-kafka client which invokes them. Deployments that have enabled the Kafka event producer — `dag_run_events_enabled` or `task_instance_events_enabled`, both disabled by default — build that client inside the scheduler process, so a user whose only privilege is editing Airflow connections gains arbitrary code execution in the control plane; the Airflow security model limits connection-configuration users to code execution on workers, not the scheduler. Deployments using Google Managed Kafka are not affected, because that code path overwrites any user-supplied `oauth_cb`; plain brokers and Amazon MSK are exposed. Users are recommended to upgrade to apache-airflow-providers-apache-kafka 2.0.0 or later, which adds an allowlist configuration option for connection-string callbacks. |
| Apache Airflow Keycloak provider: from Airflow 3.3 the Keycloak auth manager takes a user's identity from the signed Airflow session token but takes the Keycloak access and refresh tokens used for every authorization decision from separate, unauthenticated cookies, and never checks that the two describe the same subject. A user who holds any valid Airflow login of their own, together with another subject's Keycloak access or refresh token obtained out of band, can pair the two: Airflow then authorizes requests with the foreign token's privileges while the session identity, audit log and cache keys continue to name the attacker's own account. The refresh path re-issues an Airflow session token for the original identity carrying the foreign tokens, so the mismatched pairing survives across sessions.
Affects deployments running Airflow 3.3 or later with the Keycloak auth manager. Earlier versions carried the Keycloak tokens inside the signed session token, so the binding existed and was lost when they moved into separate cookies.
Users of apache-airflow-providers-keycloak are recommended to upgrade to version 0.10.0 or later, which binds the cookie-supplied tokens to the session identity. |
| Apache Airflow Keycloak provider: the unauthenticated token endpoint accepts a client-credentials grant for any confidential client registered in the Keycloak realm, not only the client configured for Airflow. No allowlist restricts which client ids may authenticate, so the credentials of an unrelated application that happens to share the realm are valid Airflow login credentials, and Airflow mints a signed session token for that application's service account. The endpoint also answers unauthenticated credential guesses against Keycloak under Airflow's identity.
Affects deployments using the Keycloak auth manager whose realm is shared with other confidential clients. The attacker needs valid credentials for any one of those clients, not for Airflow. Resource authorization is still evaluated per subject, so the access gained is whatever that service account holds, plus any endpoint gated only on being authenticated.
Users of apache-airflow-providers-keycloak are recommended to upgrade to version 0.10.0 or later, which accepts only the configured client on that grant. |
| Apache Airflow FAB provider: the Authentik OAuth path in the FAB auth manager does not validate the issuer or audience claims of the id_token it accepts. An attacker holding a token that the same Authentik identity provider minted for a different client application can present it to Airflow and be authenticated as the user it names, because the audience claim is never checked. Affects deployments using the FAB auth manager with Authentik OAuth where the same Authentik instance also serves other applications; the attacker needs a valid token for any of those other applications, not for Airflow.
CVE-2026-75156 corrected the same missing validation on the Azure AD path in this file; the Authentik path was left unchanged and is fixed here. Deployments that applied the CVE-2026-75156 fix and use Authentik must also upgrade for this one.
Users of apache-airflow-providers-fab are recommended to upgrade to version 3.9.0 or later, which fixes the issue. |
| Information disclosure via SetWatches reconnect replay in Apache ZooKeeper due to missing ACL check. An attacker can discover ACL-restricted paths by registering exists-watches on non-existent paths, then reconnecting after the paths are created with restricted ACLs. Issue is caused by incomplete fix for CVE-2024-23944 (ZOOKEEPER-4799). The fix added ACL checking to WatchManager.triggerWatch(). However, DataTree.setWatches() — the SetWatches/SetWatches2 reconnect replay handler — still calls watcher.process(event) with null ACL, bypassing the check entirely. It's important to note that only the path is exposed by this vulnerability, not the data of znode, but since znode path can contain sensitive information like user name or login ID, this issue is potentially critical.
Users are recommended to upgrade to version 3.9.6, 3.8.7 which fixes the issue. |
| Apache ZooKeeper quorum TLS fails to enforce peer hostname verification in FIPS-mode deployments. When sslQuorum=true, zookeeper.fips-mode=true, ssl.quorum.hostnameVerification=true, and ssl.quorum.clientHostnameVerification=true are enabled, the Java SSLSocket quorum path accepts a CA-trusted peer certificate whose SAN does not match the connected host. A malicious or misissued peer certificate can therefore join quorum traffic, participate in leader election, and enter replication flows.
Users are recommended to upgrade to version 3.8.7 or 3.9.6, which fixes the issue. |
| Apache Airflow FAB provider: resetting a user's password does not delete that user's existing database-backed sessions, despite documented behaviour that it does. The cleanup compares the string identifier Flask-Login stores in the session against the user's integer database identifier, so the comparison never matches and no session is removed. An attacker who already holds a copy of the victim's session cookie keeps access as that user after the password change, so the reset does not evict them.
Affects deployments using the FAB auth manager with `[fab] session_backend=database`. The trigger is an administrator (or the user) running the supported password-reset command as a containment action after a session cookie has been compromised; the secure-cookie backend is out of scope, as it documents that it cannot centrally delete sessions.
apache-airflow-providers-fab 3.9.0 also fixes CVE-2026-86462, a second, independent route to the same outcome via the Admin user-edit endpoint; a single upgrade closes both.
Users of apache-airflow-providers-fab are recommended to upgrade to version 3.9.0 or later, which compares the identifiers consistently. |
| Apache Airflow FAB provider: deactivating a user account does not stop tokens issued to that account before deactivation. Password authentication correctly rejects the disabled account, but the Core API continues to accept an existing, unexpired token naming it, and lets that token mint a replacement — so the account keeps its role-scoped access indefinitely after an administrator has disabled it. The user replays their own legitimate credential; no signature forgery or privilege escalation is involved, and the access stays within the roles the account already held.
Affects deployments using Airflow 3 with the FAB auth manager and Core API token authentication, where an administrator deactivates an account whose row remains in the database and whose previously issued token has not expired. The trigger is administrative deactivation as a containment action, which silently fails to contain.
Users of apache-airflow-providers-fab are recommended to upgrade to version 3.9.0 or later, which rejects tokens naming a deactivated account. |
| The `deleteContainer` opcode (0x14/20) is processed without verifying the caller's ACL permissions, allowing any authenticated client to delete specific znodes in the data tree regardless of the ACL restrictions on the znode or its parent. This opcode is considered internal-only and the official client doesn't have API for it, but a client that can open a plain TCP session on the ZooKeeper client port (2181 by default) - with NO authentication and NO ACL permissions - can delete any empty persistent znode (including regular persistent nodes, container nodes, and TTL nodes) by issuing the raw protocol OpCode deleteContainer (20). The deleteContainer request path completely skips both the session check and the DELETE ACL check that are enforced by the regular delete (OpCode 2) path. This is an authorization bypass / ACL enforcement bug.
This issue affects Apache ZooKeeper: from 3.9.0 through 3.9.5, from 3.8.0 through 3.8.6.
Users are recommended to upgrade to version 3.9.6 or 3.8.7, which fixes the issue. |
| Improper Input Validation vulnerability in Apache Tomcat.
This issue affects Apache Tomcat: from 11.0.0-M1 through 11.0.21, from 10.1.0-M1 through 10.1.54, from 9.0.0.M1 through 9.0.117, from 10.0.0-M1 through 10.0.27.
Older, end of support versions may also be affected.
Users are recommended to upgrade to version [FIXED_VERSION], which fixes the issue. |
| The two built-in name-finder patterns exposed by
opennlp.tools.namefind.RegexNameFinderFactory - DEFAULT_REGEX_NAME_FINDER.EMAIL
and DEFAULT_REGEX_NAME_FINDER.URL - contain ambiguous nested quantifiers. An
application that obtains these finders through
RegexNameFinderFactory.getDefaultRegexNameFinders(...) and then applies them to
untrusted text through RegexNameFinder.find(String[]) or RegexNameFinder.find(String)
can be driven into super-linear backtracking or into unbounded matcher recursion by a
small crafted input.
For the EMAIL pattern, a long run of local-part characters that is never followed by an
@ forces the matcher to re-scan to end-of-input from every starting offset. Cost grows
quadratically with input length: an input of approximately 32 KB consumes several seconds
of CPU in a single find() call and returns no match, and each doubling of the input
multiplies the cost roughly four-fold.
For the URL pattern, the query-string sub-expression nests a capturing repetition inside
an outer repetition. The JDK matcher recurses once per query token, so an input of
approximately 4 KB containing many &-separated tokens exhausts the thread stack and
causes java.lang.StackOverflowError to propagate out of find(), terminating the
calling thread. On a thread created with a smaller stack (for example -Xss512k, typical
of server worker pools) approximately 1 KB is sufficient.
In both cases an attacker who can supply text for analysis can convert a single request
into seconds to minutes of pinned CPU, or into an abrupt thread death, denying service to
the embedding application. No authentication, special configuration, or model file is
required beyond the application having selected one of the two built-in finders.
This issue affects Apache OpenNLP: from 2.0.0 through 2.5.11; from 3.0.0-M1 through
3.0.0-M5.
Users are recommended to upgrade to version 2.5.12, or to 3.0.0-M6 for users tracking the
3.0.0 milestone line, which fix the issue. |
| OOM Denial of Service via Unbounded Map Pre-Sizing in Apache OpenNLP SymSpellModelSerializer
Versions Affected:
- 3.0.0-M4
- 3.0.0-M5
(The opennlp-spellcheck extension was introduced in 3.0.0-M4. Releases 1.x and 2.x do not contain the affected code.)
Description:
The SymSpellModelSerializer.create() method reads two 32-bit signed integer count fields (unigramCount and bigramCount) from a binary SymSpell model stream and passes each value directly to LinkedHashMap.newLinkedHashMap() after validating only that it is non-negative. No upper bound is applied, so the count is fully attacker-controlled when the model file originates from an untrusted source.
A crafted .bin model file in which either count field is set to Integer.MAX_VALUE (or any value large enough to exhaust the available heap) causes the map to be pre-sized to a capacity of 2^30 entries. The oversized backing array is allocated on the first put() into that map, requesting 4–8 GB depending on whether compressed oops are in effect, and the load fails with an OutOfMemoryError. Because the count fields sit immediately after a fixed-size header (magic, format version, three UTF strings, the configuration fields, and the edit-distance identifier) the attacker pays no meaningful size cost to weaponize a payload: a file of well under 100 bytes plus a single real entry is sufficient to crash a JVM that loads it.
Any code path that deserializes a SymSpell model is affected, including SymSpellModels.deserialize(InputStream), SymSpellModels.fromBytes(byte[]), classpath model loading via SymSpellModelResolver.resolveByLanguage(String), the CorrectTextTool command-line tool, and model-archive loading through the registered ArtifactSerializer. The opennlp-spellcheck extension ships in the official OpenNLP binary distribution.
The practical impact is denial of service against processes that load SymSpell model files from untrusted or semi-trusted origins.
Mitigation:
- 3.x users should upgrade to 3.0.0-M6.
Note: The fix applies an upper bound to both count fields, checked before the map is pre-sized; counts that are negative or exceed the bound cause an IOException to be thrown and the read to fail fast with no large allocation. The bound is the existing AbstractModelReader.MAX_ENTRIES limit introduced earlie, which the current change promotes to public visibility so that serializers implementing their own binary format can share it. The default bound is 10,000,000, which is well above the entry counts of legitimate SymSpell dictionaries but far below any value that would threaten heap exhaustion. Deployments that legitimately need to load larger dictionaries 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. Note that this property is shared with the model-reader limit and raising it relaxes both.
Users who cannot upgrade immediately should treat all SymSpell .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. |
| An unauthenticated remote attacker can create arbitrary durable queues via the CORE protocol, leading to unauthorized broker state manipulation and potential denial of service.
This issue affects Apache Artemis: from 2.50.0 through 2.56.0; Apache ActiveMQ Artemis: from 1.0.0 through 2.44.0.
Users are recommended to upgrade to version 2.57.0, which fixes the issue. |
| An unauthenticated remote attacker connecting with the CORE protocol can discover cluster node details by sending a SUBSCRIBE_TOPOLOGY request prior to authentication.
This issue affects Apache Artemis: from 2.50.0 through 2.56.0; Apache ActiveMQ Artemis: from 1.0.0 through 2.44.0.
Users are recommended to upgrade to version 2.57.0, which fixes the issue. |
| An unauthenticated network-adjacent attacker can leverage discovery to capture cluster administrative credentials during the initial cluster connection handshake.
This issue affects Apache Artemis: from 2.50.0 through 2.56.0; Apache ActiveMQ Artemis: from 1.0.0 through 2.44.0.
Users are recommended to upgrade to version 2.57.0, which fixes the issue. |
| An unauthenticated remote attacker can craft a CORE protocol SESSION_REATTACH packet to steal an existing session and assume ongoing execution of the previously authenticated session.
This issue affects Apache Artemis: from 2.50.0 through 2.56.0; Apache ActiveMQ Artemis: from 1.0.0 through 2.44.0.
Users are recommended to upgrade to version 2.57.0, which fixes the issue. |
