| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| Highcharts JS is a JavaScript charting library based on SVG. From 10.0.0 until 11.4.9, 12.6.1, and 13.1.0, Highcharts applies breadcrumbs.style as SVG attributes rather than CSS and does not filter the attribute names. An attacker who controls chart configuration can provide event-handler keys through drilldown.breadcrumbs, navigation.breadcrumbs, or hierarchical series breadcrumb options. The keys become active handlers on the breadcrumb button and execute in the hosting page origin after the user navigates to a level and hovers over or activates the button. Styled-mode charts do not apply this option and are not affected. This issue is fixed in 11.4.9, 12.6.1 and 13.1.0. |
| Highcharts JS is a JavaScript charting library based on SVG. From 12.0.0 until 12.6.1 and 13.1.0, Highcharts falls back to assigning raw markup to innerHTML when DOMParser cannot return a document, before its allow-list is applied. In Chromium, the Trusted Types callback can throw while a beforeprint handler runs, so a page that redraws a chart during beforeprint can insert untrusted HTML text options into the hosting document. Event handlers in that markup then run in the page origin when printing begins. The issue affects HTML-rendered titles, labels, data labels, responsive text, and the Stock, Maps, and Gantt products shipped in the same package. This issue is fixed in 12.6.1 and 13.1.0. |
| Highcharts JS is a JavaScript charting library based on SVG. From 2.3.0 until 3.2.0, Highcharts Grid creates SVG path elements from rendering.icons object definitions and previously copied every child key with setAttribute. An attacker who controls Grid configuration can supply event-handler attribute names in an icon definition. Because custom icons override built-ins, the attacker can replace default sort or pagination icons, and script runs in the hosting page origin when a user hovers over or activates the affected icon. This issue is fixed in 3.2.0. |
| Highcharts JS is a JavaScript charting library based on SVG. From 2.0.0 until 3.2.0, Highcharts Grid Pro input renderers copy the cells.renderer.attributes and cells.editMode.renderer.attributes maps to input or select elements with raw setAttribute calls. An attacker who controls column configuration can add event-handler attributes to every rendered row or to the control created when a cell enters edit mode. Script then runs in the hosting page origin when a user hovers over or focuses an input or begins editing. This issue is fixed in Grid Pro 3.2.0. |
| Szafir accepts any client or server certificate without performing certificate and hostname validation, allowing HTTPS connections without authenticating the remote server. An attacker can provide an arbitrary SSL/TLS certificate and cause the application to download an update file from their server.
This issue was fixed in version 2.0 build 708. |
| Szafir is vulnerable to Path Traversal in the uploaded filename. A man-in-the-middle attacker who can reroute update request to their server can prepare arbitrary XML file that will be saved in location chosen by attacker.
This issue was fixed in version 2.0 build 708. |
| Szafir and Szafir Weryfikująca are vulnerable to XML External Entity (XXE) injection. When a user signs or verify a crafted XML file containing a `DOCTYPE` with an external DTD reference, the parser processes external entities, allowing an attacker to read arbitrary files from the victim's filesystem.
This issue was fixed in Szafir version 2.0 build 708. Szafir Weryfikująca is no longer supported (End of Life) and has not been fixed. |
| Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting') vulnerability in POSIMYTH Innovations The Plus Addons for Elementor Page Builder Lite the-plus-addons-for-elementor-page-builder allows Stored XSS.This issue affects The Plus Addons for Elementor Page Builder Lite: from n/a through 6.5.3. |
| Several Kafka deserialization paths read a size or count field from input data and use it directly to allocate an array, collection, or byte buffer, with no check against the bytes actually available. A small crafted input can therefore declare a very large size and force a multi-gigabyte allocation. The affected paths are, in Kafka Streams, ProcessorRecordContext (header count), ChangedDeserializer (varint value length), CombinedKeySchema (foreign-key length, which could also produce a negative size), ProcessorMetadata (key length), and AssignmentInfo group-assignment decoding; and, in the clients library, ListDeserializer (used by ListSerde).
Impact:
An attacker can trigger the oversized allocation and raise an OutOfMemoryError that crashes the affected consumer or Kafka Streams thread; the broker and the rest of the cluster are unaffected. The reachable surface depends on the path: writing a crafted record to a Streams application's internal changelog or repartition topics, or committing crafted offset metadata to its consumer group (Streams internal paths); writing a crafted record to any topic that a consumer reads with ListSerde (ListDeserializer); or, as a member of a Streams consumer group, returning crafted assignment data when elected leader (AssignmentInfo). Because a crafted record remains in its topic, the error can recur on restart. The record size limit (max.message.bytes) does not help, because the allocation is driven by a size field rather than by the input's length.
Mitigation:
Upgrade to Apache Kafka 4.2.2, 4.3.2, or 4.4.0. Where authorization is enabled, restrict write access to each application's internal topics and consumer group to its own principal, and restrict who can produce to topics that are consumed with ListSerde.
Resolution:
From Apache Kafka 4.2.2, 4.3.2, and 4.4.0: these deserialization paths validate the declared size against the remaining input before allocating and reject input that fails the check. |
| Apache Kafka's code-generated readers for flexible protocol versions read a tagged-field count declared by the sender and retain a RawTaggedField object for each unknown field. That count was bounded only by the outer request frame size (socket.request.max.bytes); it was not checked against the remaining buffer bytes or capped. Because each entry can be encoded in as little as two bytes (unknown tag 0, size 0), a modest request can declare tens of millions of tagged fields and force one object allocation per declared field. Flexible request headers (header version 2, used by flexible APIs such as ApiVersions v3) are parsed this way, so the amplification applies to the request header itself.
Impact:
The flexible request header is parsed on the network thread before authorization, request dispatch, and request-time quota accounting. A client can send a request of a few tens of megabytes whose header declares about 20 million tagged fields, forcing roughly that many RawTaggedField allocations and exhausting broker heap or inducing process-wide GC stalls, denying broker service. Because the header is parsed for pre-authentication APIs such as ApiVersions (v3), the allocation can occur before authentication; no Kafka resource ACL or quota is required.
Mitigation:
Upgrade to Apache Kafka 4.2.2, 4.3.2, or 4.4.0. Until then, lowering socket.request.max.bytes and restricting network access to broker listeners reduces, but does not remove, exposure.
Resolution:
From Apache Kafka 4.2.2, 4.3.2, and 4.4.0: generated readers bounds-check the tagged-field count for flexible-versioned messages, rejecting a negative count, a count exceeding the remaining buffer bytes, or a count exceeding a fixed maximum (10,000), before any per-field allocation. |
| Apache Kafka's code-generated protocol readers pre-size an array's collection from the element count declared by the sender. That count is checked only against the bytes remaining in the request buffer, which assumes about one byte per element; because most elements need several bytes, a request can declare far more elements than the buffer holds and still pass. For a keyed array the reader then allocates an ImplicitLinkedHashCollection of capacity (2 * count +1), so a request of a few tens of megabytes can force a heap allocation several times larger before any element is read. The pattern is emitted into every generated reader, so it affects all Kafka API types with an array field.
Impact:
Request parsing happens before any authorization check. On a listener that does not require authentication (for example PLAINTEXT), an unauthenticated attacker can send a few concurrent requests that each declare a very large=
array length and allocate many times their wire size in broker heap, crashing the broker with an OutOfMemoryError. The reporter demonstrated three concurrent ~90 MB ProduceRequest (v3) connections exhausting a 1 GB heap (~8x amplification). On listeners that require authentication, the attacker needs valid credentials but no specific ACL, since parsing precedes authorization..
Mitigation:
Upgrade to Apache Kafka 4.2.2, 4.3.2, or 4.4.0. Until then, lowering socket.request.max.bytes and restricting network access to broker listeners reduces, but does not remove, exposure.
Resolution:
From Apache Kafka 4.2.2, 4.3.2, and 4.4.0: generated readers cap the initial array pre-allocation at MessageUtil.MAX_PREALLOCATED_ARRAY_CAPACITY (1000) and grow it on demand, and reject a declared array length above MessageUtil.MAX_ARRAY_LENGTH (1,000,000). |
| A Kafka broker using SASL/OAUTHBEARER with the default validator (sasl.oauthbearer.jwt.validator.class=DefaultJwtValidator) selects its implementation from configuration. When sasl.oauthbearer.jwks.endpoint.url is set, the validator verifies the JWT signature. When that URL is unset (the default) and no verification key resolver is supplied, DefaultJwtValidator silently falls back to ClientJwtValidator, which does not verify the JWT signature, algorithm, or issuer - and the broker starts with no warning or error. This is the residual of CVE-2026-33557, whose fix restored signature verification only on the path where the JWKS endpoint URL is set.
Impact:
A broker in this state accepts any structurally valid JWT, including "alg":"none" tokens. An unauthenticated remote attacker who can reach the listener can forge a token asserting any principal - including configured superusers - and gain full access to the cluster. Only brokers that enable SASL/OAUTHBEARER validation without a JWKS endpoint URL (and without a custom verification key resolver) are affected; brokers that set the URL verify signatures and are not affected. Unlike CVE-2026-110952 (issuer/audience not enforced by default), here no signature is verified at all.
Mitigation (all affected versions, no upgrade required):
Set sasl.oauthbearer.jwks.endpoint.url for every listener/mechanism that uses OAuthBearerValidatorCallbackHandler, or configure a signature-verifying sasl.oauthbearer.jwt.validator.class.
Resolution:
From Apache Kafka 4.2.2, 4.3.1, and 4.4.0: a broker that would fall back to the non-verifying ClientJwtValidator fails to start with a ConfigException instead of running without signature verification (secure by default). |
| A BSON encoding component of the MongoDB Java Driver does not verify that an externally supplied raw document's self-declared length agrees with the extent of the byte range it is given before that range is copied into an outgoing database command. An application that wraps untrusted raw bytes in such a document and uses it as part of a command can therefore emit a structurally inconsistent message, causing the database to interpret attacker-supplied trailing content as additional fields of the surrounding command. No authentication to the driver or database is required beyond the ability to influence those bytes through the victim application, and no user interaction is needed. The result is unintended modification of the command actually executed, which can alter the data read or written under the application's credentials. |
| Kafka Streams derives its stream-time clock from record timestamps. PunctuationSchedule.next() computed the next punctuation time as timestamp + (intervalsMissed + 1) * interval with no overflow check. A record with a timestamp at or near Long.MAX_VALUE advances stream time to that value, so the next stream-time punctuation overflows to a negative time. PunctuationQueue then re-queues that time and loops on it indefinitely while holding a lock, stalling the StreamThread.
Impact:
A single record is enough. The affected StreamThread stops processing, and because the record stays in the topic the hang returns on restart; an application whose partitions are all affected becomes unresponsive. Only the Streams application is affected, not the brokers or the cluster. Triggering it needs write access to a source topic whose topology uses stream-time punctuation, and a broker that stores the record: brokers from Apache Kafka 4.0.0 reject such a timestamp by default (log.message.timestamp.after.max.ms, one hour), so it is reachable only on older brokers or where that bound has been relaxed.
Mitigation:
Upgrade Kafka Streams to 4.2.2, 4.3.2, or 4.4.0. As defense in depth, keep log.message.timestamp.after.max.ms bounded on source topics and restrict write access to them.
Resolution:
From Apache Kafka 4.2.2, 4.3.2, and 4.4.0: the next punctuation time is computed with overflow-checked arithmetic (clamped to Long.MAX_VALUE), and a rescheduled punctuation is re-queued only when its time moves forward. |
| A vulnerability was identified in Ndemic Creations Plague Inc Evolved up to 1.24.0.3. The affected element is an unknown function of the component Workshop Scenario Loader. Such manipulation leads to deserialization. The attack may be performed from remote. Upgrading to version 1.24.0.4 is sufficient to fix this issue. It is recommended to upgrade the affected component. |
| Use after free in Windows Message Queuing Queue Manager allows an authorized attacker to execute code locally. |
| Missing Authorization vulnerability in weDevs Happy Addons for Elementor happy-elementor-addons allows Exploiting Incorrectly Configured Access Control Security Levels.This issue affects Happy Addons for Elementor: from n/a through 3.50.1. |
| Missing authentication for critical function in Windows Health and Optimized Experiences Service allows an authorized attacker to elevate privileges locally. |
| Improper privilege management in Windows Internet (wininet.dll) allows an authorized attacker to perform spoofing locally. |
| Untrusted pointer dereference in Windows COM+ Event System Service allows an authorized attacker to elevate privileges locally. |
