| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| The BitPay Gateway for WooCommerce WordPress plugin through 4.1.3 does not bind the payment provider's transaction confirmation to the order it completes, nor verify the confirmed amount, allowing an unauthenticated attacker who holds a single genuine low-value transaction to mark arbitrary higher-value orders as paid. |
| The Sadad QA Payment WordPress plugin through 1.28 does not correctly verify the authenticity of incoming payment notifications, as the verification is keyed on a merchant secret that is empty by default and the rest of the signed data is attacker-controlled, allowing unauthenticated attackers to forge a valid confirmation and mark orders as paid without payment. |
| The Royal Elementor Addons WordPress plugin before 1.7.1070 does not validate submitted form field values before substituting them into the recipient addresses of its form notification emails, allowing unauthenticated attackers to replace the recipient list and relay attacker-composed email from the site's own domain when a recipient field is configured to echo a submitted value. |
| The Smart Popup by Supsystic WordPress plugin before 2.0.2 does not prevent template-injection payloads from being stored in a popup text block and later evaluated by its template engine, because the filter meant to strip template delimiters runs before a decoding step that reconstructs them, allowing an authenticated administrator to execute arbitrary template expressions server-side and read PHP constants such as the site's database credentials and authentication keys. |
| Link following vulnerability in Data Shredder components in Norton Antivirus, Avast Antivirus, and AVG Internet Security prior to 26.5.10933 on Windows allows a local, low-privileged attacker to escalate privileges to SYSTEM. During a privileged shred operation, enabling arbitrary file/folder deletion that is leveraged to gain NT AUTHORITY\SYSTEM privelages. |
| HCL BigFix AEX is affected by a Remote Code Execution Vulnerability . It may allow attackers to execute code with the privileges of the affected application or service, potentially leading to unauthorized access, data exposure, or system compromise. |
| HCL BigFix AEX is affected by an Unrestricted File Upload and Improper Access Control vulnerability. This could allow an attacker to upload malicious or unauthorized files and directly access or download sensitive uploaded content, potentially leading to information disclosure, unauthorized access, or system compromise. |
| HCL BigFix AEX is affected by an Information Disclosure vulnerability due to improper handling of errors or exceptional conditions. This could allow an attacker to obtain sensitive technical information, including stack traces, system details, or internal paths, potentially facilitating further attacks. |
| HCL BigFix AEX is affected by a Clickjacking vulnerability, which could allow an attacker to embed the application within a malicious web page or iframe and trick users into performing unintended actions, potentially leading to unauthorized transactions or changes to their accounts. |
| HCL BigFix AEX is affected by a Server-Side Request Forgery vulnerability, which could allow an attacker to cause the application to send unauthorized requests to external resources. Successful exploitation could lead to access to unintended services, sensitive information disclosure, or unauthorized actions through the affected application. |
| HCL BigFix AEX product is affected by a vulnerability in the aws-sdk package.. It may cause AWS API requests to be sent to unintended or non‐AWS endpoints. |
| Incorrect operator (CWE-480) in the FetchSnapshot RPC handler
in Apache Kafka from 2.8.0 before 4.2.2, and from 4.3.0 before 4.3.2
on all platforms allows an authenticated attacker with
CLUSTER_ACTION privilege to cause a denial of service
(controller process termination) via a crafted
FetchSnapshotRequest with an empty topics array or a topic
with no partitions.
Users are recommended to upgrade to version 4.2.2, 4.3.2, or
4.4.0, which fix this issue. Users on unsupported branches
(4.1.x and earlier) should upgrade to 4.2.2 or later. |
| A Kafka broker that authenticates clients with SASL/OAUTHBEARER using the bundled JWKS-backed validator always verifies the JWT signature, but two further checks are only performed when explicitly configured:
- The issuer (iss) claim is verified only when sasl.oauthbearer.expected.issuer is set. When it is unset, a validly-signed token carrying the expected audience is accepted regardless of which issuer minted it.
- An audience (aud) claim is required only when sasl.oauthbearer.expected.audience is set. When it is unset, a validly-signed token that omits the aud claim is accepted. (A token that carries an aud claim is
still rejected when no expected audience is configured.)
Both options default to unset and have been documented as optional (Importance LOW) since KIP-768 (3.1.0).
Impact:
The exposure is realizable mainly in deployments whose JWKS endpoint serves signing keys for more than one issuer (a multi-tenant identity provider, an aggregated/shared JWKS, or shared signing keys): a token
minted for a different issuer, or a token lacking an audience, may be accepted to authenticate — potentially as a higher-privileged principal. In a single-issuer deployment where the provider controls the iss
claim and issues audience-scoped tokens, exposure is limited. The signature trust anchor remains enforced in all cases. This is distinct from CVE-2026-33557 (acceptance of unsigned/unvalidated tokens); here the
signature is validated and only the issuer/audience checks are not enforced by default.
Mitigation (all affected versions, no upgrade required):
Set both sasl.oauthbearer.expected.issuer (to your provider's exact iss value) and sasl.oauthbearer.expected.audience (to the audience(s) your brokers expect), per listener. Brokers that set these perform
exact-match issuer/audience checks and are not affected. Both have been available since 3.1.0.
Resolution:
- From Apache Kafka 4.4.0: a broker configured with a JWKS endpoint fails to start unless both sasl.oauthbearer.expected.issuer and sasl.oauthbearer.expected.audience are set (secure by default). Deployments that
intentionally do not restrict the issuer and/or audience may opt out per axis with sasl.oauthbearer.allow.unverified.issuer=true and/or sasl.oauthbearer.allow.unverified.audience=true. |
| 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). |
| The broker-side PushTelemetry API (KIP-714) validates only the compressed size of a client's metrics payload against telemetry.max.bytes (default 1 MB) before decompressing it. ClientTelemetryUtils.decompress() then decompresses the payload with no limit on the decompressed output size, growing a heap buffer until the compressed input is exhausted. A small, highly compressible payload (for example gzip-compressed zeros) can therefore expand to hundreds of megabytes of broker heap.
Impact:
An authenticated client can send a PushTelemetry request with a small compressed payload that decompresses to a very large size, consuming broker heap out of proportion to the request size. Repeated or concurrent requests can exhaust the broker heap and cause an OutOfMemoryError, degrading or crashing the broker; a client that targets multiple brokers can affect cluster availability. PushTelemetry is available to any authenticated client and is not gated by an ACL.
Mitigation:
Upgrade to Apache Kafka 4.2.2, 4.3.1, or 4.4.0. On affected versions there is no configuration that fully prevents the amplification, because telemetry.max.bytes bounds only the compressed size; lowering telemetry.max.bytes reduces the worst-case decompressed size proportionally and is a partial mitigation.
Resolution:
- From Apache Kafka 4.2.2, 4.3.1, and 4.4.0: ClientTelemetryUtils.decompress() enforces a maximum decompressed size equal to telemetry.max.bytes (default 1 MB). Decompression reads in bounded chunks and aborts with a TelemetryTooLargeException once the decompressed total exceeds the limit, so an over-large or highly compressible payload is rejected instead of being fully expanded in heap. The decompressed size is therefore bounded by default. |
| 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). |
| 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. |
| 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. |
| 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 Connect contains an authentication bypass vulnerability affecting the admin REST API when admin.listeners is configured with a separate listener from the main listeners property.
When operators isolate the admin REST API by explicitly configuring the "admin.listeners" property, REST extensions specified via rest.extension.classes (such as BasicAuthSecurityRestExtension) are not registered or applied to the admin resource configuration.
While the primary REST API listener (configured via listeners) enforces configured authentication and authorization filters, endpoints mounted under the separate admin listener (/admin/loggers) fail to inherit or instantiate these extension filters. As a result, the admin logger endpoint accepts unauthenticated read and write requests even when security extensions are active on the main listener.
Impact:
An unauthenticated attacker with network access to the admin listener can:
1. Read the complete set of logger names and levels, disclosing loaded class names and which security extensions are in use.
2. Write logger levels, including with scope=cluster which propagates the change to all workers in the Connect cluster. This can be used to suppress
logging (setting root to OFF) or generate excessive log output (setting root to TRACE), potentially impacting availability and observability.
Mitigation:
Users who cannot upgrade immediately can remove the admin.listeners configuration (or leave it unset). When unset, admin resources share the regular listener and inherit its security extensions.
Resolution:
From Apache Kafka 4.2.2, 4.3.2, and 4.4.0, we make sure the REST extensions specified via "rest.extension.classes" will also apply to "admin.listeners". |
