| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| An excessive resource allocation issue in the remote workflow editing capabilities of KNIME Business Hub prior to version 1.19.0 allows a authenticated attack to exhaust resources on the server side. This leads to intermittent unavailability of workflow execution functionality. A workaround is not possible. |
| Next.js is a React framework for building full-stack web applications. From 13.0.0 until 15.5.28 and 16.4.1, crafted requests targeting App Router applications with at least one Server Action can cause excessive CPU consumption and block processing of further requests in the same process. Applications using the Pages Router and App Router applications without Server Actions are not affected. This issue is fixed in versions 15.5.28 and 16.4.1. |
| Allocation of Resources Without Limits or Throttling vulnerability in Drupal Webform allows Excessive Allocation. This issue affects Webform versions: from 0.0.0 to 6.2.12, from 6.3.0 to 6.3.1. |
| In lsquic before 4.9.0, the HTTP/3 implementation can use an excessive amount of memory because the number of buffered header sets is not limited, as demonstrated by a QPACK amplification attack. This is related to header_out_dynamic_entry and es_max_header_list_size. |
| The RTMP AMF parser in Apache bRPC accepts AMF string lengths and array counts directly from untrusted input. A remote unauthenticated attacker who can reach a bRPC server with RTMP enabled can send a small AMF payload with an excessive declared long string length or array count, causing excessive memory allocation or resource consumption before the parser rejects the incomplete payload.
Root Cause:
The AMF long string parser resized the destination string to the declared 32-bit length before validating that the stream contained that many bytes. ECMA array and strict array parsers also accepted 32-bit element counts without a configurable upper bound.
Affected Scenarios:
Deployments that enable the bRPC RTMP server component and accept RTMP traffic from untrusted clients. Direct uses of the AMF parser on untrusted data are also affected.
How to Fix:
1. Upgrade bRPC to version 1.18.0, which fixes this issue.
2. Apply this patch: https://github.com/apache/brpc/pull/3353
Note:
The fix introduces configurable AMF parsing limits. The default maximum string size is 64 MiB and the default maximum array element count is 1,048,576. Payloads above these limits are rejected. The limits can be adjusted with amf_max_string_size and amf_max_array_size. |
| The Redis protocol implementation in Apache bRPC allowed inline command parsing to allocate and copy the full connection buffer before a complete CRLF-terminated command was available. A remote attacker with access to a Redis-protocol bRPC service could send long inline inputs without a terminating CRLF to increase memory usage and CPU copy overhead, potentially causing denial of service.
Root Cause:
The Redis inline command parser allocated memory based on the current input buffer size and copied the entire buffer before determining the current inline command boundary. The existing redis_max_allocation_size limit was applied to RESP bulk strings and arrays, but not effectively to the inline command path before allocation.
Affected Scenarios:
Use Apache bRPC server with Redis protocol enabled and reachable by untrusted clients.
How to Fix:
1. Upgrade Apache bRPC to version 1.18.0, which fixes this issue.
2. Apply the patch from PR https://github.com/apache/brpc/pull/3363 .
Note:
The fix applies redis_max_allocation_size to Redis inline command lines and scans for CRLF before allocating parser arena memory. Inline command lines exceeding this limit are rejected. |
| Uncontrolled resource allocation in the mcpack2pb component in Apache bRPC (version < 1.18.0) on all platforms allows remote attackers to make the server allocate a large amount of memory via sending a crafted mcpack request with a huge array item count.
Root Cause:
The bRPC mcpack2pb parser trusts the item count stored in the mcpack array header without validating it against the actual payload size. The code generated by the mcpack2pb protoc plugin uses this count to preallocate memory for repeated protobuf fields via Reserve(). A malformed request declaring item_count = 0x7fffffff can force the server to preallocate about 16GB of virtual memory, which may abort the process (SIGABRT) on memory-constrained hosts.
Affected Scenarios:
Use a bRPC server that wires a NsheadService to UbrpcMcpack2Adaptor (the Baidu ubrpc deployment pattern) and parses requests with code generated by the mcpack2pb protoc plugin. The request body is parsed before authentication.
How to Fix:
(Choose one of the following options)
1. Upgrade bRPC to version 1.18.0, which fixes this issue.
2. Apply this patch: https://github.com/apache/brpc/pull/3451
Note:
No matter which option you choose, you should know that the fix caps the array item count to the remaining bytes of the array payload. |
| Resource exhaustion in Apache bRPC (version < 1.18.0) on all platforms: multiple parsers that accept wire-controlled input bound no limit on the sizes declared by the client or on decompressed output, which attackers can use to drive disproportionate memory use and crash the process.
Root Cause:
1) The gzip/zlib/snappy decompressors enforce no output limit: -max_body_size is checked against the compressed bytes only, so a cap-compliant body (e.g. 64 MiB) can decompress to tens of GiB.
2) The AMF string readers allocate the declared length before the bytes are actually available.
3) RTMP chunk headers may re-declare a message length mid-stream; the declared length was never bounded and the partial reassembly buffer kept growing.
4) The sofa-pbrpc parser does not bound meta_size.
5) mcpack2pb trusts wire-declared item counts for Reserve() and a string value_size of 0 underflows the buffer size, aborting the process.
Affected Scenarios:
Any bRPC service accepting gzip/zlib/snappy-compressed bodies or rtmp, sofa-pbrpc, or mcpack2pb messages from untrusted clients.
How to Fix:(Choose one of the following options) 1. Upgrade bRPC to version 1.18.0, which fixes this issue.
2. Apply this patch: https://github.com/apache/brpc/pull/3476
Note:
After the fix, requests whose decompressed size, RTMP message length, or mcpack declared count exceed the new limits are rejected. Operators who legitimately need larger values can raise the corresponding flag (-max_decompressed_body_size, max_body_size). |
| Unbounded memory accumulation in the stream message reassembly path of Apache bRPC (version < 1.18.0) on all platforms allows remote attackers to exhaust the memory of a server (denial of service) by sending an endless sequence of continuation data frames.
Root Cause:
The streaming RPC protocol bounds every single data frame by max_body_size, but a fragmented stream message accumulates continuation frames into one buffer (_pending_buf) without any limit on the accumulated size. A malicious peer that can open a stream can therefore keep sending continuation frames to make the receiver accumulate an arbitrarily large in-memory message. Since the default flow-control settings (socket_max_streams_unconsumed_bytes=0) do not throttle the sending rate, the receiver's memory grows until the process runs out of memory and crashes or becomes unavailable.
Affected Scenarios:
Services that accept streams from untrusted clients (for example using brpc::StreamAccept or brpc::StreamCreate with the built-in streaming protocol) are affected.
How to Fix:
(Choose one of the following options)
1. Upgrade bRPC to version 1.18.0, which fixes this issue.
2. Apply this patch: https://github.com/apache/brpc/pull/3489
Note:
The fix limits the accumulated payload of a fragmented stream message to max_body_size (default 64MB). When the accumulated payload exceeds the limit, the pending fragments are released and the stream is closed with error EMSGSIZE. After applying the fix, legitimate fragmented stream messages larger than max_body_size will be rejected; you can adjust the gflag max_body_size to change the limit. |
| Pydantic AI Harness before 0.59.0 fails to enforce UsageLimits.tool_calls_limit on nested tool calls dispatched from run_code in the CodeMode toolset. Users or prompt-injected content can make the sandboxed program invoke tools via run_nested_call() without check_before_tool_call, exceeding operator limits on costly or side-effecting tools. |
| Vulnerability in Oracle Java SE (component: Networking). The supported version that is affected is Oracle Java SE: 27. Difficult to exploit vulnerability allows unauthenticated attacker with network access via multiple protocols to compromise Oracle Java SE. Successful attacks of this vulnerability can result in unauthorized ability to cause a partial denial of service (partial DOS) of Oracle Java SE. Note: This vulnerability can be exploited by using APIs in the specified Component, e.g., through a web service which supplies data to the APIs. This vulnerability also applies to Java deployments, typically in clients running sandboxed Java Web Start applications or sandboxed Java applets, that load and run untrusted code (e.g., code that comes from the internet) and rely on the Java sandbox for security. CVSS 3.1 Base Score 3.7 (Availability impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:L). |
| Allocation of Resources Without Limits or Throttling vulnerability in Drupal Combined image style allows Excessive Allocation. This issue affects Combined image style versions: from 0.0.0 to 1.0.7. |
| In Splunk Enterprise versions below 10.4.4, 10.2.8, 10.0.11, and 9.4.16, a user who does not hold the "admin" or "power" Splunk roles could make the Splunk Enterprise instance unavailable to other users by sending a request to the Representational State Transfer (REST) API endpoint for lookup table files. The affected instance requires a restart to restore service.
The vulnerability is possible because the endpoint does not limit resource use when it processes response field selections.
For more information see Endpoints reference list (https://help.splunk.com/en/splunk-enterprise/rest-api-reference/10.4/introduction/endpoints-reference-list) and About configuring role-based user access (https://help.splunk.com/en/splunk-enterprise/administer/manage-users-and-security/10.4/manage-splunk-platform-users-and-roles/about-configuring-role-based-user-access) in the Splunk documentation. |
| In a BIND resolver, inbound DNS protocol messages with a particular structure will result in additional processing, with no limit imposed by BIND. A DNS message with a maliciously large number of such structures will cause BIND to consume disproportionate amounts of CPU time and memory during processing.
This issue affects BIND 9 versions 9.11.0 through 9.18.50, 9.20.0 through 9.20.29, 9.21.0 through 9.21.26, 9.11.3-S1 through 9.18.50-S1, and 9.20.9-S1 through 9.20.29-S1. |
| NLnet Labs Unbound up to and including version 1.26.1 limits compression-pointer traversal for each individual domain-name operation but has no aggregate inbound decompression-work budget. Many chained compression pointers in a message results in a lot of CPU usage following them. A malicious actor with recursive-query access and control of an ordinarily delegated authority that serves specially crafted replies can exploit the vulnerability and degrade Unbound's general performance. |
| Vulnerability in the Siebel CRM Integration product of Oracle Siebel CRM (component: EAI). Supported versions that are affected are 17.0-26.9. Easily exploitable vulnerability allows unauthenticated attacker with network access via HTTP to compromise Siebel CRM Integration. Successful attacks of this vulnerability can result in unauthorized ability to cause a hang or frequently repeatable crash (complete DOS) of Siebel CRM Integration. CVSS 3.1 Base Score 7.5 (Availability impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H). |
| stream-json is a micro-library of stream components for processing JSON and JSONC with a minimal memory footprint. From 2.0.0 until 3.7.0, FlexAssembler compiles string and regular-expression path filters into matchers that join the full key stack on every container start. Because the key stack grows with unbounded object or array nesting, a document nested to depth D causes D joins whose cost grows with the current depth, resulting in quadratic CPU consumption on the single-threaded event loop. The join occurs before the rule match decision, so the attacker does not need to know the target schema. Consumers using only function filters or no rules are not affected. A crafted nested document can stall all concurrent work in a process that feeds it to an affected FlexAssembler. This issue is fixed in 3.7.0, which adds a default maximum depth. |
| Django REST framework is a toolkit for building Web APIs. From 3.6.4 until 3.18.2, JSONParser.parse in rest_framework/parsers.py uses the client-supplied Content-Type charset value with codecs.getreader, which accepts byte-transform codecs such as bz2_codec rather than only text encodings. A remote unauthenticated client can therefore cause a compressed JSON request body to be expanded after Django has enforced DATA_UPLOAD_MAX_MEMORY_SIZE against only the wire bytes, allowing a very small request to consume substantial per-worker memory. The default parser set is sufficient, and any reachable endpoint that accepts a JSON body is affected. This issue is fixed in version 3.18.2. |
| Django REST framework is a toolkit for building Web APIs. Prior to 3.18.2, DefaultContentNegotiation.get_accept_list in rest_framework/negotiation.py accepts an unbounded number and aggregate size of Accept tokens, while select_renderer in that file repeatedly invokes _MediaType parsing from rest_framework/utils/mediatypes.py for each attacker-controlled token, every renderer, and forced 406 renegotiation. A remote unauthenticated client can send many distinct media types containing large quoted parameter values with numerous semicolons, causing Django's parameter parser and DRF's repeated parsing structure to consume disproportionate CPU. Requests to any reachable endpoint can pin synchronous workers for prolonged periods, and concurrent requests can make the service unavailable until the requests stop. This issue is fixed in version 3.18.2. |
| toml-node is a TOML parser for Node.js and the browser. Prior to 5.0.1, toml.parse() repeatedly slices and rejoins every path prefix in lib/compiler.js while processing long dotted keys, table headers, array-of-tables headers, and inline-table keys in assign(), deepRef(), and setNestedKey(). The resulting quadratic work can block the Node.js event loop for several seconds, and sufficiently long paths can terminate the process through memory exhaustion. The maxDepth option does not limit key-segment count and therefore does not mitigate this path. This issue is fixed in version 5.0.1. |
