Base de CVEs

PocketMine-MP versions before 4.12.5 contain a denial-of-service vulnerability in ModalFormResponsePacket processing that allows attackers to cause server resource exhaustion by sending large JSON payloads. Attackers can send numerous oversized modal form response packets to consume CPU time and prevent the server from processing legitimate connections.

Inefficient Algorithmic Complexity vulnerability in elixir-mint mint allows a remote HTTP server to exhaust CPU on the client host and cause a denial of service. parse_hex_prefix/2 in lib/mint/http1/parse.ex folds each hex digit of a chunked response's chunk-size field into an arbitrary-precision accumulator with acc * 16 + digit and imposes no limit on the digit count. Because the accumulator grows without bound, the multiplication is not constant time and one pass over N digits costs O(N squared). handle_data/2 prepends conn.buffer and re-parses from the start on every socket message, so a server that dribbles the digits out in small packets makes the client pay that cost repeatedly. A run of roughly 512,000 hex digits costs over ten seconds of CPU in a single pass, measured on stock defaults. The parser reaches this state after a valid status line and a complete, valid header section, so an intermediary inspecting only headers sees an ordinary 200 response. This issue affects mint: from 1.9.3 before 1.10.0.

stream-json is a micro-library of stream components for processing JSON and JSONC with a minimal memory footprint. Prior to 3.5.0, the path filters pick, ignore, filter, and replace in src/core/filters/filter-base.js recompute the full path string from the nesting stack for every checkable token. Because the stack length equals the current nesting depth and a checkable token is emitted at every level, a depth D document costs O(D²) rather than O(D) to process. The issue is triggered by nesting depth rather than byte volume, including the documented pick({filter: 'data'}) traversal-until-match path, so an application that sends untrusted JSON through a string or RegExp filter can block the Node.js event loop and cause denial of service with a small deeply nested document. The streamArray, streamObject, and streamValues streamers are not affected because they use the constant-time asm.depth getter. This issue is fixed in version 3.5.0.

HTTPX2 is a next generation HTTP client for Python. From 2.5.0 until 2.10.0, the HTTPX2 Server-Sent Events parser in src/httpx2/httpx2/_sse.py repeatedly copies and rescans buffered text in _SSELineDecoder.decode() when an attacker-controlled or compromised SSE endpoint splits one unterminated line across many response chunks. The behavior affects httpx2.Client.sse() and httpx2.AsyncClient.sse(), and the total processing work grows quadratically with the line length, allowing a crafted stream to consume excessive CPU and block a synchronous worker or asynchronous event loop. This issue is fixed in version 2.10.0.

sqlparse is a non-validating SQL parser module for Python. Prior to 0.6.0, sqlparse.format(sql, reindent=True) and sqlformat --reindent route attacker-controlled parenthesized tuple lists through ReindentFilter._get_offset() in sqlparse/filters/reindent.py, where _flatten_up_to_token() repeatedly rebuilds and joins the statement prefix. Thousands of offset calculations walk an expanding token tree, producing quadratic CPU consumption for inputs that remain below MAX_GROUPING_TOKENS and causing request delays, reduced throughput, or worker starvation. This issue is fixed in version 0.6.0.

pypdf is a free and open-source pure-python PDF library. Prior to 6.15.0, an attacker can craft a PDF that causes long runtimes when the pypdf/_utils.py function read_until_whitespace reads a stream containing a long run of bytes without whitespace. The function repeatedly performs immutable bytes concatenation in a one-byte loop, causing quadratic processing cost for the long non-whitespace input. This issue is fixed in version 6.15.0.

An algorithmic complexity flaw exists in libsoup's HTTP Range header processing that persists after the CVE-2025-32907 fix. CVE-2025-32907 addressed memory amplification when a client repeated the same range many times in a single Range header. Commit 9bb92f7a corrected merge correctness in soup_message_headers_get_ranges_internal() in libsoup/soup-message-headers.c, but the coalescing loop still removes merged ranges using g_array_remove_index() for each coalesced element. Because GArray is contiguous, each mid-array removal performs an O(N) memmove. When many identical satisfiable ranges are supplied (for example bytes=0-0 repeated thousands of times), the loop performs O(N²) work coalescing them into a single range. The vulnerable path is reachable server-side from handle_partial_get() in libsoup/server/http1/soup-server-message-io-http1.c when a SoupServer handler returns HTTP 200 with a non-empty body. No authentication is required. The number of ranges is bounded only by the maximum request header size (~100 KiB), allowing roughly 25,000 ranges per request. Reporter measurements on libsoup HEAD containing the CVE-2025-32907 fix show ~90 ms single-core CPU per such request at the wire maximum, blocking the server's event loop for that duration. This is a CPU exhaustion / availability issue only. No memory corruption or information disclosure occurs. Affected: libsoup versions containing the CVE-2025-32907 fix but not merge request !550. Fixed upstream: MR !550 merged 2026-08-20, replacing per-element removal with O(N) in-place compaction and rejecting Range headers requesting more than 200 ranges. Upstream report: https://gitlab.gnome.org/GNOME/libsoup/-/issues/538 Related: CVE-2025-32907

In Splunk Connect for Kafka versions below 2.2.7, an unauthenticated user who can reach the Kafka Connect Representational State Transfer (REST) API could configure timestamp extraction with a crafted regular expression and matching event data to block a Kafka Connect worker thread, stopping event delivery for the affected connector. The vulnerability is possible because timestamp extraction evaluates customer-supplied regular expressions without a time limit. For more information see Install Splunk Connect for Kafka (https://help.splunk.com/en/data-management/integrate-data-with-add-ons/splunk-connect-for-kafka/2.2/install/install-splunk-connect-for-kafka) and Data ingestion parameters for Splunk Connect for Kafka (https://help.splunk.com/en/data-management/integrate-data-with-add-ons/splunk-connect-for-kafka/2.2/overview/data-ingestion-parameters-for-splunk-connect-for-kafka) in the Splunk documentation.