CVE Database

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.

Previously, resolving relative paths containing parent directory ('..') segments performed string conversions and buffer rewrites on each step, resulting in quadratic time complexity and high memory allocation overhead. Now, path resolution operates on a byte buffer using index-based backtracking for '..' segments, eliminating the quadratic time complexity and significantly reducing memory allocations.

A flaw in Elasticsearch allows a low-privileged authenticated user who can index documents to submit a single small document containing a crafted user-supplied input. Processing one such document occupies a worker thread from a bounded pool for a disproportionate amount of time, degrading the availability of indexing operations on the affected node.

Inefficient Algorithmic Complexity (CWE-407) in Kibana can lead to denial of service via Input Data Manipulation (CAPEC-153). A specially crafted, deeply nested expression submitted to a Kibana TSVB visualization is evaluated with a worst-case cost that grows disproportionately with the size of the input. Because the evaluation runs synchronously, a single request consumes the Kibana request-processing thread indefinitely, and Kibana stops responding to all further requests until the service is restarted.

Hono is a Web application framework that provides support for any JavaScript runtime. From 4.12.0 to 4.12.33, the languageDetector middleware is vulnerable to algorithmic complexity denial of service when processing a crafted language tag containing a large number of hyphen separated subtags. To implement progressive language tag truncation, normalizeLanguage() repeatedly calls parts.slice(0, i).join('-') for every possible prefix, so the total amount of string processing grows quadratically with the number of subtags. Language values may come from a query parameter, cookie, Accept-Language header, or URL path, depending on the detector configuration, and the default detector order enables query string, cookie, and header detection, so applications using languageDetector() may expose this processing to unauthenticated requests. An attacker may repeatedly send requests containing long, hyphen separated language tags, causing excessive CPU consumption and preventing unrelated requests from being processed. This issue is fixed in version 4.12.34.

Next.js is a React framework for building full-stack web applications. In versions 15.5.0 through 15.5.20 and 16.0.0 through 16.2.10, when self-hosting Next.js with the default image loader, the Image Optimization API can optimize remotely hosted images if configured (not enabled by default). If those images contain malicious content, they can cause CPU exhaustion in /_next/image endpoints.Only config.images.remotePatterns is affected, and just the patterns in that array, whereas config.images.unoptimized: true, config.images.loader: 'custom', and Vercel are not impacted. This issue has been fixed in versions 15.5.21 and 16.2.11.