| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| pypdf is a free and open-source pure-python PDF library. Prior to 6.17.0, a crafted PDF can provide unusually large Roman page-label values that cause pypdf/_page_labels.py to generate excessively large numeral strings when an application retrieves document page labels, consuming large amounts of memory and potentially making the application unavailable. This issue is fixed in version 6.17.0. |
| pypdf is a free and open-source pure-python PDF library. Prior to 6.19.0, a crafted PDF with form field values can cause pypdf/generic/_appearance_stream.py appearance-stream generation to repeat invariant selection-data work inside a loop when an application updates fields with flattening enabled, resulting in excessive runtimes and application unavailability. This issue is fixed in version 6.19.0. |
| pypdf is a free and open-source pure-python PDF library. Prior to 6.12.2, an attacker who uses this vulnerability can craft a PDF which leads to large memory usage. This requires extracting the text of a page which contains a form XObject with self-references. This vulnerability is fixed in 6.12.2. |
| pypdf is a free and open-source pure-python PDF library. Prior to version 6.1.3, an attacker who uses this vulnerability can craft a PDF which leads to large memory usage. This requires parsing the content stream of a page using the LZWDecode filter. This has been fixed in pypdf version 6.1.3. |
| pypdf is a free and open-source pure-python PDF library. Prior to 6.7.3, an attacker who uses this vulnerability can craft a PDF which leads to the RAM being exhausted. This requires accessing the `xfa` property of a reader or writer and the corresponding stream being compressed using `/FlateDecode`. This has been fixed in pypdf 6.7.3. As a workaround, apply the patch manually. |
| pypdf is a free and open-source pure-python PDF library. Prior to 6.21.3, pypdf can consume excessive memory when strict mode loads a crafted invalid PDF dictionary. Nested parsing failures add repeated exception text to newly raised errors, allowing a small malformed dictionary to amplify into very large error messages. This issue is fixed in version 6.21.3. |
| pypdf is a free and open-source pure-python PDF library. Prior to 6.15.0, a crafted PDF can cause large memory consumption when pypdf/_cmap.py function parse_bfrange parses unusually large source-code or destination-string tokens in a font /ToUnicode CMap during text extraction. This issue is fixed in 6.15.0. |
| 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. |
| pypdf is a free and open-source pure-python PDF library. Prior to version 6.0.0, an attacker can craft a PDF which leads to the RAM being exhausted. This requires just reading the file if a series of FlateDecode filters is used on a malicious cross-reference stream. Other content streams are affected on explicit access. This issue has been fixed in 6.0.0. If an update is not possible, a workaround involves including the fixed code from pypdf.filters.decompress into the existing filters file. |
| pypdf is a free and open-source pure-python PDF library. Prior to 6.16.1, an attacker can craft a PDF that causes pypdf/_doc_common.py _get_outline to consume long runtimes and large amounts of memory when retrieving document outlines with large numbers of entries or deeply nested reused paths because the traversal lacked global entry-count and nesting-depth limits. This issue is fixed in version 6.16.1. |
| pypdf is a free and open-source pure-python PDF library. Prior to 6.21.3, pypdf can consume excessive memory while opening a crafted PDF containing cross-reference streams that declare very large entry counts. Highly compressible streams and /Prev chains can multiply the number of parsed entries across a document, while negative /Index counts can bypass the existing per-stream clamp. This issue is fixed in version 6.21.3. |
| `jwkFields.CheckConsistency` detected duplicate `key_ops` entries with a pairwise nested loop, making validation cost grow with the **square** of the entry count. Because the check runs on every JWK and JWKS unmarshal, a modestly sized but hostile JWKS document could occupy a CPU core for many seconds. |
| Semantic MediaWiki from 3.0.0 before 7.3.1 contains an algorithmic complexity vulnerability in TermParser::normalize_compact_form() that allows unauthenticated attackers to cause quadratic CPU consumption. Attackers can submit long search terms with many distinct in:(...) or has:(...) expressions to Special:Search to exhaust PHP search workers when the extended search engine is enabled. |
| Tornado is a Python web framework and asynchronous networking library. Prior to 6.6, Tornado's HTTP header parser accepts obsolete line folding and repeatedly concatenates short continuation lines, causing quadratic CPU consumption on the IOLoop thread. An unauthenticated client can submit a header block containing many obsolete line-folding continuations before application code runs, blocking other connections served by the process. The cost grows with max_header_size, and only availability is affected. This issue is fixed in version 6.6. |
| Pistache is a high-performance REST toolkit written in C++. From 0.0.5 through 0.4.26, `SegmentTreeNode::sanitizeResource()` in `src/server/router.cc` uses `std::regex_replace()` with the `//+` expression to collapse consecutive slashes. With libstdc++, a path containing tens of thousands of consecutive slash characters can recurse deeply enough to exhaust the stack and crash the process. `Router::route()`, `Router::addRoute()` and `Router::removeRoute()` reach this normalization. Unauthenticated remote exploitation is conditional on a deployment allowing sufficiently long request paths, such as by increasing `Endpoint::Options::maxRequestSize()` above the default 4096-byte limit or forwarding long paths through a front end. Direct router API use is also affected when attacker-controlled route or resource strings reach the normalizer. Native builds reproduced a crash around 24,000 consecutive slashes, although the exact threshold depends on the stack and standard library; the demonstrated failure is stack exhaustion rather than uninitialized-memory use. A source fix replaces recursive regular-expression normalization with an iterative pass, but no containing repository release was verified as of time of publication. |
| Daptin is a GraphQL/JSON-API headless CMS. Prior to 0.12.26, the global `LanguageMiddleware` in `server/language.go` passes the raw `Accept-Language` header from `LanguageMiddlewareFunc` through `GetLanguagePreference` to `golang.org/x/text/language.ParseAcceptLanguage` without a size or shape filter. The guard for CVE-2022-32149 counts hyphens but not underscores, even though the internal parser normalizes underscores to hyphens. Long lists of underscore-separated language subtags longer than eight characters bypass the guard. These subtags fail the `len <= 8` length check in `golang.org/x/text/internal/language/parse.go`, causing repeated `gobble` operations to move the entire remaining buffer through `runtime.memmove`. With many invalid subtags, this repeated buffer movement takes quadratic work. Unauthenticated requests to the public landing page or `/api` can consume disproportionate CPU, with a 256 KiB header already causing seconds of processing and larger input permitted by the default 1 MiB header ceiling. `resource.CheckErr` also logs the entire invalid header, amplifying cost, while normal successful HTTP responses can conceal the denial-of-service traffic. Repeated requests can saturate server CPU. This issue is fixed in version 0.12.26. |
| In Splunk Enterprise versions below 10.4.4, 10.2.8, 10.0.11, and 9.4.16, an unauthenticated user could send a single crafted request to the Splunk Web JavaScript logger endpoint and cause Splunk Web to stop responding to requests. Splunk Web remains unavailable until Splunk Enterprise is restarted.
The vulnerability is possible because Splunk Web does not limit request-header parsing before processing the request. |
| In Splunk Enterprise versions below 10.4.4, 10.2.8, and 10.0.11, an unauthenticated user could send repeated requests to the Edge Processor package download endpoint in the Splunk Pipeline Builders app. The requests could cause the Splunk daemon to consume excessive system resources and potentially stop responding.
The vulnerability is possible because the endpoint accepts package download requests without authentication and reads and base64-encodes the complete package in memory for each request.
After upgrading, prepare all existing Edge Processor instances, turn on authentication for Edge Processor package downloads, and restart Splunk Enterprise. For more information, see Enable authentication for Edge Processor package downloads (https://help.splunk.com/en/data-management/process-data-at-the-edge/use-edge-processors-for-splunk-enterprise/10.4/administer-edge-processors/enable-authentication-for-edge-processor-package-downloads). |
| 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. |
| Uncontrolled Resource Consumption vulnerability in Apache IoTDB JDBC driver.
This issue affects Apache IoTDB JDBC driver: from 1.3.3 before 2.0.11.
Users are recommended to upgrade to version 2.0.11, which fixes the issue. |
