Jeudi 30 juillet 2026

GeoVision Inc. GV-IP Device Utility

A DLL hijacking vulnerability exists in the GeoVision GV-IP Device Utility desktop application. The application loads one or more dynamic-link libraries (DLLs) from an unsafe search path, allowing a local attacker to place a malicious DLL in a location searched before the legitimate library location.

ninenines cowlib

Allocation of resources without limits vulnerability in ninenines cowlib allows an unauthenticated remote HTTP/2 or HTTP/3 peer to exhaust memory on the vulnerable server (or client) and cause a denial of service. The HPACK and QPACK prefixed-integer decoder cow_hpack_common:dec_big_int/3 in src/cow_hpack_common.hrl (invoked from cow_hpack:decode/2 in src/cow_hpack.erl and from cow_qpack:decode_field_section/3 in src/cow_qpack.erl) reads continuation octets until it sees one whose high bit is clear, evaluating Int + (Value bsl M) at each step with the shift M growing by seven per octet. No limit is enforced on the number of continuation octets, on the resulting bit width, or on the value; the decoder consumes whatever encoded length the peer supplies. Because Erlang integers are immutable, each intermediate Value bsl M and each accumulator update allocates a fresh bignum whose digit width grows linearly with the number of octets processed so far. Summed across the whole decode, the transient bignum digit materialization is on the order of the square of the encoded length. A single maximal HPACK indexed representation carried inside one HTTP/2 HEADERS plus one CONTINUATION frame at Cowboy's default max_frame_size_received can force hundreds of megabytes of transient allocation and garbage-collection churn before the resulting header-table index is rejected as invalid. Repeated or concurrent connections multiply the pressure and can drive the Erlang VM to memory exhaustion. Cowlib is the HTTP parser used by Cowboy, RabbitMQ's management plugin, and other Erlang and Elixir HTTP/2 and HTTP/3 servers and clients, so any exposed endpoint that accepts HPACK or QPACK from an untrusted peer is reachable. This issue affects cowlib: from 2.0.0 before 2.19.0.

mtrudel bandit

Inefficient Algorithmic Complexity vulnerability in mtrudel bandit allows unauthenticated remote denial of service via CPU exhaustion during WebSocket fragment reassembly. The size guard 'Elixir.Bandit.WebSocket.Connection':oversize_message?/2 called from handle_frame/3 in lib/bandit/websocket/connection.ex appends each non-final continuation frame to a left-nested iolist and then re-measures the entire accumulated buffer with IO.iodata_length/1 on every frame. Because the buffer grows by one element per frame and is fully re-traversed each time, reassembly work is quadratic (O(n^2)) in the number of continuation frames. The max_fragmented_message_size limit (default 8 MB) bounds total bytes but not frame count, and each frame can carry as little as one payload byte, so an attacker can send millions of tiny continuation frames using modest bandwidth to pin a CPU core for minutes to hours. Many concurrent connections can starve the whole server of CPU, denying service to legitimate users. The WebSocket read timeout does not help, because it is an idle timeout evaluated between reads and cannot preempt the synchronous reassembly work spent inside a single callback. This issue affects bandit: from 1.11.0 before 1.12.1.

misp

MISP installation scripts generated an Apache HTTP virtual-host configuration containing an incorrectly formatted HTTP-to-HTTPS redirect: Redirect permanent / https://misp.example Apache’s Redirect directive appends any portion of the requested path that follows the matched prefix to the configured destination URL. Because the destination did not end with /, attacker-controlled path content was appended directly to the hostname rather than to its URL path. For example, a request resembling: http://misp.example/@attacker.example/ could result in a redirect resembling: https://misp.example@attacker.example/ Under standard URL parsing, misp.example is interpreted as user information and attacker.example as the destination host. An unauthenticated remote attacker could therefore construct a URL hosted under the legitimate MISP domain that redirects users to an attacker-controlled website. The vulnerability could be used for phishing, credential collection, or potentially disclosing sensitive query-string information preserved during the redirect. Exploitation requires a user to follow the crafted HTTP URL. The fix adds the missing trailing slash to the redirect destination, ensuring that appended request data remains part of the path on the configured MISP host. Existing installationsExisting MISP installations should review their Apache HTTP virtual-host configuration and ensure that the HTTPS redirect destination ends with a trailing slash: Redirect permanent / https://misp.example/ After updating the configuration, validate it with apachectl configtest and reload or restart Apache for the change to take effect

moby BuildKit · Mobyproject buildkit

BuildKit custom frontends or clients using the raw low-level API can set git.checkoutbundle=true when checking out Git sources. If the Git source is malicious, this could lead to a crafted command invocation on the host.

smub Easy Digital Downloads – eCommerce Payments and Subscriptions made easy

The Easy Digital Downloads plugin for WordPress is vulnerable to Arbitrary File Upload in versions up to and including 3.6.9. This is due to insufficient file type validation in the edd_do_ajax_import_file_upload() function , which only checks the client-supplied $_FILES['edd-import-file']['type'] Content-Type header against an allow-list of CSV mime types, then uses raw move_uploaded_file() (bypassing wp_handle_upload()'s core MIME enforcement) to write the file under its original extension into the web-accessible wp-content/uploads/edd/exports/ directory. This makes it possible for authenticated attackers, with Shop Manager-level access and above, to upload arbitrary files on the affected site's server which may make remote code execution possible.

Apache Software Foundation Apache OpenNLP · Red Hat Data Grid 8 · Red Hat Fuse 7 · Red Hat JBoss Enterprise Application Platform Expansion Pack

OOM Denial of Service via Unbounded Array Allocation in Apache OpenNLP AbstractModelReader  Versions Affected:  before 1.9.5 before 2.5.9 before 3.0.0-M3  Description: The AbstractModelReader methods getOutcomes(), getOutcomePatterns(), and getPredicates() each read a 32-bit signed integer count field from a binary model stream and pass that value directly to an array allocation (new String[numOutcomes], new int[numOCTypes][], new String[NUM_PREDS]) without validating that the value is non-negative or within a reasonable bound. The count is therefore fully attacker-controlled when the model file originates from an untrusted source. A crafted .bin model file in which any of these count fields is set to Integer.MAX_VALUE (or any value large enough to exhaust the available heap) triggers an OutOfMemoryError at the array allocation itself, before the corresponding label or pattern data is consumed from the stream. The error occurs very early in deserialization: for a GIS model, getOutcomes() is reached after only the model-type string, the correction constant, and the correction parameter have been read; so the attacker pays no meaningful size cost to weaponize a payload, and a single small file can crash a JVM that loads it. Any code path that deserializes a .bin model is affected, including direct use of GenericModelReader and any higher-level component that delegates to it during model load. The practical impact is denial of service against processes that load model files from untrusted or semi-trusted origins.   Mitigation: * 2.x users should upgrade to 2.5.9. * 3.x users should upgrade to 3.0.0-M3. Note: The fix introduces an upper bound on each of the three count fields, checked before array allocation; counts that are negative or exceed the bound cause an IllegalArgumentException to be thrown and the read to fail fast with no large allocation. The default bound is 10,000,000, which is well above the entry counts of legitimate OpenNLP models but far below any value that would threaten heap exhaustion. Deployments that legitimately need to load models with more entries than the default can raise the limit at JVM startup by setting the OPENNLP_MAX_ENTRIES system property to the desired positive integer (e.g. -DOPENNLP_MAX_ENTRIES=50000000); invalid or non-positive values fall back to the default. Users who cannot upgrade immediately should treat all .bin model files as untrusted input unless their provenance is verified, and should avoid loading models supplied by end users or fetched from third-party repositories without integrity checks.