| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| The Linux kernel NFSD implementation prior to versions 5.19.17 and 6.0.2 are vulnerable to buffer overflow. NFSD tracks the number of pages held by each NFSD thread by combining the receive and send buffers of a remote procedure call (RPC) into a single array of pages. A client can force the send buffer to shrink by sending an RPC message over TCP with garbage data added at the end of the message. The RPC message with garbage data is still correctly formed according to the specification and is passed forward to handlers. Vulnerable code in NFSD is not expecting the oversized request and writes beyond the allocated buffer space. CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H |
| Prometheus is an open-source monitoring system and time series database. Prior to versions 3.5.3 and 3.11.3, the remote read endpoint (/api/v1/read) does not validate the declared decoded length in a snappy-compressed request body before allocating memory. An unauthenticated attacker can send a small payload that causes a huge heap allocation per request. Under concurrent load this can exhaust available memory and crash the Prometheus process. This issue has been patched in versions 3.5.3 and 3.11.3. |
| A denial of service vulnerability was identified in GitHub Enterprise Server that allowed an unauthenticated attacker to cause service disruption by sending crafted requests with deeply nested JSON payloads to an unauthenticated API endpoint. The endpoint parsed user-controlled JSON request bodies without size or depth limits, causing excessive CPU and memory consumption. This vulnerability affected all versions of GitHub Enterprise Server prior to 3.21 and was fixed in versions 3.20.2, 3.19.6, 3.18.9, 3.17.15, and 3.16.18. This vulnerability was reported via the GitHub Bug Bounty program. |
| monetr is a budgeting application for recurring expenses. Prior to version 1.12.5, a server-side request forgery (SSRF) vulnerability in monetr's Lunch Flow integration allowed any authenticated user on a self-hosted instance to cause the monetr server to issue HTTP GET requests to arbitrary URLs supplied by the caller, with the response body from non-200 upstream responses reflected back in the API error message. This issue has been patched in version 1.12.5. |
| OpenTelemetry.Exporter.Zipkin is the .NET Zipkin exporter for OpenTelemetry. In versions 1.15.2 and earlier, the Zipkin exporter remote endpoint cache accepts unbounded key growth derived from span attributes. In high-cardinality scenarios, a process using Zipkin export for client or producer spans could experience avoidable memory growth under sustained unique remote endpoint values, increasing process memory usage over time and degrading availability. This issue is fixed in version 1.15.3, which introduces a bounded, thread-safe LRU cache for remote endpoints with a fixed maximum size. |
| The OpenFeature feature toggle evaluation endpoint reads unbounded values into memory, which can cause out-of-memory crashes. |
| Due to multiple time-of-check time-of-use race conditions in the resource count check and increment logic, as well as missing validations, users of the platform are able to exceed the allocation limits configured for their accounts/domains. This can be used by an attacker to degrade the infrastructure's resources and lead to denial of service conditions.
Users are recommended to upgrade to Apache CloudStack versions 4.20.3.0 or 4.22.0.1, or later, which fixes this issue. |
| ZEBRA is a Zcash node written entirely in Rust. Prior to version 4.4.0, a composite denial-of-service vulnerability in Zebra's block discovery pipeline allows an unauthenticated remote attacker to permanently halt all new block discovery on a targeted node. The attack exploits three independent weaknesses in the gossip, syncer, and download subsystems — all exercisable from a single TCP connection — to create a monotonically growing block deficit that never self-heals. This issue has been patched in version 4.4.0. |
| ZEBRA is a Zcash node written entirely in Rust. Prior to zebrad version 4.4.0, prior to zebra-chain version 7.0.0, and prior to zebra-network version 6.0.0, several inbound deserialization paths in Zebra allocated buffers sized against generic transport or block-size ceilings before the tighter protocol or consensus limits were enforced. An unauthenticated or post-handshake peer could therefore force the node to preallocate and parse for orders of magnitude more data than the protocol intended, across headers messages, equihash solutions in block headers, Sapling spend vectors in V5/V4 transactions, and coinbase script bytes in blocks. This issue has been patched in zebrad version 4.4.0, zebra-chain version 7.0.0, and zebra-network version 6.0.0. |
| CoreDNS is a DNS server that chains plugins. In versions prior to 1.14.3, the DNS-over-QUIC (DoQ) server can be driven into unbounded goroutine and memory growth by a remote client that opens many QUIC streams and sends only 1 byte per stream. When the worker pool is full, CoreDNS still spawns a goroutine per accepted stream to wait for a worker token. Additionally, active workers block indefinitely in io.ReadFull() with no per-stream read deadline, allowing an attacker to pin all workers by sending a single byte so the read blocks waiting for the second byte of the DoQ length prefix. This enables an unauthenticated remote attacker to cause memory exhaustion and OOM-kill. This issue has been fixed in version 1.14.3. No known workarounds exist. |
| A security vulnerability has been detected in GPAC up to 26.02.0. This affects the function sidx_box_read of the file src/isomedia/box_code_base.c. The manipulation leads to allocation of resources. The attack must be carried out locally. The exploit has been disclosed publicly and may be used. The identifier of the patch is 442e2299530138d8f874fd885c565ba98a6318ba. It is suggested to install a patch to address this issue. |
| Regex Denial of Service in youtube-regex npm package through version 1.0.5. |
| Improperly controlled modification of Dynamically-Determined object attributes, Allocation of resources without limits or throttling vulnerability in DivvyDrive Information Technologies Inc. DivvyDrive allows Excessive Allocation, Flooding.
This issue affects DivvyDrive: from 4.8.2.19 before 4.8.3.2. |
| Incus is a system container and virtual machine manager. Prior to version 7.0.0, user provided image and backup tarballs would be unpacked and YAML files parsed without any size restrictions. This was making it easy for an authenticated user to provide a crafted image or backup tarball that when parsed by Incus would lead to a very large YAML document being loaded into memory, potentially causing the entire server to run out of memory. This issue has been patched in version 7.0.0. |
| Incus is a system container and virtual machine manager. Prior to version 7.0.0, uploads of large amount of data by authenticated users can run the Incus server out of disk space, potentially taking down the host system. The impact here is limited for anyone using storage.images_volume and storage.backups_volume as those users will have large uploads be stored on those volumes rather than directly on the host filesystem. This is the default behavior on IncusOS. This issue has been patched in version 7.0.0. |
| A denial of service vulnerability could be triggered by sending specially crafted HTTP requests to server function endpoints, this could lead to server crashes, out-of-memory exceptions or excessive CPU usage; affecting the following packages: react-server-dom-webpack, react-server-dom-parcel, react-server-dom-turbopack (versions 19.0.0 through 19.0.5, 19.1.0 through 19.1.6, and 19.2.0 through 19.2.5). |
| An issue was discovered in MM in Samsung Mobile Processor, Wearable Processor, and Modem Exynos 980, 990, 850, 2100, 1280, 2200, 1330, 1380, 1480, 2400, 1580, W920, W930, W1000, Modem 5123, and Modem 5300. Incorrect handling of 5G NR NAS registration accept messages leads to a Denial of Service. |
| In the Linux kernel, the following vulnerability has been resolved:
crypto: af_alg - limit RX SG extraction by receive buffer budget
Make af_alg_get_rsgl() limit each RX scatterlist extraction to the
remaining receive buffer budget.
af_alg_get_rsgl() currently uses af_alg_readable() only as a gate
before extracting data into the RX scatterlist. Limit each extraction
to the remaining af_alg_rcvbuf(sk) budget so that receive-side
accounting matches the amount of data attached to the request.
If skcipher cannot obtain enough RX space for at least one chunk while
more data remains to be processed, reject the recvmsg call instead of
rounding the request length down to zero. |
| Allocation of Resources Without Limits or Throttling vulnerability in Apache HTTP Server's mod_md via OCSP response data.
This issue affects Apache HTTP Server: from 2.4.30 through 2.4.66.
Users are recommended to upgrade to version 2.4.67, which fixes the issue. |
| OOM Denial of Service via Unbounded Array Allocation in Apache OpenNLP AbstractModelReader
Versions Affected:
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. |
