vLLM is an inference and serving engine for large language models. From 0.22.0 to 0.23.0, the /v1/audio/transcriptions and /v1/audio/translations routes call request.file.read() to fully materialize an uploaded audio file into memory before vLLM checks the documented VLLM_MAX_AUDIO_CLIP_FILESIZE_MB compressed upload size limit (default 25 MB) later in the speech-to-text preprocessing step, so an API caller who can reach those routes can submit an oversized multipart upload and cause vLLM to allocate memory proportional to the uploaded file size before the request is rejected as too large, creating memory pressure or terminating the process depending on deployment resource limits. This issue is fixed in version 0.24.0.

vLLM is an inference and serving engine for large language models (LLMs). From 0.7.0 to before 0.19.0, the VideoMediaIO.load_base64() method at vllm/multimodal/media/video.py splits video/jpeg data URLs by comma to extract individual JPEG frames, but does not enforce a frame count limit. The num_frames parameter (default: 32), which is enforced by the load_bytes() code path, is completely bypassed in the video/jpeg base64 path. An attacker can send a single API request containing thousands of comma-separated base64-encoded JPEG frames, causing the server to decode all frames into memory and crash with OOM. This vulnerability is fixed in 0.19.0.

vLLM is an inference and serving engine for large language models (LLMs). From 0.1.0 to before 0.19.0, a Denial of Service vulnerability exists in the vLLM OpenAI-compatible API server. Due to the lack of an upper bound validation on the n parameter in the ChatCompletionRequest and CompletionRequest Pydantic models, an unauthenticated attacker can send a single HTTP request with an astronomically large n value. This completely blocks the Python asyncio event loop and causes immediate Out-Of-Memory crashes by allocating millions of request object copies in the heap before the request even reaches the scheduling queue. This vulnerability is fixed in 0.19.0.

vLLM is an inference and serving engine for large language models (LLMs). In versions from 0.6.4 to before 0.12.0, users can crash the vLLM engine serving multimodal models that use the Idefics3 vision model implementation by sending a specially crafted 1x1 pixel image. This causes a tensor dimension mismatch that results in an unhandled runtime error, leading to complete server termination. This issue has been patched in version 0.12.0.

vLLM is an inference and serving engine for large language models (LLMs). From version 0.5.5 to before 0.11.1, the /v1/chat/completions and /tokenize endpoints allow a chat_template_kwargs request parameter that is used in the code before it is properly validated against the chat template. With the right chat_template_kwargs parameters, it is possible to block processing of the API server for long periods of time, delaying all other requests. This issue has been patched in version 0.11.1.

vLLM, an inference and serving engine for large language models (LLMs), has a Regular Expression Denial of Service (ReDoS) vulnerability in the file `vllm/entrypoints/openai/tool_parsers/pythonic_tool_parser.py` of versions 0.6.4 up to but excluding 0.9.0. The root cause is the use of a highly complex and nested regular expression for tool call detection, which can be exploited by an attacker to cause severe performance degradation or make the service unavailable. The pattern contains multiple nested quantifiers, optional groups, and inner repetitions which make it vulnerable to catastrophic backtracking. Version 0.9.0 contains a patch for the issue.

vLLM is an inference and serving engine for large language models (LLMs). In version 0.8.0 up to but excluding 0.9.0, the vLLM backend used with the /v1/chat/completions OpenAPI endpoint fails to validate unexpected or malformed input in the "pattern" and "type" fields when the tools functionality is invoked. These inputs are not validated before being compiled or parsed, causing a crash of the inference worker with a single request. The worker will remain down until it is restarted. Version 0.9.0 fixes the issue.

vLLM is a library for LLM inference and serving. From 0.12.0 to before 0.24.0, sending a pure prompt embeds payload in a /v1/completions request with a model using M-RoPE causes EngineCore to fail an assertion and fatally crash, shutting down the entire server application. Any remote user who is authorized to make a /v1/completions request can make such a request and induce a crash. This issue is fixed in version 0.24.0.

vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.23.1rc0, vLLM's /v1/audio/transcriptions endpoint limits compressed upload size but not decoded PCM output. A 25MB OPUS file expands to ~14.9GB of float32 PCM at decode time. This vulnerability is fixed in 0.23.1rc0.

vLLM is an inference and serving engine for large language models (LLMs). From 0.18.0 to before 0.20.0, the extract_hidden_states speculative decoding proposer in vLLM returns a tensor with an incorrect shape after the first decode step, causing a RuntimeError that crashes the EngineCore process. The crash is triggered when any request in the batch uses sampling penalty parameters (repetition_penalty, frequency_penalty, or presence_penalty). A single request with a penalty parameter (e.g., "repetition_penalty": 1.1) is sufficient to crash the server. This vulnerability is fixed in 0.20.0.

vLLM is an inference and serving engine for large language models (LLMs). From 0.6.1 to before 0.20.0, there is a a Token Injection vulnerability in vLLM’s multimodal processing. Unauthenticated, text-only prompts that spell special tokens are interpreted as control. Image and video placeholder sequences supplied without matching data cause vLLM to index into empty grids during input-position computation, raising an unhandled IndexError and terminating the worker or degrading availability. Multimodal paths that rely on image_grid_thw/video_grid_thw are affected. This vulnerability is fixed in 0.20.0.

vLLM is an inference and serving engine for large language models (LLMs). Version 0.8.0 up to but excluding 0.9.0 have a Denial of Service (ReDoS) that causes the vLLM server to crash if an invalid regex was provided while using structured output. This vulnerability is similar to GHSA-6qc9-v4r8-22xg/CVE-2025-48942, but for regex instead of a JSON schema. Version 0.9.0 fixes the issue.

vLLM is an inference and serving engine for large language models (LLMs). From version 0.5.5 to before 0.11.1, users can crash the vLLM engine serving multimodal models by passing multimodal embedding inputs with correct ndim but incorrect shape (e.g. hidden dimension is wrong), regardless of whether the model is intended to support such inputs (as defined in the Supported Models page). This issue has been patched in version 0.11.1.

vLLM is an inference and serving engine for large language models (LLMs). In versions 0.8.0 up to but excluding 0.9.0, hitting the /v1/completions API with a invalid json_schema as a Guided Param kills the vllm server. This vulnerability is similar GHSA-9hcf-v7m4-6m2j/CVE-2025-48943, but for regex instead of a JSON schema. Version 0.9.0 fixes the issue.

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Versions starting from 0.8.0 and prior to 0.8.5 are affected by a critical performance vulnerability in the input preprocessing logic of the multimodal tokenizer. The code dynamically replaces placeholder tokens (e.g., <|audio_|>, <|image_|>) with repeated tokens based on precomputed lengths. Due to ​​inefficient list concatenation operations​​, the algorithm exhibits ​​quadratic time complexity (O(n²))​​, allowing malicious actors to trigger resource exhaustion via specially crafted inputs. This issue has been patched in version 0.8.5.

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Versions starting from 0.5.2 and prior to 0.8.5 are vulnerable to denial of service and data exposure via ZeroMQ on multi-node vLLM deployment. In a multi-node vLLM deployment, vLLM uses ZeroMQ for some multi-node communication purposes. The primary vLLM host opens an XPUB ZeroMQ socket and binds it to ALL interfaces. While the socket is always opened for a multi-node deployment, it is only used when doing tensor parallelism across multiple hosts. Any client with network access to this host can connect to this XPUB socket unless its port is blocked by a firewall. Once connected, these arbitrary clients will receive all of the same data broadcasted to all of the secondary vLLM hosts. This data is internal vLLM state information that is not useful to an attacker. By potentially connecting to this socket many times and not reading data published to them, an attacker can also cause a denial of service by slowing down or potentially blocking the publisher. This issue has been patched in version 0.8.5.

vLLM versions 0.8.0 and later are vulnerable to an Out-of-Memory (OOM) Denial of Service (DoS) attack due to unbounded frame count processing in the `VideoMediaIO.load_base64()` method. When processing `video/jpeg` data URLs, the method splits the base64 data string on commas to extract individual JPEG frames without enforcing a frame count limit. An attacker can exploit this by crafting a single API request containing thousands of comma-separated base64-encoded JPEG frames in a data URL, causing the server to decode all frames into memory and crash due to excessive memory consumption. This vulnerability is reachable via the OpenAI-compatible chat completions API and does not require authentication.

An allocation of resources without limits or throttling vulnerability has been reported to affect Qsync Central. If a remote attacker gains a user account, they can then exploit the vulnerability to prevent other systems, applications, or processes from accessing the same type of resource. We have already fixed the vulnerability in the following version: Qsync Central 5.0.0.4 ( 2026/01/20 ) and later

Marinus Pfund, member of the AXIS OS Bug Bounty Program, has found the VAPIX API alwaysmulti.cgi was vulnerable for file globbing which could lead to resource exhaustion of the Axis device. Axis has released patched AXIS OS versions for the highlighted flaw. Please refer to the Axis security advisory for more information and solution.

A vulnerability was found in wallabag 2.5.4. It has been declared as problematic. Affected by this vulnerability is an unknown functionality of the file /config of the component Profile Config. The manipulation of the argument Name leads to allocation of resources. The exploit has been disclosed to the public and may be used. The associated identifier of this vulnerability is VDB-233359. NOTE: The vendor was contacted early about this disclosure but did not respond in any way.

On BIG-IP Next CNF, BIG-IP Next SPK, and BIG-IP Next for Kubernetes systems, repeated undisclosed API calls can cause the Traffic Management Microkernel (TMM) to terminate.  Note: Software versions which have reached End of Technical Support (EoTS) are not evaluated.

An allocation of resources without limits or throttling vulnerability has been reported to affect File Station 5. If a remote attacker gains a user account, they can then exploit the vulnerability to prevent other systems, applications, or processes from accessing the same type of resource. We have already fixed the vulnerability in the following version: File Station 5 5.5.6.5018 and later

An allocation of resources without limits or throttling vulnerability has been reported to affect File Station 5. If a remote attacker gains a user account, they can then exploit the vulnerability to prevent other systems, applications, or processes from accessing the same type of resource. We have already fixed the vulnerability in the following version: File Station 5 5.5.6.5018 and later

Allocation of Resources Without Limits or Throttling vulnerability in Apache Software Foundation Apache Struts.This issue affects Apache Struts: through 2.5.30, through 6.1.2. Upgrade to Struts 2.5.31 or 6.1.2.1 or greater.

An allocation of resources without limits or throttling vulnerability in the Schweitzer Engineering Laboratories SEL-451 could allow a remote authenticated attacker to make the system unavailable for an indefinite amount of time. See product Instruction Manual Appendix A dated 20230830 for more details.

Wyse Management Suite versions prior to 4.0 contain a denial-of-service vulnerability. An authenticated malicious user can flood the configured SMTP server with numerous requests in order to deny access to the system.

MeterSphere is an open source continuous testing platform. Version 2.9.1 and prior are vulnerable to denial of service. ​The `checkUserPassword` method is used to check whether the password provided by the user matches the password saved in the database, and the `CodingUtil.md5` method is used to encrypt the original password with MD5 to ensure that the password will not be saved in plain text when it is stored. If a user submits a very long password when logging in, the system will be forced to execute the long password MD5 encryption process, causing the server CPU and memory to be exhausted, thereby causing a denial of service attack on the server. This issue is fixed in version 2.10.0-lts with a maximum password length.

An issue has been discovered in GitLab CE/EE affecting all versions before 17.10.7, 17.11 before 17.11.3, and 18.0 before 18.0.1. A lack of proper validation in GitLab could allow an authenticated user to cause a denial of service condition.

An uncontrolled resource consumption vulnerability has been reported to affect Qsync Central. If a remote attacker gains a user account, they can then exploit the vulnerability to launch a denial-of-service (DoS) attack. We have already fixed the vulnerability in the following version: Qsync Central 5.0.0.2 ( 2025/07/31 ) and later

Allocation of Resources Without Limits or Throttling vulnerability in Kron Technologies Kron PAM allows HTTP DoS. This issue affects Kron PAM: before 3.7.

IBM Db2 for Linux, UNIX and Windows (includes Db2 Connect Server) 10.5, 11.1, and 11.5 is vulnerable to denial of service with a specially crafted query.

Allocation of resources without limits or throttling in Windows DirectX allows an authorized attacker to deny service over a network.

Envoy is an open source edge and service proxy designed for cloud-native applications. Prior to versions 1.26.0, 1.25.3, 1.24.4, 1.23.6, and 1.22.9, the Lua filter is vulnerable to denial of service. Attackers can send large request bodies for routes that have Lua filter enabled and trigger crashes. As of versions versions 1.26.0, 1.25.3, 1.24.4, 1.23.6, and 1.22.9, Envoy no longer invokes the Lua coroutine if the filter has been reset. As a workaround for those whose Lua filter is buffering all requests/ responses, mitigate by using the buffer filter to avoid triggering the local reply in the Lua filter.

Allocation of Resources Without Limits or Throttling in GitHub repository vriteio/vrite prior to 0.3.0.

ReportPortal is an AI-powered test automation platform. Prior to version 5.10.0 of the `com.epam.reportportal:service-api` module, corresponding to ReportPortal version 23.2, the ReportPortal database becomes unstable and reporting almost fully stops except for small launches with approximately 1 test inside when the test_item.path field is exceeded the allowable `ltree` field type indexing limit (path length>=120, approximately recursive nesting of the nested steps). REINDEX INDEX path_gist_idx and path_idx aren't helped. The problem was fixed in `com.epam.reportportal:service-api` module version 5.10.0 (product release 23.2), where the maximum number of nested elements were programmatically limited. A workaround is available. After deletion of the data with long paths, and reindexing both indexes (path_gist_idx and path_idx), the database becomes stable and ReportPortal works properly.

Discourse is an open source platform for community discussion. Versions prior to 3.1.0.beta1 (beta) (tests-passed) are vulnerable to Allocation of Resources Without Limits. Users can create chat drafts of an unlimited length, which can cause a denial of service by generating an excessive load on the server. Additionally, an unlimited number of drafts were loaded when loading the user. This issue has been patched in version 2.1.0.beta1 (beta) and (tests-passed). Users should upgrade to the latest version where a limit has been introduced. There are no workarounds available.

Nextcloud Server is a self hosted personal cloud system, and the Nextcloud Groupfolders app provides admin-configured folders shared by everyone in a group or team. In Nextcloud Server prior to 30.0.2, 29.0.9, and 28.0.1, Nextcloud Enterprise Server prior to 30.0.2 and 29.0.9, and Nextcloud Groupfolders app prior to 18.0.3, 17.0.5, and 16.0.11, the absence of quota checking on attachments allowed logged-in users to upload files exceeding the group folder quota. Nextcloud Server versions 30.0.2 and 29.0.9, Nextcloud Enterprise Server versions 30.0.2, 29.0.9, or 28.0.12, and Nextcloud Groupfolders app 18.0.3, 17.0.5, and 16.0.11 fix the issue. No known workarounds are available.

A flaw was found in the `/v2/_catalog` endpoint in distribution/distribution, which accepts a parameter to control the maximum number of records returned (query string: `n`). This vulnerability allows a malicious user to submit an unreasonably large value for `n,` causing the allocation of a massive string array, possibly causing a denial of service through excessive use of memory.

The webinstaller is a Golang web server executable that enables the generation of an Auvesy image agent. Resource consumption can be achieved by generating large amounts of installations, which are then saved without limitation in the temp folder of the webinstaller executable.

A vulnerability in a logging API in Cisco Firepower Management Center (FMC) Software could allow an unauthenticated, remote attacker to cause the device to become unresponsive or trigger an unexpected reload. This vulnerability could also allow an attacker with valid user credentials, but not Administrator privileges, to view a system log file that they would not normally have access to. This vulnerability is due to a lack of rate-limiting of requests that are sent to a specific API that is related to an FMC log. An attacker could exploit this vulnerability by sending a high rate of HTTP requests to the API. A successful exploit could allow the attacker to cause a denial of service (DoS) condition due to the FMC CPU spiking to 100 percent utilization or to the device reloading. CPU utilization would return to normal if the attack traffic was stopped before an unexpected reload was triggered.

An allocation of resources without limits or throttling vulnerability has been reported to affect several QNAP operating system versions. If a remote attacker gains a user account, they can then exploit the vulnerability to prevent other systems, applications, or processes from accessing the same type of resource. We have already fixed the vulnerability in the following versions: QTS 5.2.6.3195 build 20250715 and later QuTS hero h5.2.6.3195 build 20250715 and later

Using a densely populated chars mask and a large input string in the MongoDB aggregation operators $trim, $ltrim, and $rtrim, an authenticated user with aggregation permissions can pin CPU utilization at 100% for an extended period of time. This issue impacts MongoDB Server v7.0 versions prior to 7.0.34, v8.0 versions prior to 8.0.23, v8.2 versions prior to 8.2.9 and v8.3 versions prior to 8.3.2.

GitLab has remediated an issue in GitLab CE/EE affecting all versions from 8.3 before 18.9.7, 18.10 before 18.10.6, and 18.11 before 18.11.3 that could have allowed an authenticated user to cause denial of service through excessive memory consumption due to improper input validation.

Liferay Portal 7.4.0 through 7.4.3.132, and Liferay DXP 2025.Q1.0 through 2025.Q1.1, 2024.Q4.0 through 2024.Q4.7, 2024.Q3.1 through 2024.Q3.13, 2024.Q2.0 through 2024.Q2.13, 2024.Q1.1 through 2024.Q1.14 and 7.4 GA through update 92 allow users to upload an unlimited amount of files through the forms, the files are stored in the document_library allowing an attacker to cause a potential DDoS.

Liferay Portal 7.4.0 through 7.4.3.132, and Liferay DXP 2025.Q1.0 through 2025.Q1.4, 2024.Q4.0 through 2024.Q4.7, 2024.Q3.1 through 2024.Q3.13, 2024.Q2.0 through 2024.Q2.13, 2024.Q1.1 through 2024.Q1.15 and 7.4 GA through update 92 allow users to upload an unlimited amount of files through the object entries attachment fields, the files are stored in the document_library allowing an attacker to cause a potential DDoS.

A denial of service issue was discovered in GitLab CE/EE affecting all versions starting from 13.2.4 before 15.10.8, all versions starting from 15.11 before 15.11.7, all versions starting from 16.0 before 16.0.2 which allows an attacker to cause high resource consumption using malicious test report artifacts.

User-controlled operations could have allowed Denial of Service in M-Files Server before 23.4.12528.1 due to uncontrolled memory consumption.

A low privileged remote attacker can run the webshell with an empty command containing whitespace. The server will then block until it receives more data, resulting in a DoS condition of the websserver.

An issue has been discovered in GitLab CE/EE affecting all versions from 10.7 before 17.11.5, 18.0 before 18.0.3, and 18.1 before 18.1.1 that could have allowed authenticated attackers to create a DoS condition by sending crafted GraphQL requests.

OpenClaw versions before 2026.6.1 contain a denial of service vulnerability where remote media URLs can trigger slow-read attacks that exhaust gateway worker resources. Attackers with access to configured input paths can supply remote media URLs that consume gateway resources and reduce availability.