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1175 advisories across 32 monitored vendors.
Authentication Bypass via Host Header Injection. Red Hat rates this important (CVSS 8.1). Weakness: CWE-290.
http-proxy-middleware is node.js http-proxy middleware. From 3.0.4 until 3.0.7 and 4.1.1, fixRequestBody() is the library's documented helper for re-emitting a request body that was already consumed by a body parser. When the outgoing Content-Type is multipart/form-data, it rebuilds the body with handlerFormDataBodyData(), which interpolates each req.body key and value directly into the multipart wire format without neutralizing CR/LF. A \r\n inside a value (or key) lets an attacker close the current part and inject an entirely new form part. Because the proxy's own body parser saw a single opaque value, any gateway-side policy or validation performed on req.body is evaluated against a different set of fields than the upstream backend ultimately parses a request/parameter desynchronization across the trust boundary. This vulnerability is fixed in 3.0.7 and 4.1.1. A remote attacker could exploit a vulnerability in the fixRequestBody() function, which is used to re-emit a request body. By injecting carriage return and line feed characters (\r\n) into a request body key or value, an attacker can bypass security policies and validation performed by the proxy. This desynchronization between the proxy and the backend server can lead to a compromise of data integrity. Other Red Hat AI products are not affected or do not expose the vulnerable code path in normal operation.
Information Disclosure via Path Traversal in `nltk.data.load()`. Red Hat rates this important (CVSS 7.5). Weakness: CWE-22.
Arbitrary code execution via prototype pollution of filename option. Red Hat rates this important (CVSS 8.1). Weakness: CWE-915.
request.form() limits silently ignored for application/x-www-form-urlencoded enable DoS. Red Hat rates this important (CVSS 7.5). Weakness: CWE-770. Red Hat lists fixing advisory RHSA-2026:36006 with package rhaiis/vllm-cuda-rhel9:1782951012, jaeger-main-2.19.0-1.hum1, rhaiis/vllm-rocm-rhel9:1782951244.
protobufjs compiles protobuf definitions into JavaScript (JS) functions. Prior to 7.6.1 and 8.4.1, protobufjs could recurse without a depth limit while converting decoded messages to plain objects or JSON. This affected generated toObject() conversion and the custom google.protobuf.Any JSON conversion path. A crafted protobuf binary payload containing deeply nested Any values could cause the JavaScript call stack to be exhausted during conversion to JSON. This vulnerability is fixed in 7.6.1 and 8.4.1. A flaw was found in protobufjs. This uncontrolled recursion could exhaust the JavaScript call stack during conversion to JSON, leading to a Denial of Service (DoS). Red Hat rates this issue as having Low impact for Red Hat Enterprise Linux AI bootc images. Although protobufjs is present as a transitive dependency, the vulnerable parsing path is not exercised in normal product operation. Red Hat severity: Moderate — CVSS 7.5 (CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H). Weakness: CWE-606. Affected Red Hat products: Red Hat Enterprise Linux AI (RHEL AI) 3; Red Hat OpenShift AI (RHOAI).
@angular/platform-server: Angular: SSRF via Hostname Hijacking in @angular/platform-server. Red Hat rates this important.
@angular/platform-server: domino: Angular Platform Server: Cross-Site Scripting via unescaped `</noscript>` tags in dynamic content. Red Hat rates this important (CVSS 8.1). Weakness: CWE-79.
A flaw was found in OpenSSH. A local unprivileged attacker on a Linux client host can hijack client-side X11 forwarding connections. This is possible by pre-binding the preferred abstract X socket name when X11 forwarding is enabled and a local UNIX-domain X socket is used. A successful attack can compromise the confidentiality of forwarded X11 traffic, including sensitive window contents and input, and may allow some manipulation of the forwarded session. This is a Moderate severity flaw. The OpenSSH client in Red Hat Enterprise Linux is vulnerable to a local man-in-the-middle attack on X11 forwarding connections. Exploitation requires an attacker to have local unprivileged access on the client system and for X11 forwarding to be explicitly enabled and in use, which is not a default configuration. This vulnerability doesn't affect the upstream OpenSSH versions and is restricted to the versions as shipped with Red Hat Enterprise Linux. Red Hat severity: Moderate — CVSS 5 (CVSS:3.1/AV:L/AC:H/PR:L/UI:R/S:U/C:H/I:L/A:N). Weakness: CWE-923. Affected Red Hat products: Red Hat Hardened Images; Red Hat Enterprise Linux 10; Red Hat Enterprise Linux 6; Red Hat Enterprise Linux 7; Red Hat Enterprise Linux 8; Red Hat Enterprise Linux 9. Under investigation: Red Hat OpenShift Container Platform 4. Red Hat fixing advisory: RHSA-2026:36759.
A flaw was found in OpenSSH. A malicious SSH server can exploit a double free vulnerability in the Diffie-Hellman Group Exchange (DH-GEX) client path. This occurs during FIPS (Federal Information Processing Standards) mode known-group validation when the client processes attacker-controlled DH-GEX group parameters. Successful exploitation leads to client-side process termination, resulting in a Denial of Service (DoS). This Moderate flaw in OpenSSH affects clients operating in FIPS mode when negotiating Diffie-Hellman Group Exchange (DH-GEX) with a malicious SSH server. While it can lead to client process termination, resulting in a denial of service, the impact is limited to availability and does not result in broader system compromise. In order to exploit this vulnerability the attacker needs to trick the user to connect to an untrusted malicious server or compromise the server first. The availability impact is considered Low as the only impacted process is the single run of the SSH client trying to connect to the malicious server. This vulnerability affects only the OpenSSH versions shipped with Red Hat products. Red Hat severity: Moderate — CVSS 4.3 (CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:L). Weakness: CWE-415.
vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.22.0, vLLM's revision pinning controls do not consistently apply to all artifacts loaded for a model. A deployment that supplies --revision or --code-revision can still load dynamic code, GGUF files, image processors, retrieval side weights, or same-repository subfolder weights/config from an unpinned/default revision. This is a supply-chain integrity issue for pinned vLLM deployments. Operators can believe they are serving a reviewed model revision while vLLM resolves behavior-affecting nested or sibling artifacts outside that reviewed revision. This vulnerability is fixed in 0.22.0. This issue can lead to a supply-chain integrity compromise, where operators may unknowingly serve models with unreviewed or unintended behavior. Red Hat rates this issue as having Moderate impact. The flaw is a supply-chain integrity issue when operators pin a HuggingFace model revision but vLLM may still load nested artifacts from an unpinned revision. It affects Red Hat AI Inference Server, Red Hat OpenShift AI, and Red Hat Enterprise Linux AI images that ship vLLM versions prior to 0.22.0. KServe control-plane components that bundle vLLM as a library are not affected. Red Hat severity: Moderate — CVSS 6.5 (CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:H/A:N). Weakness: CWE-829.
vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.22.1, the vLLM Dockerfile is vulnerable to a dependency confusion attack through the flashinfer-jit-cache package. The package is installed from a custom index (flashinfer.ai/whl/) using --extra-index-url, but the package name was not registered on PyPI, and UV_INDEX_STRATEGY="unsafe-best-match" is set globally. An attacker who registers flashinfer-jit-cache on PyPI with version 0.6.11.post2 can execute arbitrary code as root during the Docker build and backdoor every resulting container image, enabling exfiltration of all user prompts, API credentials, and model data from production vLLM deployments This vulnerability is fixed in 0.22.1. CVE-2026-54232 is a build-time dependency confusion issue in upstream vLLM Dockerfiles before 0.22.1. It does not allow remote exploitation of a running vLLM inference service. Red Hat OpenShift AI is not affected. Red Hat AI Inference Server and RHEL AI CUDA images that include flashinfer-jit-cache are in scope for build-process review, but Red Hat has no evidence that shipped images were compromised. Red Hat severity: Moderate — CVSS 5.7 (CVSS:3.1/AV:N/AC:H/PR:H/UI:R/S:U/C:H/I:H/A:N). Weakness: CWE-426. Affected Red Hat products: Red Hat AI Inference Server; Red Hat Enterprise Linux AI (RHEL AI) 3. Red Hat does not currently list a fixing RHSA for this CVE.
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. A remote attacker could exploit a vulnerability in the `/v1/audio/transcriptions` endpoint. By uploading a specially crafted compressed audio file, such as an OPUS file, the attacker could cause the system to allocate an excessive amount of memory during the decoding process. This uncontrolled memory allocation can lead to a Denial of Service (DoS) condition, making the service unavailable to legitimate users. Red Hat rates this issue as having Moderate impact. A crafted audio upload to the vLLM /v1/audio/transcriptions endpoint can cause excessive decoded PCM allocation and denial of service. Affected components are vLLM serving images in Red Hat AI Inference Server, Red Hat OpenShift AI, and Red Hat Enterprise Linux AI bootc that ship vLLM prior to 0.23.1. KServe sidecars are not affected. Red Hat severity: Moderate — CVSS 6.5 (CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H). Weakness: CWE-770. Red Hat lists Red Hat OpenShift AI (RHOAI) as not affected. Red Hat does not currently list a fixing RHSA for this CVE.
vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.23.1rc0, the fix for CVE-2026-22778, which introduced a sanitize_message helper that strips object-repr memory addresses from error messages before they reach the client, is incomplete: several response paths echo str(exc) directly to clients without calling sanitize_message. The unsanitized sites include the Anthropic API router in vllm/entrypoints/anthropic/api_router.py (the POST /v1/messages and POST /v1/messages/count_tokens handlers), the Server-Sent Events streaming converter in vllm/entrypoints/anthropic/serving.py, and the realtime speech-to-text WebSocket in vllm/entrypoints/speech_to_text/realtime/connection.py. These paths catch the exception inside the route coroutine and construct the JSONResponse themselves, bypassing the sanitizing global FastAPI exception handler, and WebSocket frames do not traverse that handler chain at all. Using the same primitive as the parent issue, an unauthenticated attacker can send malformed image bytes through the Anthropic Messages API image content parts so that PIL.Image.open raises an UnidentifiedImageError whose message contains the BytesIO object repr, leaking the heap memory address verbatim in the error.message field of the response body. This vulnerability is fixed in 0.23.1rc0.
vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.23.1rc0, ll temperature validation gates use comparison operators (<, >), which silently evaluate to False for NaN and for positive Infinity in Python's IEEE 754 float semantics. Both values pass every guard and propagate to GPU sampling kernels, where they produce undefined behavior or CUDA errors that can crash the inference worker. This vulnerability is fixed in 0.23.1rc0. This could allow an attacker to cause a Denial of Service (DoS) by providing specially crafted input. This Moderate impact flaw in vLLM, as used in Red Hat AI Inference Server, Red Hat OpenShift AI, and Red Hat Enterprise Linux AI, allows for a denial of service. Improper validation of floating-point values like Not-a-Number (NaN) or positive Infinity in temperature parameters can bypass security checks, leading to undefined behavior or CUDA errors that crash the inference worker. This could be exploited by providing specially crafted input to the LLM inference engine. Red Hat severity: Moderate — CVSS 6.5 (CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:L/A:L). Weakness: CWE-1287. Affected Red Hat products: Red Hat AI Inference Server 3.2; Red Hat AI Inference Server; Red Hat Enterprise Linux AI (RHEL AI) 3; Red Hat OpenShift AI (RHOAI). Red Hat lists Red Hat OpenShift AI (RHOAI) as not affected.
vLLM is an inference and serving engine for large language models (LLMs). From 0.5.5 until 0.23.1rc0, integer truncation of tensor dimensions in vLLM's GGUF dequantize kernels (csrc/quantization/gguf/gguf_kernel.cu) causes partial tensor processing. The output tensor is allocated at full size via torch::empty (uninitialized memory), but the dequantize CUDA kernel processes only a truncated number of elements. The unfilled portion of the output tensor retains whatever was previously in GPU memory. In multi-tenant inference deployments, this residual GPU memory may contain tensor data from other users' inference requests, constituting information disclosure. This vulnerability is fixed in 0.23.1rc0. A flaw was found in vLLM. Red Hat rates this issue as having Low impact for Red Hat AI products. The upstream issue is limited information disclosure via integer truncation in vLLM sampling parameters. Red Hat OpenShift AI, Red Hat AI Inference Server, and Red Hat Enterprise Linux AI images are not considered affected because untrusted clients cannot control the vulnerable parameters in supported deployment models. Red Hat severity: Low — CVSS 4.3 (CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:N). Weakness: CWE-824.
WebOb provides objects for HTTP requests and responses. Prior to 1.8.10, the normalization of the HTTP Location header during a redirect is vulnerable to an open redirect: WebOb joins the redirect target to the request URI using Python's urljoin, and since Python 3.10 the underlying urlsplit strips ASCII tab, carriage return, and newline characters before parsing, so a redirect target containing such characters can be reinterpreted as a protocol-relative URL whose authority is an attacker-controlled host. This bypasses the CVE-2024-42353 fix that escaped a leading double slash, allowing an attacker who influences the redirect location to send users to an arbitrary external site instead of the intended one. This vulnerability is fixed in 1.8.10. Due to improper normalization of the Location header, specifically how certain ASCII characters are handled, an attacker can cause a user to be redirected to an arbitrary external website instead of the intended destination. This open redirect vulnerability can lead to information disclosure and impact the integrity of user sessions. This is rated as Moderate (CVSS 6.1) because exploitation requires user interaction — a victim must click a crafted link that triggers the redirect (UI:R).
pypdf is a free and open-source pure-python PDF library. Prior to 6.13.1, an attacker who uses this vulnerability can craft a PDF which leads to an infinite loop. This requires merging a file with threads/articles into a writer. This vulnerability is fixed in 6.13.1. A flaw was found in pypdf. This vulnerability can result in a Denial of Service (DoS) condition, making the affected system unresponsive. Red Hat rates this issue as Moderate severity (CVSS 5.9) because exploitation requires a specially crafted PDF containing thread/article objects to be processed by pypdf's merge functionality. Weakness: CWE-835. Affected Red Hat products: Exploit Intelligence; OpenShift Lightspeed; Red Hat Ansible Automation Platform 2; Red Hat Enterprise Linux AI (RHEL AI) 3; Red Hat OpenShift AI (RHOAI); Red Hat Quay 3. Red Hat does not currently list a fixing RHSA for this CVE.
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 long runtimes. This requires accessing a stream which uses the /FlateDecode filter with a PNG predictor. This vulnerability is fixed in 6.12.2. A flaw was found in pypdf (before 6.12.2). pypdf is vulnerable to denial of service when parsing a crafted PDF containing a /FlateDecode stream with a PNG predictor. An attacker who can supply such a document for processing may cause the application to hang on long runtimes. Red Hat exposure is in Python services that use pypdf for PDF ingestion, including Quay, OpenShift logging/observability tooling, and other containerized apps that bundle the library for document handling. Red Hat severity: Moderate — CVSS 5.5 (CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H). Weakness: CWE-770. Red Hat lists Exploit Intelligence; OpenShift Lightspeed; Red Hat Ansible Automation Platform 2; Red Hat Enterprise Linux AI (RHEL AI) 3; Red Hat OpenShift AI (RHOAI); Red Hat Quay 3 as not affected.
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. A flaw was found in pypdf. An attacker can craft a malicious PDF document containing a form XObject with self-references. When a user attempts to extract text from a page within this crafted PDF, it can lead to excessive memory consumption. This vulnerability may result in a Denial of Service (DoS) due to resource exhaustion. Moderate: A flaw in pypdf and python-PyPDF2 can lead to a denial of service due to excessive memory consumption. This occurs when processing a specially crafted PDF document containing self-referencing form XObjects during text extraction. The vulnerability requires user interaction to trigger the text extraction process. Red Hat severity: Moderate — CVSS 5.5 (CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H). Weakness: CWE-835. Affected Red Hat products: Exploit Intelligence; OpenShift Lightspeed; Red Hat Ansible Automation Platform 2; Red Hat Enterprise Linux AI (RHEL AI) 3; Red Hat OpenShift AI (RHOAI); Red Hat Quay 3. Red Hat does not currently list a fixing RHSA for this CVE.