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High [CVE-2026-55603] Data integrity compromise via CR/LF injection
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.
High [CVE-2026-54293] Information Disclosure via Path Traversal in `nltk.data.load `
NLTK (Natural Language Toolkit) is a suite of open source Python modules, data sets, and tutorials supporting research and development in Natural Language Processing. Prior to 3.10.0-rc1, nltk.data.load() in NLTK is vulnerable to path traversal via URL-encoded path separators and traversal segments when using the nltk: URL scheme. The unsafe-path regex check is performed before url2pathname() decodes the %xx sequences (a classic decode-after-check / TOCTOU-style flaw), allowing an attacker to bypass the protection documented in NLTK's SECURITY.md and read arbitrary files from the filesystem. While literal traversal strings such as../../../etc/passwd are correctly blocked, encoded variants such as %2fetc%2fpasswd, %2e%2e%2f..., and..%2f..%2f slip past the regex and are subsequently decoded into a real filesystem path. This vulnerability is fixed in 3.10.0-rc1. An attacker can exploit a decode-after-check flaw, where URL-encoded path separators and traversal segments bypass security checks. This vulnerability is rated Important because a remote attacker may be able to read arbitrary local files on a system where an application passes untrusted input to nltk.data.load() using the nltk: URL scheme. Exploitation requires an application that loads NLTK resources from attacker-influenced identifiers.
High [CVE-2026-55388] Arbitrary code execution via prototype pollution of filename option
piscina is a node.js worker pool implementation. Prior to 6.0.0-rc.2, 5.2.0, and 4.9.3, piscina's constructor and run() paths read the filename option via plain member access. Both reads fall through the prototype chain when the caller's options object doesn't have filename as an own property. When Object.prototype.filename is polluted upstream the inherited value flows to worker_threads. Worker import and the attacker's.mjs runs in the worker. This vulnerability is fixed in 6.0.0-rc.2, 5.2.0, and 4.9.3. This vulnerability allows an attacker to achieve arbitrary code execution by exploiting a prototype pollution issue. By manipulating the `filename` option, an attacker can cause their malicious code to be executed within the worker, potentially compromising the system's confidentiality, integrity, and availability. Red Hat products are not affected by this vulnerability as the vulnerable code is not present in Red Hat's `piscina` implementation. Red Hat severity: Important — CVSS 8.1 (CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H). Weakness: CWE-915.
High [CVE-2026-54283] request.form limits silently ignored for application/x-www-form-urlencoded enable DoS
Starlette is a lightweight ASGI framework/toolkit. From 0.4.1 until 1.3.1, request.form() accepts max_fields and max_part_size to bound resource consumption while parsing form data. These limits are enforced for multipart/form-data, but silently ignored for application/x-www-form-urlencoded. An unauthenticated attacker can therefore send a urlencoded body with an arbitrarily large number of fields or an arbitrarily large field, even when the application configured limits it believed would apply. This vulnerability is fixed in 1.3.1. An unauthenticated attacker can exploit this by sending a urlencoded request body with an arbitrarily large number of fields or an oversized field, causing denial of service through resource exhaustion. This only affects applications that explicitly call request.form() on urlencoded input; JSON-only APIs and services where Starlette is a transitive dependency not used for form parsing are not affected. Red Hat severity: Important — CVSS 7.5 (CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H). Weakness: CWE-770. Affected products named by the advisory: Red Hat AI Inference Server 3.2; Red Hat Ansible Automation Platform 2.6; Red Hat Ansible Automation Platform 2.7; Red Hat Migration Toolkit for Applications 8.2; and 6 more.
High [CVE-2026-48712] Denial of Service via uncontrolled recursion with crafted protobuf payload
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. 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 Developer Hub 1.9; Red Hat Enterprise Linux AI (RHEL AI) 3; Red Hat OpenShift AI (RHOAI); Red Hat OpenShift Container Platform 4. Red Hat lists Cryostat 4; OpenShift Pipelines; OpenShift Service Mesh 3; Red Hat Ansible Automation Platform 2; Red Hat Build of Podman Desktop; Red Hat Ceph Storage 9; Red Hat Enterprise Linux 8; Red Hat Hardened Images; Red Hat OpenShift Container Platform 4; Red Hat Openshift Data Foundation 4; Self-service automation portal 2 as not affected.
High [CVE-2026-46417] @angular/platform-server: Angular: SSRF via Hostname Hijacking in @angular/platform-server
Angular is a development platform for building mobile and desktop web applications using TypeScript/JavaScript and other languages. Prior to 22.0.0-next.12, 21.2.13, 20.3.21, and 19.2.22, a Server-Side Request Forgery (SSRF) vulnerability exists in @angular/platform-server. The issue stems from how the server-side rendering (SSR) engine processes the request URL provided to the rendering entry points. When an absolute-form URL (e.g., ) is passed to the rendering engine, the internal ServerPlatformLocation can be manipulated into adopting the attacker-controlled domain as the "current" hostname. Consequently, any relative HttpClient requests or PlatformLocation.hostname references are redirected to the attacker controlled server, potentially exposing internal APIs or metadata services. This occurs when an absolute-form URL is provided to the server-side rendering engine, potentially exposing internal APIs or metadata services. Red Hat Enterprise Linux is not affected as the vulnerable code is not in its execution path. Red Hat severity: Important. Affected Red Hat products: Red Hat Fuse 7. Will not fix / out of support: Red Hat Fuse 7. Red Hat does not currently list a fixing RHSA for this CVE.
High [CVE-2026-50556] @angular/platform-server: domino: Angular Platform Server: Cross-Site Scripting via unescaped `</noscript>` tags in dynamic content
Angular is a development platform for building mobile and desktop web applications using TypeScript/JavaScript and other languages. Prior to 22.0.0-rc.2, 21.2.16, 20.3.24, and 19.2.25, a Cross-Site Scripting (XSS) vulnerability exists in @angular/platform-server's DOM emulation dependency (domino) when serializing the content of in the bound dynamic text was never escaped under any circumstances. The unescaped closing tag was serialized directly into the output HTML (e.g. ). When parsed by a browser, it closes the ` tags within dynamic text content. This allows for the execution of arbitrary code in the user's browser context, potentially leading to information disclosure or other malicious activities. This flaw has an Important impact as @angular/platform-server's DOM emulation dependency (domino) in Red Hat products does not properly escape closing noscript tags during server-s…
Medium [CVE-2026-55655] Local MITM of X11 forwarding via abstract UNIX socket pre-binding in Red Hat Enterprise Linux OpenSSH client versions
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 Enterprise Linux 10; Red Hat Enterprise Linux 8; Red Hat Enterprise Linux 9; Red Hat Hardened Images; Red Hat Update Infrastructure 5; Red Hat Enterprise Linux 6; Red Hat Enterprise Linux 7. Under investigation: Red Hat OpenShift Container Platform 4.
Medium [CVE-2026-55653] Double free in Red Hat Enterprise Linux versions of OpenSSH DH-GEX client path during FIPS known-group validation leads to client-side denial of service
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.
Medium [CVE-2026-47155] Supply-chain integrity issue due to inconsistent revision pinning controls
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.
Medium [CVE-2026-54232] Arbitrary code execution via dependency confusion during Docker build
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.
Medium [CVE-2026-54233] Denial of Service via excessive memory allocation in audio transcription
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.
Medium [CVE-2026-54236] Information Disclosure via Incomplete Error Message Sanitization
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.
Medium [CVE-2026-54235] Denial of Service due to improper floating-point validation
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.
Medium [CVE-2026-53923] Information disclosure via integer truncation
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. Red Hat fixing advisory: RHSA-2026:59138, RHSA-2026:59139. Affected product named by the advisory: Red Hat AI Inference Server 3.3.
Medium [CVE-2026-44889] Open Redirect vulnerability via HTTP Location header normalization
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). Affected products named by the advisory: Red Hat Enterprise Linux 6; Red Hat package: python-webob.
Medium [CVE-2026-54651] Denial of Service via crafted PDF
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.
Medium [CVE-2026-49460] Denial of Service via crafted PDF with FlateDecode filter
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.
Medium [CVE-2026-49461] Denial of Service via crafted PDF with self-referencing form XObject
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.
Medium [CVE-2026-54531] Denial of Service via crafted PDF with outlines
pypdf is a free and open-source pure-python PDF library. Prior to 6.13.0, an attacker who uses this vulnerability can craft a PDF which leads to an infinite loop. This requires merging a file with outlines into a writer. This vulnerability is fixed in 6.13.0. A flaw was found in pypdf (before 6.13.0). A crafted PDF with outlines can trigger an infinite loop when merged into a PdfWriter, causing denial of service. An attacker who can supply such a document for merge processing may hang the application indefinitely. Red Hat exposure mirrors other pypdf consumers: Python services that merge or rewrite PDFs using the library in Quay, observability, and hybrid platform containers. 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. 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.