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Vulnerabilidades

Con el objetivo de informar, advertir y ayudar a los profesionales sobre las últimas vulnerabilidades de seguridad en sistemas tecnológicos, ponemos a disposición de los usuarios interesados en esta información una base de datos con información en castellano sobre cada una de las últimas vulnerabilidades documentadas y conocidas.

Este repositorio con más de 75.000 registros esta basado en la información de NVD (National Vulnerability Database) – en función de un acuerdo de colaboración – por el cual desde INCIBE realizamos la traducción al castellano de la información incluida. En ocasiones este listado mostrará vulnerabilidades que aún no han sido traducidas debido a que se recogen en el transcurso del tiempo en el que el equipo de INCIBE realiza el proceso de traducción.

Se emplea el estándar de nomenclatura de vulnerabilidades CVE (Common Vulnerabilities and Exposures), con el fin de facilitar el intercambio de información entre diferentes bases de datos y herramientas. Cada una de las vulnerabilidades recogidas enlaza a diversas fuentes de información así como a parches disponibles o soluciones aportadas por los fabricantes y desarrolladores. Es posible realizar búsquedas avanzadas teniendo la opción de seleccionar diferentes criterios como el tipo de vulnerabilidad, fabricante, tipo de impacto entre otros, con el fin de acortar los resultados.

Mediante suscripción RSS o Boletines podemos estar informados diariamente de las últimas vulnerabilidades incorporadas al repositorio.

CVE-2026-54235
Fecha de publicación:
22/06/2026
Idioma:
Inglés
*** Pendiente de traducción *** 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.
Gravedad CVSS v4.0: MEDIA
Última modificación:
24/06/2026

CVE-2026-54236
Fecha de publicación:
22/06/2026
Idioma:
Inglés
*** Pendiente de traducción *** 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.
Gravedad CVSS v3.1: MEDIA
Última modificación:
24/06/2026

CVE-2026-47155
Fecha de publicación:
22/06/2026
Idioma:
Inglés
*** Pendiente de traducción *** 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.
Gravedad CVSS v3.1: MEDIA
Última modificación:
24/06/2026

CVE-2026-53923
Fecha de publicación:
22/06/2026
Idioma:
Inglés
*** Pendiente de traducción *** 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.
Gravedad CVSS v4.0: MEDIA
Última modificación:
24/06/2026

CVE-2026-54232
Fecha de publicación:
22/06/2026
Idioma:
Inglés
*** Pendiente de traducción *** 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.
Gravedad CVSS v3.1: ALTA
Última modificación:
24/06/2026

CVE-2026-41523
Fecha de publicación:
22/06/2026
Idioma:
Inglés
*** Pendiente de traducción *** vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.22.0, an assert-based security check in vLLM's activation function loading allows any unauthenticated attacker to achieve arbitrary code execution on the server by publishing a malicious HuggingFace model, when vLLM runs in Python optimized mode (python -O or PYTHONOPTIMIZE=1). This vulnerability is fixed in 0.22.0.
Gravedad CVSS v3.1: ALTA
Última modificación:
15/07/2026

CVE-2026-48746
Fecha de publicación:
22/06/2026
Idioma:
Inglés
*** Pendiente de traducción *** vLLM is an inference and serving engine for large language models (LLMs). From 0.3.0 until 0.22.0, a vulnerability in ASGI web servers and starlette's trust on those web servers enables an authentication bypass of the OpenAI API AuthenticationMiddleware. It allows to use the API without providing the configured VLLM_API_KEY or --api-key. This vulnerability is fixed in 0.22.0.
Gravedad CVSS v3.1: CRÍTICA
Última modificación:
22/07/2026

CVE-2026-56324
Fecha de publicación:
22/06/2026
Idioma:
Inglés
*** Pendiente de traducción *** Capgo before 12.128.2 contains a rate limit bypass vulnerability in the channel_self endpoint that allows attackers to circumvent rate limiting by rotating the user-controlled device_id parameter. Attackers can send multiple requests per second by changing device_id values to flood the channel_devices table and cause database exhaustion.
Gravedad CVSS v4.0: ALTA
Última modificación:
24/06/2026

CVE-2026-56348
Fecha de publicación:
22/06/2026
Idioma:
Inglés
*** Pendiente de traducción *** n8n before 2.20.0 contains a credential exfiltration vulnerability in the POST /rest/dynamic-node-parameters/options endpoint that allows authenticated users to bypass Allowed HTTP Request Domains restrictions. Attackers with credential access can cause the n8n server to issue HTTP requests with credentials to unauthorized hosts, exfiltrating sensitive authentication data.
Gravedad CVSS v4.0: MEDIA
Última modificación:
24/06/2026

CVE-2026-56357
Fecha de publicación:
22/06/2026
Idioma:
Inglés
*** Pendiente de traducción *** n8n before 1.123.15 and 2.5.0 contains a webhook forgery vulnerability in the GitHub Webhook Trigger node that fails to implement HMAC-SHA256 signature verification. Attackers who know the webhook URL can send unsigned POST requests to trigger workflows with arbitrary data, spoofing GitHub webhook events.
Gravedad CVSS v4.0: MEDIA
Última modificación:
24/06/2026

CVE-2026-56280
Fecha de publicación:
22/06/2026
Idioma:
Inglés
*** Pendiente de traducción *** Cap-go before 12.128.2 contains a privilege inversion vulnerability in GET /build/logs/:jobId that allows read-only API key holders to cancel running native builds. The endpoint registers an abort listener on the SSE stream that unconditionally invokes cancelBuildOnDisconnect() using the privileged server-side BUILDER_API_KEY when clients disconnect, bypassing the app.build_native permission check required by the explicit POST /build/cancel/:jobId endpoint. Attackers with read-only API keys can repeatedly disrupt native build operations and CI/CD workflows by opening the log stream and dropping the connection.
Gravedad CVSS v4.0: ALTA
Última modificación:
24/06/2026

CVE-2026-56323
Fecha de publicación:
22/06/2026
Idioma:
Inglés
*** Pendiente de traducción *** Capgo before 12.128.2 contains an information disclosure vulnerability in the /functions/v1/channel_self endpoint that allows unauthenticated attackers to enumerate non-public channel names and determine app existence and subscription status. Remote attackers can send GET requests with arbitrary app_id parameters to disclose internal rollout channels, enumerate valid applications across tenants, and leak billing status without authentication or device binding.
Gravedad CVSS v4.0: ALTA
Última modificación:
23/06/2026
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