| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| Memory corruption when decoding corrupted satellite data files with invalid signature offsets. |
| Cryptographic issue while copying data to a destination buffer without validating its size. |
| Transient DOS when processing nonstandard FILS Discovery Frames with out-of-range action sizes during initial scans. |
| Memory Corruption when retrieving output buffer with insufficient size validation. |
| Memory Corruption when sending IOCTL requests with invalid buffer sizes during memcpy operations. |
| Memory Corruption when accessing an output buffer without validating its size during IOCTL processing. |
| Memory Corruption when processing auxiliary sensor input/output control commands with insufficient buffer size validation. |
| Memory Corruption when accessing an output buffer without validating its size during IOCTL processing. |
| Memory Corruption when accessing an output buffer without validating its size during IOCTL processing in a camera sensor driver. |
| Memory Corruption when accessing an output buffer without validating its size during IOCTL processing in a camera sensor driver. |
| Memory Corruption when using deprecated DMABUF IOCTL calls to manage video memory. |
| Transient DOS when receiving a service data frame with excessive length during device matching over a neighborhood awareness network protocol connection. |
| Memory Corruption when handling power management requests with improperly sized input/output buffers. |
| vLLM is an inference and serving engine for large language models (LLMs). From 0.16.0 to before 0.19.0, a server-side request forgery (SSRF) vulnerability in download_bytes_from_url allows any actor who can control batch input JSON to make the vLLM batch runner issue arbitrary HTTP/HTTPS requests from the server, without any URL validation or domain restrictions.
This can be used to target internal services (e.g. cloud metadata endpoints or internal HTTP APIs) reachable from the vLLM host. This vulnerability is fixed in 0.19.0. |
| 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. |
| Electron is a framework for writing cross-platform desktop applications using JavaScript, HTML and CSS. From 33.0.0-alpha.1 to before 39.8.5, 40.8.5, 41.1.0, and 42.0.0-alpha.5, apps that use offscreen rendering with GPU shared textures may be vulnerable to a use-after-free. Under certain conditions, the release() callback provided on a paint event texture can outlive its backing native state, and invoking it after that point dereferences freed memory in the main process, which may lead to a crash or memory corruption. Apps are only affected if they use offscreen rendering with webPreferences.offscreen: { useSharedTexture: true }. Apps that do not enable shared-texture offscreen rendering are not affected. To mitigate this issue, ensure texture.release() is called promptly after the texture has been consumed, before the texture object becomes unreachable. This vulnerability is fixed in 39.8.5, 40.8.5, 41.1.0, and 42.0.0-alpha.5. |
| KubeAI is an AI inference operator for kubernetes. Prior to 0.23.2, the ollamaStartupProbeScript() function in internal/modelcontroller/engine_ollama.go constructs a shell command string using fmt.Sprintf with unsanitized model URL components (ref, modelParam). This shell command is executed via bash -c as a Kubernetes startup probe. An attacker who can create or update Model custom resources can inject arbitrary shell commands that execute inside model server pods. This vulnerability is fixed in 0.23.2. |
| fast-jwt provides fast JSON Web Token (JWT) implementation. In 6.1.0 and earlier, the publicKeyPemMatcher regex in fast-jwt/src/crypto.js uses a ^ anchor that is defeated by any leading whitespace in the key string, re-enabling the exact same JWT algorithm confusion attack that CVE-2023-48223 patched. |
| Workbench is a suite of tools for administrators and developers to interact with Salesforce.com organizations via the Force.com APIs. Prior to 65.0.0, Workbench contains a reflected cross-site scripting vulnerability via the footerScripts parameter, which does not sanitize user-supplied input before rendering it in the page response. Improper neutralization of input during web page generation ('cross-site scripting') vulnerability in Workbench allows XSS Targeting Error Pages. This vulnerability is fixed in 65.0.0. |
