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.
Nhost is an open source Firebase alternative with GraphQL. Prior to 0.48.0, the auth service's OAuth provider callback flow places the refresh token directly into the redirect URL as a query parameter. Refresh tokens in URLs are logged in browser history, server access logs, HTTP Referer headers, and proxy/CDN logs. Note that the refresh token is one-time use and all of these leak vectors are on owned infrastructure or services integrated by the application developer. This vulnerability is fixed in 0.48.0.
Vim is an open source, command line text editor. Prior to version 9.2.0276, a modeline sandbox bypass in Vim allows arbitrary OS command execution when a user opens a crafted file. The `complete`, `guitabtooltip` and `printheader` options are missing the `P_MLE` flag, allowing a modeline to be executed. Additionally, the `mapset()` function lacks a `check_secure()` call, allowing it to be abused from sandboxed expressions. Commit 9.2.0276 fixes the issue.
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.
OpenEXR provides the specification and reference implementation of the EXR file format, an image storage format for the motion picture industry. From 3.2.0 to before 3.2.7, 3.3.9, and 3.4.9, the DWA lossy decoder constructs temporary per-component block pointers using signed 32-bit arithmetic. For a large enough width, the calculation overflows and later decoder stores operate on a wrapped pointer outside the allocated rowBlock backing store. This vulnerability is fixed in 3.2.7, 3.3.9, and 3.4.9.
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.
OpenEXR provides the specification and reference implementation of the EXR file format, an image storage format for the motion picture industry. From 3.4.0 to before 3.4.9, a missing bounds check on the dataWindow attribute in EXR file headers allows an attacker to trigger a signed integer overflow in generic_unpack(). By setting dataWindow.min.x to a large negative value, OpenEXRCore computes an enormous image width, which is later used in a signed integer multiplication that overflows, causing the process to terminate with SIGILL via UBSan. This vulnerability is fixed in 3.4.9.