vLLM is an inference and serving engine for large language models (LLMs). From versions 0.10.2 to before 0.11.1, a memory corruption vulnerability could lead to a crash (denial-of-service) and potentially remote code execution (RCE), exists in the Completions API endpoint. When processing user-supplied prompt embeddings, the endpoint loads serialized tensors using torch.load() without sufficient validation. Due to a change introduced in PyTorch 2.8.0, sparse tensor integrity checks are disabled by default. As a result, maliciously crafted tensors can bypass internal bounds checks and trigger an out-of-bounds memory write during the call to to_dense(). This memory corruption can crash vLLM and potentially lead to code execution on the server hosting vLLM. This issue has been patched in version 0.11.1.

vLLM is an inference and serving engine for large language models (LLMs). In version 0.8.0 up to but excluding 0.9.0, the vLLM backend used with the /v1/chat/completions OpenAPI endpoint fails to validate unexpected or malformed input in the "pattern" and "type" fields when the tools functionality is invoked. These inputs are not validated before being compiled or parsed, causing a crash of the inference worker with a single request. The worker will remain down until it is restarted. Version 0.9.0 fixes the issue.

Bugsink is a self-hosted error tracking tool. In 2.1.0, an authenticated file write vulnerability was identified in Bugsink 2.1.0 in the artifact bundle assembly flow. A user with a valid authentication token could cause the application to write attacker-controlled content to a filesystem location writable by the Bugsink process. This vulnerability is fixed in 2.1.1.

CycloneDX BOM Repository Server is a bill of materials (BOM) repository server for distributing CycloneDX BOMs. CycloneDX BOM Repository Server before version 2.0.1 has an improper input validation vulnerability leading to path traversal. A malicious user may potentially exploit this vulnerability to create arbitrary directories or a denial of service by deleting arbitrary directories. The vulnerability is resolved in version 2.0.1. The vulnerability is not exploitable with the default configuration with the post and delete methods disabled. This can be configured by modifying the `appsettings.json` file, or alternatively, setting the environment variables `ALLOWEDMETHODS__POST` and `ALLOWEDMETHODS__DELETE` to `false`.

ormar is a async mini ORM for Python. Versions 0.23.0 and below are vulnerable to Pydantic validation bypass through the model constructor, allowing any unauthenticated user to skip all field validation by injecting "__pk_only__": true into a JSON request body. By injecting "__pk_only__": true into a JSON request body, an unauthenticated attacker can skip all field validation and persist unvalidated data directly to the database. A secondary __excluded__ parameter injection uses the same pattern to selectively nullify arbitrary model fields (e.g., email or role) during construction. This affects ormar's canonical FastAPI integration pattern recommended in its official documentation, enabling privilege escalation, data integrity violations, and business logic bypass in any application using ormar.Model directly as a request body parameter. This issue has been fixed in version 0.23.1.