VAITP

Open WebUI is a self-hosted artificial intelligence platform designed to operate entirely offline. Prior to 0.9.5, the tool update endpoint (POST /api/v1/tools/id/{id}/update) is missing the workspace.tools permission check that is present on the tool create endpoint. This allows a user who has been explicitly denied tool management capabilities ( and who the administrator considers untrusted for code execution ) to replace a tool's server-side Python content and trigger execution, bypassing the intended workspace.tools security boundary. This vulnerability is fixed in 0.9.5.

Microsoft APM is an open-source, community-driven dependency manager for AI agents. Prior to 0.13.0, Microsoft APM contains a Windows-specific archive extraction boundary failure in the legacy-bundle probe used by apm install <bundle> on supported Python 3.10 and 3.11 runtimes. When apm install is given a local .tar.gz that is not recognized as a plugin-format bundle, APM probes whether it is a legacy --format apm bundle. On Python versions earlier than 3.12, that probe extracts untrusted tar members with raw tar.extractall() without rejecting Windows absolute member names such as D:/.... This vulnerability is fixed in 0.13.0.

python-utcp is the python implementation of UTCP. Prior to 1.1.3, _prepare_environment() in cli_communication_protocol.py passes a full copy of os.environ to every CLI subprocess. When combined with CVE-2026-45369, an attacker can exfiltrate all process-level secrets in a single tool call. This vulnerability is fixed in 1.1.3.

python-utcp is the python implementation of UTCP. Prior to 1.1.3, the _substitute_utcp_args method in cli_communication_protocol.py inserts user-controlled tool_args values directly into shell command strings without any sanitization or escaping. These commands are then executed via /bin/bash -c (Unix) or powershell.exe -Command (Windows), allowing an attacker to inject arbitrary shell commands. This vulnerability is fixed in 1.1.3.

python-utcp is the python implementation of UTCP. Prior to 1.1.3, the utcp-http plugin is vulnerable to a blind Server-Side Request Forgery (SSRF) caused by a trust-boundary inconsistency between manual discovery and tool invocation. register_manual() validates the discovery URL against an HTTPS / loopback allowlist, but call_tool() and call_tool_streaming() reuse the resolved tool_call_template.url directly without revalidating, and the OpenAPI converter blindly trusts whatever servers[0].url an attacker-hosted spec declares. An attacker who hosts a malicious OpenAPI spec on a legitimate HTTPS endpoint can declare e.g. servers: [{ url: "http://127.0.0.1:9090" }] or servers: [{ url: "http://169.254.169.254" }]; the OpenAPI converter then produces tools whose URL points at internal services on the agent host. All three HTTP-class protocols (utcp_http.http, utcp_http.streamable_http, utcp_http.sse) shared the same gap. This vulnerability is fixed in 1.1.3.

Missing integrity verification in the Triton inference handler in Amazon SageMaker Python SDK v2 before v2.257.2 and v3 before v3.8.0 might allow a remote authenticated actor to achieve code execution in inference containers via replacement of model artifacts in S3 with a specially crafted pickle payload that is deserialized without verification. This issue requires a remote authenticated actor with S3 write access to the model artifact path. To remediate this issue, we recommend upgrading to Amazon SageMaker Python SDK v2.257.2 or v3.8.0 and rebuild any Triton models previously created with ModelBuilder using the updated SDK.

Cleartext storage of sensitive information in the ModelBuilder/Serve component in Amazon SageMaker Python SDK before v2.257.2 and v3 before v3.8.0 might allow a remote authenticated actor to extract the HMAC signing key from SageMaker API responses and forge valid integrity signatures for specially crafted model artifacts, achieving code execution in inference containers. This issue requires a remote authenticated actor with permissions to call SageMaker describe APIs and S3 write access to the model artifact path. To remediate this issue, we recommend upgrading to Amazon SageMaker Python SDK v2.257.2 or v3.8.0 and rebuild any models previously created with ModelBuilder using the updated SDK.

Diffusers is the a library for pretrained diffusion models. Prior to 0.38.0, diffusers 0.37.0 allows remote code execution without the trust_remote_code=True safeguard when loading pipelines from Hugging Face Hub repositories. The _resolve_custom_pipeline_and_cls function in pipeline_loading_utils.py performs string interpolation on the custom_pipeline parameter using f"{custom_pipeline}.py". When custom_pipeline is not supplied by the user, it defaults to None, which Python interpolates as the literal string "None.py". If an attacker publishes a Hub repository containing a file named None.py with a class that subclasses DiffusionPipeline, the file is automatically downloaded and executed during a standard DiffusionPipeline.from_pretrained() call with no additional keyword arguments. The trust_remote_code check in DiffusionPipeline.download() is bypassed because it evaluates custom_pipeline is not None as False (since the kwarg was never supplied), while the downstream code path that actually loads the module resolves the None value into a valid filename. An attacker can achieve silent arbitrary code execution by publishing a malicious model repository with a None.py file and a standard-looking model_index.json that references a legitimate pipeline class name, requiring only that a victim calls from_pretrained on the repository. This vulnerability is fixed in 0.38.0.

Python-Multipart is a streaming multipart parser for Python. Prior to 0.0.27, python-multipart has a denial of service vulnerability in multipart part header parsing. When parsing multipart/form-data, MultipartParser previously had no limit on the number of part headers or the size of an individual part header. An attacker could send a request with either many repeated headers without terminating the header block or a single very large header value, causing excessive CPU work before request rejection or completion. This vulnerability is fixed in 0.0.27.

Twisted is an event-based framework for internet applications, supporting Python 3.6+. Prior to 26.4.0rc2, the twisted.names module is vulnerable to a Denial of Service (DoS) attack via resource exhaustion during DNS name decompression. A remote, unauthenticated attacker can exploit this by sending a crafted TCP DNS packet containing deeply chained compression pointers. This flaw bypasses previous loop-prevention logic, causing the single-threaded Twisted reactor to hang while processing millions of recursive lookups, effectively freezing the server. This vulnerability is fixed in 26.4.0rc2.