Veille CVE

Jeudi 14 mai 2026

6 CVE

Critique 0 Élevée 5 Moyenne 1 Faible 0 Non évaluée 0 Exploitées (KEV) Tri : plus récentes « Python python » ✕
  1. CVE-2026-45370 7.7 Élevée

    universal-tool-calling-protocol python-utcp

    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.

  2. CVE-2026-45369 8.3 Élevée

    universal-tool-calling-protocol python-utcp

    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.

  3. CVE-2026-44661 4.7 Moyenne

    universal-tool-calling-protocol python-utcp

    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.

  4. CVE-2026-8597 7.2 Élevée

    AWS Amazon SageMaker Python SDK

    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.

  5. CVE-2026-8596 7.2 Élevée

    AWS Amazon SageMaker Python 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.

  6. CVE-2026-44827 8.8 Élevée

    huggingface diffusers

    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.