Vulnerabilities
All
My stack
Known exploited
Public exploit
Critical (9+)
Likely exploited (EPSS ≥ 50%)
Changed, 24 h
New, 24 h
Sort:
risk
modified
published
CVSS
EPSS
| CVE | CVSS | EPSS | Vendor, product | Description | Modified |
|---|---|---|---|---|---|
| CVE-2026-64849KEVexploit ×5 | 9.3 critical | 10.0% | lfprojects, mlflow | MLflow is an open source AI engineering platform for agents, large language models, and machine learning models. Starting in 3.3.0 and prior to 3.15.0, the unauthenticated POST /api/2.0/mlflow/webhooks/{id}/test endpoint calls _validate_webhook_url() in mlflow/utils/validation.py only for the original URL while mlflow/webhooks/delivery.py follows redirects and re-resolves the hostname without pinning the validated address, allowing attackers to reach internal or cloud metadata services and receive response_status and response_body. This issue is fixed in version 3.15.0. | Mon 26 Oct |
| CVE-2025-11201exploit | 9.8 critical | 26.0% | lfprojects, mlflow | MLflow Tracking Server Model Creation Directory Traversal Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of MLflow Tracking Server. Authentication is not required to exploit this vulnerability. The specific flaw exists within the handling of model file paths. The issue results from the lack of proper validation of a user-supplied path prior to using it in file operations. An attacker can leverage this vulnerability to execute code in the context of the service account. Was ZDI-CAN-26921. | 19 Oct |
| CVE-2026-116692exploit | 9.8 critical | 1.0% | no CPE yet | MLflow is an open source AI engineering platform for agents, large language models, and machine learning models. From 3.12.0rc0 until 3.17.0, third-party scorer deserialization in mlflow/genai/scorers/base.py accepts request-selected descendants of THIRD_PARTY_SCORER_ALLOWED_MODULES rather than requiring an exact allowed module. A caller can use MLflow artifact endpoints to place a Python module beneath a writable allowed package and submit its module and class through /ajax-api/3.0/mlflow/scorer/invoke. Scorer.model_validate_json() imports the module and constructs the class before trace processing. Authentication-disabled deployments permit this code execution without credentials, while basic-auth deployments require a principal capable of creating an experiment and run and uploading artifacts. In either configuration, the allowed scorer package must be writable by the server process. This issue is fixed in version 3.17.0. | 14 h ago |
| CVE-2026-53995exploit | 8.8 high | 1.0% | no CPE yet | MLflow 3.17.1 and earlier contains a path traversal vulnerability in the FileStore tracking backend that allows unauthenticated attackers to control filesystem paths by submitting a crafted trace ID containing traversal sequences to the POST /api/3.0/mlflow/traces endpoint. Attackers can exploit FileStore.start_trace() to write trace_info.yaml, request_metadata, and tag files with attacker-controlled metadata outside the intended experiment trace directory, subject to the server process's filesystem permissions. FileStore is the default tracking backend before MLflow 3.7.0 and opt-in thereafter (the default changed to a SQLite database), so deployments in the earlier part of the affected range are vulnerable without special configuration while later ones are affected only when FileStore is explicitly selected. | 20 Oct |
| CVE-2025-11200 | 9.8 critical | 1.0% | lfprojects, mlflow | MLflow Weak Password Requirements Authentication Bypass Vulnerability. This vulnerability allows remote attackers to bypass authentication on affected installations of MLflow. Authentication is not required to exploit this vulnerability. The specific flaw exists within the handling of passwords. The issue results from weak password requirements. An attacker can leverage this vulnerability to bypass authentication on the system. Was ZDI-CAN-26916. | 19 Oct |
| CVE-2026-8147 | 8.1 high | 1.0% | lfprojects, mlflow | In MLflow versions prior to 3.14.0, when running with authentication enabled, the trace API endpoints lack proper authorization validators. This allows any authenticated user to bypass experiment-level authorization controls on all trace operations, including reading, deleting, and modifying traces on experiments they do not have permission to access. The issue arises from the `_before_request` handler, which does not register authorization validators for trace endpoints, resulting in requests proceeding without validation. This vulnerability can expose sensitive data, destroy audit logs, and allow unauthorized modifications. | Sun 25 Oct |
| CVE-2026-116690 | 7.5 high | 1.0% | no CPE yet | MLflow is an open source AI engineering platform for agents, large language models, and machine learning models. From 3.10.0 until 3.17.0, the prompt branch in _create_model_version() accepts schemeless relative sources because path validation runs only when a source has a URL scheme, and mlflow.prompt.is_prompt is interpreted by tag presence rather than a true value. A remote caller can store a server-local path as the model version artifact location and use /api/2.0/mlflow/model-versions/get-artifact to read files accessible to the MLflow server process. Default unauthenticated servers require no privileges, while basic-auth deployments are exposed to authenticated users who can create registered models. This issue is fixed in version 3.17.0. | 13 h ago |
| CVE-2026-2611 | 9.6 critical | 0.00% | lfprojects, mlflow | In MLflow version 3.9.0, the MLflow Assistant feature introduced improper origin validation in its /ajax-api endpoints. This vulnerability allows a remote attacker to exploit cross-origin requests from a malicious webpage to interact with the MLflow Assistant running on a victim's local machine. By bypassing the loopback-only restriction, the attacker can modify the Assistant's configuration to enable full access, which in turn allows the execution of arbitrary commands via the Claude Code sub-agent. This issue is resolved in version 3.10.0. | 14 Oct |
| CVE-2026-47487 | 7.1 high | 0.00% | nvidia, triton_inference_server | NVIDIA Triton Inference Server for Linux contains a vulnerability where a user could cause files outside the model repository to be read, written to, or modified by providing a path in the model name to the Triton MLflow plugin. A successful exploit of this vulnerability might lead to denial of service and information disclosure. | Sat 24 Oct |
| CVE-2025-15381 | 7.1 high | 0.00% | lfprojects, mlflow | In the latest version of mlflow/mlflow, when the `basic-auth` app is enabled, tracing and assessment endpoints are not protected by permission validators. This allows any authenticated user, including those with `NO_PERMISSIONS` on the experiment, to read trace information and create assessments for traces they should not have access to. This vulnerability impacts confidentiality by exposing trace metadata and integrity by allowing unauthorized creation of assessments. Deployments using `mlflow server --app-name=basic-auth` are affected. | 12 Oct |
| CVE-2026-116691 | 6.5 medium | 0.00% | no CPE yet | MLflow is an open source AI engineering platform for agents, large language models, and machine learning models. From 2.19.0 until 3.17.0, mlflow.crewai.autolog() records CrewAI Agent objects without consistently sanitizing nested credential-bearing values. mlflow/crewai/autolog.py::_get_agent_attributes() converts the Agent llm field to a string that includes the provider API key, while mlflow/crewai/autolog.py::_construct_full_inputs() expands the Agent object into mlflow.spanInputs. Applications can therefore send plaintext provider credentials to the MLflow trace backend or a configured OTLP destination, where trace readers can obtain them. This issue is fixed in version 3.17.0. | 14 h ago |
| CVE-2026-115886 | 6.5 medium | 0.00% | no CPE yet | MLflow is an open source AI engineering platform for agents, large language models, and machine learning models. From 3.12.0rc0 until 3.17.0, the scorer invoke, register, and GenAI evaluate endpoints, including unauthenticated POST /ajax-api/3.0/mlflow/scorer/invoke, accept serialized third-party scorer data that Scorer.model_validate_json resolves using a module-path allowlist without constraining the selected class or its constructor arguments. When optional trulens and trulens-providers-litellm packages are installed, an attacker can select mlflow.genai.scorers.trulens, choose a model URI outside MLflow's native provider registry so execution uses the LiteLLM fallback, and supply api_base and api_key values that cause the server to send an outbound request to an attacker-chosen or internal host with attacker-controlled headers and body. The vulnerable deserialization occurs before a tracking-store lookup or job submission. This issue is fixed in version 3.17.0. | Mon 26 Oct |
12 of 12
Download these 12 as CSV · subscribe to this list.
EPSS scores: 2026-10-28. NVD cursor: 2026-10-29 00:22. The News page shows only notable CVEs (KEV, your stack, new criticals, EPSS over 50%, or active exploitation per SSVC); this table has all of them.