CVE-2026-53995
HighCVSS 8.2Exploitation Probability (EPSS)
Low risk49th percentile - higher than 49% of all known CVEs
Summary
MLflow 3.17.1 and earlier contains a path traversal vulnerability in the FileStore tracking backend. An unauthenticated attacker can submit a crafted trace ID containing traversal sequences to the POST /api/3.0/mlflow/traces endpoint, causing trace_info.yaml, request_metadata and tag files to be written outside the intended experiment trace directory (within the server process's filesystem permissions). FileStore was the default backend before MLflow 3.7.0 and is opt-in thereafter.
Risk Assessment
An unauthenticated attacker can write attacker-controlled files to unexpected filesystem locations, potentially overwriting data or enabling further escalation. Deployments using FileStore as the tracking backend are vulnerable without special configuration.
Recommendation
Upgrade MLflow to a version later than 3.17.1 or switch the tracking backend to SQLite (default since 3.7.0). If FileStore is required, restrict the server process's filesystem permissions and protect the tracking endpoint from unauthenticated access.
Other vulnerabilities in MLflow
See all- CVE-2026-2393High
A Server-Side Request Forgery (SSRF) vulnerability exists in MLflow versions prior to 3.9.0. The `_create_webhook()` function accepts a user-controlled `url` parameter without validation, allowing an attacker to force the MLflow backend to send HTTP requests to internal services or arbitrary external servers.
- CVE-2026-116692Critical
In MLflow from 3.12.0rc0 until 3.17.0, third-party scorer deserialization in mlflow/genai/scorers/base.py accepts request-selected descendants of allowed modules rather than requiring an exact allowed module. An attacker can place a Python module under a writable allowed package and invoke it via /ajax-api/3.0/mlflow/scorer/invoke, leading to code execution. Version 3.17.0 fixes the issue.
- CVE-2026-64849CriticalActively exploited
MLflow versions 3.3.0 through 3.15.0 contain an SSRF vulnerability in the unauthenticated POST /api/2.0/mlflow/webhooks/{id}/test endpoint. URL validation is only performed on the original URL, while the webhook delivery mechanism follows redirects and re-resolves the hostname without pinning the validated address. Attackers can thereby reach internal or cloud metadata services and read response_status and response_body.
- CVE-2026-2651Critical
A vulnerability in MLflow versions up to 3.10.1.dev0 allows unauthorized access to multipart upload (MPU) endpoints when `--serve-artifacts` mode is enabled. The authorization logic does not enforce resource-level permission checks for `/mlflow-artifacts/mpu/*` endpoints, enabling attackers to overwrite artifacts belonging to other users. This can lead to unauthorized cross-user writes, model supply chain poisoning, and arbitrary code execution when compromised models are loaded.
- CVE-2026-2611Critical
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.
- CVE-2026-0545Critical
In mlflow/mlflow, the FastAPI job endpoints under `/ajax-api/3.0/jobs/*` are not protected by authentication or authorization when the `basic-auth` app is enabled. Even with basic authentication enabled, any network client can submit, read, search, and cancel jobs without credentials.
- CVE-2025-15379Critical
A command injection vulnerability exists in MLflow version 3.8.0 in the model serving container initialization code. The `_install_model_dependencies_to_env()` function directly interpolates dependencies from the `python_env.yaml` file into a shell command without sanitization, allowing an attacker to execute arbitrary commands by supplying a malicious model artifact.
- CVE-2025-15036Critical
A path traversal vulnerability exists in the `extract_archive_to_dir` function of mlflow before version 3.7.0. Lack of tar member path validation allows an attacker to overwrite arbitrary files or escalate privileges.
- CVE-2025-15031Critical
A vulnerability in MLflow's pyfunc extraction process allows arbitrary file writes due to improper handling of tar archive entries. The use of `tarfile.extractall` without path validation enables crafted tar.gz files containing `..` or absolute paths to escape the intended extraction directory.
- CVE-2025-11200Critical
MLflow contains a weak password requirements vulnerability that allows authentication bypass. Authentication is not required to exploit it; the flaw results from weak password requirements and can be leveraged to bypass authentication on the system.
Original NVD description (English source)
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.
Vulnerability data from NVD (NIST) · CISA KEV · EPSS