{"answers":{"affected":"2 open source packages ship an affected version, including mlflow.","exploited":"There is no known exploitation of GHSA-83fm-w79m-64r5 in the wild and no public exploit code.","fix":"Upgrade mlflow to 2.3.1 or later.","what":"GHSA-83fm-w79m-64r5 is a vulnerability in mlflow, published on 1 May 2023."},"fix_prompt":"Check this codebase for GHSA-83fm-w79m-64r5 and fix it if it is affected.\n\nWhat it is: GHSA-83fm-w79m-64r5 is a vulnerability in mlflow, published on 1 May 2023. \nExploitation: There is no known exploitation of GHSA-83fm-w79m-64r5 in the wild and no public exploit code.\n\nAffected packages and fixed versions:\n- mlflow (PyPI): affected all versions; fixed in 2.3.1\n- mlflow (PyPI): affected all versions; fixed in 2.3.1\n\nTask:\n1. Search the dependency manifests and lockfiles (for example package.json, package-lock.json, requirements.txt, poetry.lock, uv.lock, pom.xml, build.gradle, go.mod, Cargo.lock, Gemfile.lock) and any Dockerfiles for the affected packages, including transitive dependencies.\n2. If an affected version is in use, upgrade it to the fixed version for its release line with the project's package manager, so the lockfile is regenerated. Prefer the smallest upgrade that fixes it.\n3. If there is no fix, or the upgrade would break the build, do not guess: explain the risk and the safest workaround instead.\n4. Run the tests and summarise what changed and why.\n5. If nothing in the project is affected, say so and change nothing.\n\nReference: https://insights.sbomify.com/GHSA-83fm-w79m-64r5/ (Markdown: https://insights.sbomify.com/GHSA-83fm-w79m-64r5/index.md)","id":"GHSA-83fm-w79m-64r5","markdown":"https://insights.sbomify.com/GHSA-83fm-w79m-64r5/index.md","name":"GHSA-83fm-w79m-64r5","record":{"aliases":[],"analysis":null,"analysis_generated_by":null,"assigner":null,"cpe":[],"cvss":{"score":null,"severity":null,"source":null,"vector":null,"version":null},"cwes":[],"description":"### Impact\n\nUsers of the MLflow Open Source Project who are hosting the MLflow Model Registry using the ``mlflow server`` or ``mlflow ui`` commands using an MLflow version older than **MLflow 2.3.1** may be vulnerable to a remote file access exploit if they are not limiting who can query their server (for example, by using a cloud VPC, an IP allowlist for inbound requests, or authentication / authorization middleware).\n\nThis issue only affects users and integrations that run the ``mlflow server`` and ``mlflow ui`` commands. Integrations that do not make use of ``mlflow server`` or ``mlflow ui`` are unaffected; for example, the Databricks Managed MLflow product and MLflow on Azure Machine Learning do not make use of these commands and are not impacted by these vulnerabilities in any way.\n\nThe vulnerability is very similar to https://nvd.nist.gov/vuln/detail/CVE-2023-1177, and a separate CVE will be published and updated here shortly.\n\n### Patches\n\nThis vulnerability has been patched in MLflow 2.3.1, which was released to PyPI on April 27th, 2023. If you are using ``mlflow server`` or ``mlflow ui`` with the MLflow Model Registry, we recommend upgrading to MLflow 2.3.1 as soon as possible.\n\n### Workarounds\nIf you are using the MLflow open source ``mlflow server`` or ``mlflow ui`` commands, we strongly recommend limiting who can access your MLflow Model Registry and MLflow Tracking servers using a cloud VPC, an IP allowlist for inbound requests, authentication / authorization middleware, or another access restriction mechanism of your choosing.\n\nIf you are using the MLflow open source ``mlflow server`` or ``mlflow ui`` commands, we also strongly recommend limiting the remote files to which your MLflow Model Registry and MLflow Tracking servers have access. For example, if your MLflow Model Registry or MLflow Tracking server uses cloud-hosted blob storage for MLflow artifacts, make sure to restrict the scope of your server's cloud credentials such that it can only access files and directories related to MLflow.\n\n### References","ecosystems":["PyPI"],"exploits":{"exploitdb":null,"first_seen":null,"github_poc":null,"max_poc_stars":null,"metasploit":null,"nuclei":null,"refs":[],"tier":null},"has_fix":true,"headline":"Remote file access vulnerability in `mlflow server` and `mlflow ui` CLIs","id":"GHSA-83fm-w79m-64r5","indexable":true,"kev":null,"kind":"advisory","links":{"ecosystem":"PyPI","product":null,"products":[],"type":null,"year":"2023"},"n_packages":2,"n_references":null,"packages":[{"ecosystem":"PyPI","fixed":["2.3.1"],"introduced":["0"],"last_affected":[],"package":"mlflow","purl":null,"source":"GHSA-83fm-w79m-64r5"},{"ecosystem":"PyPI","fixed":["2.3.1"],"introduced":["0"],"last_affected":[],"package":"mlflow","purl":"pkg:pypi/mlflow","source":"GHSA-83fm-w79m-64r5"}],"products":null,"published":"2023-05-01","related":[],"sources":[{"class":"attribution","licence":"CC BY 4.0","name":"GitHub Advisory Database","prefix":"GHSA"}],"ssvc":{"automatable":null,"exploitation":null,"technical_impact":null},"state":null,"updated":null,"vendors":null},"summary":null,"url":"https://insights.sbomify.com/GHSA-83fm-w79m-64r5/"}