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83 vulnerabilities found for by MLflow
CVE-2026-116692 (GCVE-0-2026-116692)
Vulnerability from cvelistv5
Published
2026-10-27 20:03
Modified
2026-10-27 20:03
Severity ?
VLAI Severity ?
EPSS score ?
CWE
Summary
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.
References
| URL | Tags | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
||||||||||||||
{
"containers": {
"cna": {
"affected": [
{
"product": "mlflow",
"vendor": "mlflow",
"versions": [
{
"status": "affected",
"version": "\u003e= 3.12.0rc0, \u003c 3.17.0"
}
]
}
],
"descriptions": [
{
"lang": "en",
"value": "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."
}
],
"metrics": [
{
"cvssV3_1": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 9.8,
"baseSeverity": "CRITICAL",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"version": "3.1"
}
}
],
"problemTypes": [
{
"descriptions": [
{
"cweId": "CWE-94",
"description": "CWE-94: Improper Control of Generation of Code (\u0027Code Injection\u0027)",
"lang": "en",
"type": "CWE"
}
]
},
{
"descriptions": [
{
"cweId": "CWE-470",
"description": "CWE-470: Use of Externally-Controlled Input to Select Classes or Code (\u0027Unsafe Reflection\u0027)",
"lang": "en",
"type": "CWE"
}
]
}
],
"providerMetadata": {
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"orgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
"shortName": "GitHub_M"
},
"references": [
{
"name": "https://github.com/mlflow/mlflow/security/advisories/GHSA-26p8-2jq9-3vq9",
"tags": [
"x_refsource_CONFIRM"
],
"url": "https://github.com/mlflow/mlflow/security/advisories/GHSA-26p8-2jq9-3vq9"
},
{
"name": "https://github.com/mlflow/mlflow/pull/25906",
"tags": [
"x_refsource_MISC"
],
"url": "https://github.com/mlflow/mlflow/pull/25906"
},
{
"name": "https://github.com/mlflow/mlflow/commit/a732443e695c3c677680c54320efc394c81654ab",
"tags": [
"x_refsource_MISC"
],
"url": "https://github.com/mlflow/mlflow/commit/a732443e695c3c677680c54320efc394c81654ab"
},
{
"name": "https://github.com/mlflow/mlflow/releases/tag/v3.17.0",
"tags": [
"x_refsource_MISC"
],
"url": "https://github.com/mlflow/mlflow/releases/tag/v3.17.0"
}
],
"source": {
"advisory": "GHSA-26p8-2jq9-3vq9",
"discovery": "UNKNOWN"
},
"title": "MLflow: Remote code execution on unauthenticated MLflow servers via third-party scorer deserialization importing caller-placed artifact modules"
}
},
"cveMetadata": {
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"dateReserved": "2026-10-26T20:00:59.830Z",
"dateUpdated": "2026-10-27T20:03:49.661Z",
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CVE-2026-116691 (GCVE-0-2026-116691)
Vulnerability from cvelistv5
Published
2026-10-27 19:56
Modified
2026-10-27 19:56
Severity ?
VLAI Severity ?
EPSS score ?
CWE
Summary
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.
References
| URL | Tags | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
||||||||||||||
{
"containers": {
"cna": {
"affected": [
{
"product": "mlflow",
"vendor": "mlflow",
"versions": [
{
"status": "affected",
"version": "\u003e= 2.19.0, \u003c 3.17.0"
}
]
}
],
"descriptions": [
{
"lang": "en",
"value": "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."
}
],
"metrics": [
{
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"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 6.5,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "HIGH",
"integrityImpact": "NONE",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:N",
"version": "3.1"
}
}
],
"problemTypes": [
{
"descriptions": [
{
"cweId": "CWE-200",
"description": "CWE-200: Exposure of Sensitive Information to an Unauthorized Actor",
"lang": "en",
"type": "CWE"
}
]
},
{
"descriptions": [
{
"cweId": "CWE-312",
"description": "CWE-312: Cleartext Storage of Sensitive Information",
"lang": "en",
"type": "CWE"
}
]
},
{
"descriptions": [
{
"cweId": "CWE-532",
"description": "CWE-532: Insertion of Sensitive Information into Log File",
"lang": "en",
"type": "CWE"
}
]
}
],
"providerMetadata": {
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},
"references": [
{
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"tags": [
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],
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},
{
"name": "https://github.com/mlflow/mlflow/pull/25845",
"tags": [
"x_refsource_MISC"
],
"url": "https://github.com/mlflow/mlflow/pull/25845"
},
{
"name": "https://github.com/mlflow/mlflow/commit/20927930d27f0072fcfeb7f024f24ad8c5e77c47",
"tags": [
"x_refsource_MISC"
],
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},
{
"name": "https://github.com/mlflow/mlflow/releases/tag/v3.17.0",
"tags": [
"x_refsource_MISC"
],
"url": "https://github.com/mlflow/mlflow/releases/tag/v3.17.0"
}
],
"source": {
"advisory": "GHSA-j7qq-vr8c-xvw3",
"discovery": "UNKNOWN"
},
"title": "MLflow CrewAI autologging records LLM provider API keys in logged trace spans"
}
},
"cveMetadata": {
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"assignerShortName": "GitHub_M",
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"datePublished": "2026-10-27T19:56:22.083Z",
"dateReserved": "2026-10-26T20:00:59.830Z",
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"state": "PUBLISHED"
},
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"dataVersion": "5.2"
}
CVE-2026-116690 (GCVE-0-2026-116690)
Vulnerability from cvelistv5
Published
2026-10-27 19:42
Modified
2026-10-28 16:13
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-22 - Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')
Summary
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.
References
| URL | Tags | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
||||||||||||||
{
"containers": {
"adp": [
{
"metrics": [
{
"other": {
"content": {
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},
{
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},
{
"Technical Impact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2026-10-28T15:54:25.087287Z",
"version": "2.0.3"
},
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}
}
],
"providerMetadata": {
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"orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"shortName": "CISA-ADP"
},
"title": "CISA ADP Vulnrichment"
}
],
"cna": {
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{
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"vendor": "mlflow",
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}
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}
],
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}
],
"metrics": [
{
"cvssV3_1": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 7.5,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N",
"version": "3.1"
}
}
],
"problemTypes": [
{
"descriptions": [
{
"cweId": "CWE-22",
"description": "CWE-22: Improper Limitation of a Pathname to a Restricted Directory (\u0027Path Traversal\u0027)",
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"type": "CWE"
}
]
}
],
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],
"url": "https://github.com/mlflow/mlflow/security/advisories/GHSA-m65p-9j7q-6w33"
},
{
"name": "https://github.com/mlflow/mlflow/pull/25870",
"tags": [
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},
{
"name": "https://github.com/mlflow/mlflow/commit/81730a2ce4ebcd60d5d18f68c96f422ad7e30772",
"tags": [
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{
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"tags": [
"x_refsource_MISC"
],
"url": "https://github.com/mlflow/mlflow/releases/tag/v3.17.0"
}
],
"source": {
"advisory": "GHSA-m65p-9j7q-6w33",
"discovery": "UNKNOWN"
},
"title": "MLflow: Arbitrary file read on unauthenticated MLflow servers via prompt model version source"
}
},
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"dateUpdated": "2026-10-28T16:13:38.096Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.2"
}
CVE-2026-115886 (GCVE-0-2026-115886)
Vulnerability from cvelistv5
Published
2026-10-23 21:17
Modified
2026-10-23 21:17
Severity ?
VLAI Severity ?
EPSS score ?
CWE
Summary
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.
References
| URL | Tags | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
||||||||||||||
{
"containers": {
"cna": {
"affected": [
{
"product": "mlflow",
"vendor": "mlflow",
"versions": [
{
"status": "affected",
"version": "\u003e= 3.12.0rc0, \u003c 3.17.0"
}
]
}
],
"descriptions": [
{
"lang": "en",
"value": "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\u0027s 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."
}
],
"metrics": [
{
"cvssV3_1": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 6.5,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "LOW",
"integrityImpact": "LOW",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:N",
"version": "3.1"
}
}
],
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"description": "CWE-470: Use of Externally-Controlled Input to Select Classes or Code (\u0027Unsafe Reflection\u0027)",
"lang": "en",
"type": "CWE"
}
]
},
{
"descriptions": [
{
"cweId": "CWE-918",
"description": "CWE-918: Server-Side Request Forgery (SSRF)",
"lang": "en",
"type": "CWE"
}
]
}
],
"providerMetadata": {
"dateUpdated": "2026-10-23T21:17:21.462Z",
"orgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
"shortName": "GitHub_M"
},
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{
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"tags": [
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],
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{
"name": "https://github.com/mlflow/mlflow/commit/bac30b4148c4f40045b4d4db07ae60ab3838803f",
"tags": [
"x_refsource_MISC"
],
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},
{
"name": "https://github.com/mlflow/mlflow/releases/tag/v3.17.0",
"tags": [
"x_refsource_MISC"
],
"url": "https://github.com/mlflow/mlflow/releases/tag/v3.17.0"
}
],
"source": {
"advisory": "GHSA-2wqp-q3c6-ccj5",
"discovery": "UNKNOWN"
},
"title": "MLflow: SSRF on unauthenticated MLflow servers via third-party scorer kwargs in the scorer invoke, register, and GenAI evaluate endpoints"
}
},
"cveMetadata": {
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"cveId": "CVE-2026-115886",
"datePublished": "2026-10-23T21:17:21.462Z",
"dateReserved": "2026-10-23T19:37:27.176Z",
"dateUpdated": "2026-10-23T21:17:21.462Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.2"
}
CVE-2026-53995 (GCVE-0-2026-53995)
Vulnerability from cvelistv5
Published
2026-10-19 21:05
Modified
2026-10-27 10:14
Severity ?
8.8 (High) - CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:H/VA:L/SC:N/SI:N/SA:N
8.2 (High) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:L
8.2 (High) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:L
VLAI Severity ?
EPSS score ?
CWE
- CWE-22 - Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')
Summary
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.
References
| URL | Tags | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
|||||||||||
{
"containers": {
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{
"metrics": [
{
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},
{
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},
{
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}
],
"role": "CISA Coordinator",
"timestamp": "2026-10-19T21:36:41.493578Z",
"version": "2.0.3"
},
"type": "ssvc"
}
}
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"orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"shortName": "CISA-ADP"
},
"title": "CISA ADP Vulnrichment"
}
],
"cna": {
"affected": [
{
"defaultStatus": "unknown",
"packageURL": "pkg:github/mlflow/mlflow",
"product": "mlflow",
"repo": "https://github.com/mlflow/mlflow",
"vendor": "mlflow",
"versions": [
{
"lessThanOrEqual": "3.17.1",
"status": "affected",
"version": "0",
"versionType": "semver"
}
]
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CVE-2026-96804 (GCVE-0-2026-96804)
Vulnerability from cvelistv5
Published
2026-09-23 16:35
Modified
2026-09-24 03:55
Severity ?
VLAI Severity ?
EPSS score ?
Summary
MLflow's statsmodel flavor, versions 2.1.0 to 3.14.0, omits the MLFLOW_ALLOW_PICKLE_DESERIALIZATION=False security control entirely in _load_model(), which allows a remote attacker to execute arbitrary code via a crafted MLmodel artifact.
References
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CVE-2026-96775 (GCVE-0-2026-96775)
Vulnerability from cvelistv5
Published
2026-09-23 16:28
Modified
2026-09-23 17:22
Severity ?
VLAI Severity ?
EPSS score ?
Summary
MLflow's dspy flavor, versions >= 2.0, applies the MLFLOW_ALLOW_PICKLE_DESERIALIZATION=False security control only when the model_path ends in .pkl, which allows a remote attacker to execute arbitrary code via a crafted MLmodel artifact.
References
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CVE-2026-79721 (GCVE-0-2026-79721)
Vulnerability from cvelistv5
Published
2026-09-08 18:10
Modified
2026-09-08 18:51
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-829 - Inclusion of functionality from untrusted control sphere
Summary
Code execution can occur in versions of the MLflow platform running version 0.0.1 or newer, enabling a maliciously crafted model artifact to execute arbitrary code on an end user's system when loaded by the project.
References
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CVE-2026-69146 (GCVE-0-2026-69146)
Vulnerability from cvelistv5
Published
2026-08-17 21:22
Modified
2026-08-18 18:16
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-862 - Missing Authorization
Summary
MLflow is an open source AI engineering platform for agents, large language models, and machine learning models. From 3.13.0 until 3.15.0, LogInputs is absent from BEFORE_REQUEST_HANDLERS in the mlflow/server/auth package, allowing any authenticated user to call POST /api/2.0/mlflow/runs/log-inputs for another user's run_id and inject attacker-controlled DatasetInput records into the dataset_inputs lineage metadata without UPDATE permission. This issue is fixed in version 3.15.0.
References
| URL | Tags | |||||||||||||
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CVE-2026-69148 (GCVE-0-2026-69148)
Vulnerability from cvelistv5
Published
2026-08-17 21:21
Modified
2026-08-18 13:15
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-862 - Missing Authorization
Summary
MLflow is an open source AI engineering platform for agents, large language models, and machine learning models. Prior to 3.15.0, CreateModelVersion accepts a run_id or model_id after _validate_source_run() or _validate_source_model() in mlflow/server/handlers.py verifies only path containment, allowing authenticated users to create a model version that references another user's artifact directory and read files through GET /model-versions/get-artifact without the required READ permission. This issue is fixed in version 3.15.0.
References
| URL | Tags | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
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CVE-2026-64849 (GCVE-0-2026-64849)
Vulnerability from cvelistv5
Published
2026-08-17 21:16
Modified
2026-10-02 18:22
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-918 - Server-Side Request Forgery (SSRF)
Summary
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.
References
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CVE-2026-71211 (GCVE-0-2026-71211)
Vulnerability from cvelistv5
Published
2026-08-05 06:59
Modified
2026-08-10 11:45
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-918 - Server-Side Request Forgery (SSRF)
Summary
MLflow's AI Gateway accepts an auth_config.api_base value when creating a gateway secret (mlflow/server/handlers.py, _create_gateway_secret) with no validation of scheme, host, or IP range; the value is stored verbatim. The gateway proxy endpoint (mlflow/server/gateway_api.py, raw_proxy) subsequently issues an HTTP request to that stored api_base plus a caller-supplied path and returns the full response body.
References
| URL | Tags | ||||
|---|---|---|---|---|---|
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CVE-2026-8147 (GCVE-0-2026-8147)
Vulnerability from cvelistv5
Published
2026-07-02 07:32
Modified
2026-07-02 12:28
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-284 - Improper Access Control
Summary
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.
References
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| mlflow | mlflow/mlflow |
Version: unspecified < 3.14.0 |
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CVE-2026-4035 (GCVE-0-2026-4035)
Vulnerability from cvelistv5
Published
2026-06-03 07:18
Modified
2026-08-14 12:04
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-201 - Insertion of Sensitive Information Into Sent Data
Summary
A vulnerability in mlflow/mlflow versions prior to 3.11.0 allows for the resolution of environment variables in AI Gateway secrets, which can be exploited to exfiltrate sensitive server-side environment credentials to an attacker-controlled endpoint. This issue arises because the `api_key` field in gateway secrets can accept `$ENV_VAR` references, which are resolved against the MLflow server's environment during runtime. The resolved secrets are then sent in provider authentication headers to the configured upstream `api_base`. This vulnerability can be exploited by low-privileged authenticated users in basic-auth deployments or by unauthenticated users in default deployments without `basic-auth`. The impact includes potential leakage of sensitive credentials such as cloud artifact credentials (`AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`), which could lead to artifact poisoning and cross-boundary code execution in downstream environments. The issue is fixed in version 3.11.0.
References
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| mlflow | mlflow/mlflow |
Version: unspecified < 3.11.0 |
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CVE-2026-3198 (GCVE-0-2026-3198)
Vulnerability from cvelistv5
Published
2026-06-02 02:50
Modified
2026-06-02 13:32
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-284 - Improper Access Control
Summary
MLflow 3.9.0 with basic-auth (`--app-name basic-auth`) fails to enforce authorization checks for multiple Gateway API 'list' endpoints. Specifically, the `BEFORE_REQUEST_HANDLERS` dictionary in `mlflow/server/auth/__init__.py` does not include entries for `ListGatewaySecretInfos`, `ListGatewayEndpoints`, and `ListGatewayModelDefinitions`. This allows any authenticated user, regardless of their assigned permissions, to enumerate all gateway secrets, endpoints, and model definitions. This vulnerability exposes sensitive information, such as API keys, endpoint configurations, and proprietary model definitions, to unauthorized users.
References
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| mlflow | mlflow/mlflow |
Version: unspecified < |
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CVE-2026-2651 (GCVE-0-2026-2651)
Vulnerability from cvelistv5
Published
2026-05-25 06:00
Modified
2026-07-15 02:49
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-862 - Missing Authorization
Summary
A vulnerability in MLflow versions <=3.10.1.dev0 allows unauthorized access to multipart upload (MPU) endpoints when the `--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. The issue is resolved in version 3.10.0.
References
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| mlflow | mlflow/mlflow |
Version: unspecified < 3.10.0 |
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CVE-2026-2734 (GCVE-0-2026-2734)
Vulnerability from cvelistv5
Published
2026-05-21 03:49
Modified
2026-05-21 12:40
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-284 - Improper Access Control
Summary
In mlflow/mlflow versions up to 3.9.0, the `SearchModelVersions` REST API endpoint and the `mlflowSearchModelVersions` GraphQL query lack proper per-model authorization checks when basic authentication is enabled. This allows any authenticated user to enumerate all model versions across all registered models, regardless of their permission level. The issue arises due to the absence of `SearchModelVersions` in the `BEFORE_REQUEST_VALIDATORS` and `AFTER_REQUEST_HANDLERS` for the REST API, and its omission from `GraphQLAuthorizationMiddleware.PROTECTED_FIELDS` for GraphQL. This vulnerability can expose sensitive information such as model names, version descriptions, source URIs, tags, and other metadata, potentially revealing proprietary or confidential details in multi-tenant environments. The issue is resolved in version 3.10.0.
References
Impacted products
| Vendor | Product | Version | ||
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CVE-2026-2611 (GCVE-0-2026-2611)
Vulnerability from cvelistv5
Published
2026-05-19 09:16
Modified
2026-07-15 02:49
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-346 - Origin Validation Error
Summary
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.
References
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CVE-2026-4137 (GCVE-0-2026-4137)
Vulnerability from cvelistv5
Published
2026-05-18 20:26
Modified
2026-05-19 12:47
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-378 - Creation of Temporary File With Insecure Permissions
Summary
In mlflow/mlflow versions prior to 3.11.0, the `get_or_create_nfs_tmp_dir()` function in `mlflow/utils/file_utils.py` creates temporary directories with world-writable permissions (0o777), and the `_create_model_downloading_tmp_dir()` function in `mlflow/pyfunc/__init__.py` creates directories with group-writable permissions (0o770). These insecure permissions allow local attackers to tamper with model artifacts, such as cloudpickle-serialized Python objects, and achieve arbitrary code execution when the tampered artifacts are deserialized via `cloudpickle.load()`. This vulnerability is particularly critical in environments with shared NFS mounts, such as Databricks, where NFS is enabled by default. The issue is a continuation of the vulnerability class addressed in CVE-2025-10279, which was only partially fixed.
References
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| mlflow | mlflow/mlflow |
Version: unspecified < 3.11.0 |
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CVE-2026-2652 (GCVE-0-2026-2652)
Vulnerability from cvelistv5
Published
2026-05-15 02:13
Modified
2026-05-15 13:22
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-305 - Authentication Bypass by Primary Weakness
Summary
A vulnerability in mlflow/mlflow versions 3.9.0 and earlier allows unauthenticated access to certain FastAPI routes when the server is started with authentication enabled (`--app-name basic-auth`) and served via uvicorn (ASGI). The FastAPI permission middleware only enforces authentication on `/gateway/` routes, leaving other routes such as the Job API (`/ajax-api/3.0/jobs/*`) and the OpenTelemetry trace ingestion API (`/v1/traces`) unprotected. This allows unauthenticated remote attackers to submit jobs, read job results, cancel running jobs, and inject arbitrary trace data into experiments. The issue arises from an architectural mismatch between Flask and FastAPI authentication mechanisms, where the `_find_fastapi_validator()` function fails to handle non-`/gateway/` paths, resulting in a complete authentication bypass. This vulnerability is fixed in version 3.10.0.
References
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| mlflow | mlflow/mlflow |
Version: unspecified < 3.10.0 |
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CVE-2026-2614 (GCVE-0-2026-2614)
Vulnerability from cvelistv5
Published
2026-05-11 19:02
Modified
2026-08-28 12:04
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-22 - Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')
Summary
A vulnerability in the `_create_model_version()` handler of `mlflow/server/handlers.py` in mlflow/mlflow versions 3.9.0 and earlier allows an unauthenticated remote attacker to read arbitrary files from the server's filesystem. The issue arises when a `CreateModelVersion` request includes the tag `mlflow.prompt.is_prompt`, which bypasses source path validation. This enables an attacker to store an arbitrary local filesystem path as the model version source. The `get_model_version_artifact_handler()` function later uses this source to serve files without verifying the model version's prompt status, leading to a complete confidentiality compromise. This issue is fixed in version 3.10.0.
References
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| mlflow | mlflow/mlflow |
Version: unspecified < 3.10.0 |
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CVE-2026-2393 (GCVE-0-2026-2393)
Vulnerability from cvelistv5
Published
2026-05-11 16:30
Modified
2026-05-11 18:52
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-918 - Server-Side Request Forgery (SSRF)
Summary
A Server-Side Request Forgery (SSRF) vulnerability exists in MLflow versions prior to 3.9.0. The `_create_webhook()` function in `mlflow/server/handlers.py` accepts a user-controlled `url` parameter without validation, and the `_send_webhook_request()` function in `mlflow/webhooks/delivery.py` sends HTTP POST requests to this attacker-controlled URL. This allows an authenticated attacker to force the MLflow backend to send HTTP requests to internal services, cloud metadata endpoints, or arbitrary external servers. The lack of input sanitization, URL scheme filtering, or allowlist validation on the webhook URL enables exploitation, potentially leading to cloud credential theft, internal network access, and data exfiltration.
References
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
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Version: unspecified < 3.10.0 |
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CVE-2026-33866 (GCVE-0-2026-33866)
Vulnerability from cvelistv5
Published
2026-04-07 12:57
Modified
2026-04-14 15:12
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-862 - Missing Authorization
Summary
MLflow is vulnerable to an authorization bypass affecting the AJAX endpoint used to download saved model artifacts. Due to missing access‑control validation, a user without permissions to a given experiment can directly query this endpoint and retrieve model artifacts they are not authorized to access.
This issue affects MLflow version through 3.10.1
References
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CVE-2026-33865 (GCVE-0-2026-33865)
Vulnerability from cvelistv5
Published
2026-04-07 12:57
Modified
2026-04-14 15:13
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-79 - Improper Neutralization of Input During Web Page Generation (XSS or 'Cross-site Scripting')
Summary
MLflow is vulnerable to Stored Cross-Site Scripting (XSS) caused by unsafe parsing of YAML-based MLmodel artifacts in its web interface. An authenticated attacker can upload a malicious MLmodel file containing a payload that executes when another user views the artifact in the UI. This allows actions such as session hijacking or performing operations on behalf of the victim.
This issue affects MLflow version through 3.10.1
References
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CVE-2026-0545 (GCVE-0-2026-0545)
Vulnerability from cvelistv5
Published
2026-04-03 17:03
Modified
2026-08-28 12:04
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-306 - Missing Authentication for Critical Function
Summary
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. This vulnerability affects the latest version of the repository. If job execution is enabled (`MLFLOW_SERVER_ENABLE_JOB_EXECUTION=true`) and any job function is allowlisted, any network client can submit, read, search, and cancel jobs without credentials, bypassing basic-auth entirely. This can lead to unauthenticated remote code execution if allowed jobs perform privileged actions such as shell execution or filesystem changes. Even if jobs are deemed safe, this still constitutes an authentication bypass, potentially resulting in job spam, denial of service (DoS), or data exposure in job results.
References
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| mlflow | mlflow/mlflow |
Version: unspecified < |
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CVE-2026-0596 (GCVE-0-2026-0596)
Vulnerability from cvelistv5
Published
2026-03-31 14:25
Modified
2026-04-01 03:55
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-78 - Improper Neutralization of Special Elements used in an OS Command
Summary
A command injection vulnerability exists in mlflow/mlflow when serving a model with `enable_mlserver=True`. The `model_uri` is embedded directly into a shell command executed via `bash -c` without proper sanitization. If the `model_uri` contains shell metacharacters, such as `$()` or backticks, it allows for command substitution and execution of attacker-controlled commands. This vulnerability affects the latest version of mlflow/mlflow and can lead to privilege escalation if a higher-privileged service serves models from a directory writable by lower-privileged users.
References
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| mlflow | mlflow/mlflow |
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CVE-2025-15379 (GCVE-0-2025-15379)
Vulnerability from cvelistv5
Published
2026-03-30 07:16
Modified
2026-09-07 12:05
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-77 - Improper Neutralization of Special Elements used in a Command ('Command Injection')
Summary
A command injection vulnerability exists in MLflow's model serving container initialization code, specifically in the `_install_model_dependencies_to_env()` function. When deploying a model with `env_manager=LOCAL`, MLflow reads dependency specifications from the model artifact's `python_env.yaml` file and directly interpolates them into a shell command without sanitization. This allows an attacker to supply a malicious model artifact and achieve arbitrary command execution on systems that deploy the model. The vulnerability affects versions 3.8.0 and is fixed in version 3.8.2.
References
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| mlflow | mlflow/mlflow |
Version: unspecified < 3.8.2 |
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CVE-2025-15036 (GCVE-0-2025-15036)
Vulnerability from cvelistv5
Published
2026-03-30 01:16
Modified
2026-07-15 01:27
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-29 - Path Traversal: '\..\filename'
Summary
A path traversal vulnerability exists in the `extract_archive_to_dir` function within the `mlflow/pyfunc/dbconnect_artifact_cache.py` file of the mlflow/mlflow repository. This vulnerability, present in versions before v3.7.0, arises due to the lack of validation of tar member paths during extraction. An attacker with control over the tar.gz file can exploit this issue to overwrite arbitrary files or gain elevated privileges, potentially escaping the sandbox directory in multi-tenant or shared cluster environments.
References
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| mlflow | mlflow/mlflow |
Version: unspecified < 3.9.0 |
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CVE-2025-15381 (GCVE-0-2025-15381)
Vulnerability from cvelistv5
Published
2026-03-27 16:17
Modified
2026-07-15 01:26
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-200 - Exposure of Sensitive Information to an Unauthorized Actor
Summary
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.
References
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| mlflow | mlflow/mlflow |
Version: unspecified < |
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CVE-2025-15031 (GCVE-0-2025-15031)
Vulnerability from cvelistv5
Published
2026-03-18 22:06
Modified
2026-07-15 01:27
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-22 - Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')
Summary
A vulnerability in MLflow's pyfunc extraction process allows for arbitrary file writes due to improper handling of tar archive entries. Specifically, the use of `tarfile.extractall` without path validation enables crafted tar.gz files containing `..` or absolute paths to escape the intended extraction directory. This issue affects the latest version of MLflow and poses a high/critical risk in scenarios involving multi-tenant environments or ingestion of untrusted artifacts, as it can lead to arbitrary file overwrites and potential remote code execution.
References
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| mlflow | mlflow/mlflow |
Version: unspecified < |
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