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166 vulnerabilities by MLflow
CVE-2026-116692 (GCVE-0-2026-116692)
Vulnerability from nvd – Published: 2026-10-27 20:03 – Updated: 2026-10-27 20:03
VLAI
EPSS
VEX
Title
MLflow: Remote code execution on unauthenticated MLflow servers via third-party scorer deserialization importing caller-placed artifact modules
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.
Severity
9.8 (Critical)
CWE
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/mlflow/mlflow/security/advisor… | x_refsource_CONFIRM |
| https://github.com/mlflow/mlflow/pull/25906 | x_refsource_MISC |
| https://github.com/mlflow/mlflow/commit/a732443e6… | x_refsource_MISC |
| https://github.com/mlflow/mlflow/releases/tag/v3.17.0 | x_refsource_MISC |
{
"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",
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},
"references": [
{
"name": "https://github.com/mlflow/mlflow/security/advisories/GHSA-26p8-2jq9-3vq9",
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],
"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"
},
{
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"tags": [
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],
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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-26p8-2jq9-3vq9",
"discovery": "UNKNOWN"
},
"title": "MLflow: Remote code execution on unauthenticated MLflow servers via third-party scorer deserialization importing caller-placed artifact modules"
}
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CVE-2026-116691 (GCVE-0-2026-116691)
Vulnerability from nvd – Published: 2026-10-27 19:56 – Updated: 2026-10-27 19:56
VLAI
EPSS
VEX
Title
MLflow CrewAI autologging records LLM provider API keys in logged trace spans
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.
Severity
6.5 (Medium)
CWE
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/mlflow/mlflow/security/advisor… | x_refsource_CONFIRM |
| https://github.com/mlflow/mlflow/pull/25845 | x_refsource_MISC |
| https://github.com/mlflow/mlflow/commit/20927930d… | x_refsource_MISC |
| https://github.com/mlflow/mlflow/releases/tag/v3.17.0 | x_refsource_MISC |
{
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"affected": [
{
"product": "mlflow",
"vendor": "mlflow",
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{
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}
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}
],
"metrics": [
{
"cvssV3_1": {
"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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{
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},
{
"name": "https://github.com/mlflow/mlflow/releases/tag/v3.17.0",
"tags": [
"x_refsource_MISC"
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}
],
"source": {
"advisory": "GHSA-j7qq-vr8c-xvw3",
"discovery": "UNKNOWN"
},
"title": "MLflow CrewAI autologging records LLM provider API keys in logged trace spans"
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CVE-2026-116690 (GCVE-0-2026-116690)
Vulnerability from nvd – Published: 2026-10-27 19:42 – Updated: 2026-10-27 19:42
VLAI
EPSS
VEX
Title
MLflow: Arbitrary file read on unauthenticated MLflow servers via prompt model version source
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.
Severity
7.5 (High)
CWE
- CWE-22 - Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/mlflow/mlflow/security/advisor… | x_refsource_CONFIRM |
| https://github.com/mlflow/mlflow/pull/25870 | x_refsource_MISC |
| https://github.com/mlflow/mlflow/commit/81730a2ce… | x_refsource_MISC |
| https://github.com/mlflow/mlflow/releases/tag/v3.17.0 | x_refsource_MISC |
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"cna": {
"affected": [
{
"product": "mlflow",
"vendor": "mlflow",
"versions": [
{
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"version": "\u003e= 3.10.0, \u003c 3.17.0"
}
]
}
],
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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)",
"lang": "en",
"type": "CWE"
}
]
}
],
"providerMetadata": {
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{
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],
"source": {
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"discovery": "UNKNOWN"
},
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}
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CVE-2026-115886 (GCVE-0-2026-115886)
Vulnerability from nvd – Published: 2026-10-23 21:17 – Updated: 2026-10-23 21:17
VLAI
EPSS
VEX
Title
MLflow: SSRF on unauthenticated MLflow servers via third-party scorer kwargs in the scorer invoke, register, and GenAI evaluate endpoints
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.
Severity
6.5 (Medium)
CWE
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/mlflow/mlflow/security/advisor… | x_refsource_CONFIRM |
| https://github.com/mlflow/mlflow/pull/25890 | x_refsource_MISC |
| https://github.com/mlflow/mlflow/commit/bac30b414… | x_refsource_MISC |
| https://github.com/mlflow/mlflow/releases/tag/v3.17.0 | x_refsource_MISC |
{
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{
"product": "mlflow",
"vendor": "mlflow",
"versions": [
{
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"descriptions": [
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}
],
"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"
}
}
],
"problemTypes": [
{
"descriptions": [
{
"cweId": "CWE-470",
"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": {
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"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-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"
}
},
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"dateReserved": "2026-10-23T19:37:27.176Z",
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"state": "PUBLISHED"
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CVE-2026-53995 (GCVE-0-2026-53995)
Vulnerability from nvd – Published: 2026-10-19 21:05 – Updated: 2026-10-27 10:14 X_Open Source
VLAI
EPSS
VEX
Title
MLflow 3.17.1 and prior Path Traversal via Trace ID API
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.
Severity
SSVC
Exploitation: poc
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-10-19 21:36 UTC
CWE
- CWE-22 - Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://github.com/SaiTeja-Erukude/CVE-2026-53995… | technical-descriptionexploit |
| https://github.com/mlflow/mlflow/pull/26842 | issue-tracking |
| https://www.vulncheck.com/advisories/mlflow-path-… | third-party-advisory |
Impacted products
Date Public
2026-10-17 00:00
{
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CVE-2026-96804 (GCVE-0-2026-96804)
Vulnerability from nvd – Published: 2026-09-23 16:35 – Updated: 2026-09-24 03:55
VLAI
EPSS
VEX
Title
CVE-2026-96804
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.
Severity
8.8 (High)
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-23 00:00 UTC
CWE
- CWE-502 Deserialization of Untrusted Data
- CWE-502 - Deserialization of Untrusted Data
Assigner
References
1 reference
| URL | Tags |
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CVE-2026-96775 (GCVE-0-2026-96775)
Vulnerability from nvd – Published: 2026-09-23 16:28 – Updated: 2026-09-23 17:22
VLAI
EPSS
VEX
Title
MLflow dspy bypasses pickle deserialization control
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.
Severity
8.8 (High)
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-23 17:22 UTC
CWE
- CWE-502 Deserialization of Untrusted Data
- CWE-502 - Deserialization of Untrusted Data
Assigner
References
1 reference
| URL | Tags |
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CVE-2026-79721 (GCVE-0-2026-79721)
Vulnerability from nvd – Published: 2026-09-08 18:10 – Updated: 2026-09-08 18:51
VLAI
EPSS
VEX
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.
Severity
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-08 18:51 UTC
CWE
- CWE-829 - Inclusion of functionality from untrusted control sphere
Assigner
References
1 reference
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CVE-2026-69148 (GCVE-0-2026-69148)
Vulnerability from nvd – Published: 2026-08-17 21:21 – Updated: 2026-08-18 13:15
VLAI
EPSS
VEX
Title
MLflow: CreateModelVersion source validation does not check READ permission on referenced run_id
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.
Severity
7.1 (High)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-18 13:14 UTC
CWE
- CWE-862 - Missing Authorization
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/mlflow/mlflow/security/advisor… | x_refsource_CONFIRM |
| https://github.com/mlflow/mlflow/pull/24293 | x_refsource_MISC |
| https://github.com/mlflow/mlflow/commit/4bb747477… | x_refsource_MISC |
| https://github.com/mlflow/mlflow/releases/tag/v3.15.0 | x_refsource_MISC |
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CVE-2026-69146 (GCVE-0-2026-69146)
Vulnerability from nvd – Published: 2026-08-17 21:22 – Updated: 2026-08-18 18:16
VLAI
EPSS
VEX
Title
MLflow: LogInputs endpoint bypasses per-run UPDATE authorization in basic-auth
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.
Severity
6.5 (Medium)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-18 18:16 UTC
CWE
- CWE-862 - Missing Authorization
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/mlflow/mlflow/security/advisor… | x_refsource_CONFIRM |
| https://github.com/mlflow/mlflow/pull/24291 | x_refsource_MISC |
| https://github.com/mlflow/mlflow/commit/5c34aec56… | x_refsource_MISC |
| https://github.com/mlflow/mlflow/releases/tag/v3.15.0 | x_refsource_MISC |
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CVE-2026-64849 (GCVE-0-2026-64849)
Vulnerability from nvd – Published: 2026-08-17 21:16 – Updated: 2026-10-02 18:22Title
MLflow: Unauthenticated full-read SSRF in webhook delivery: _validate_webhook_url bypassed via unvalidated HTTP redirects (and DNS rebinding)
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.
Severity
9.3 (Critical)
SSVC
Exploitation: active
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-20 03:55 UTC
CWE
- CWE-918 - Server-Side Request Forgery (SSRF)
Assigner
References
6 references
| URL | Tags |
|---|---|
| https://github.com/mlflow/mlflow/security/advisor… | x_refsource_CONFIRM |
| https://github.com/mlflow/mlflow/issues/24179 | x_refsource_MISC |
| https://github.com/mlflow/mlflow/pull/24258 | x_refsource_MISC |
| https://github.com/mlflow/mlflow/commit/ba9495224… | x_refsource_MISC |
| https://github.com/mlflow/mlflow/releases/tag/v3.15.0 | x_refsource_MISC |
| https://www.cisa.gov/known-exploited-vulnerabilit… | government-resource |
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CVE-2026-71211 (GCVE-0-2026-71211)
Vulnerability from nvd – Published: 2026-08-05 06:59 – Updated: 2026-08-10 11:45
VLAI
EPSS
VEX
Title
mlflow - Unvalidated Gateway Secret api_base Enables SSRF via Gateway Proxy Endpoint
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.
Severity
7.1 (High)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-05 13:26 UTC
CWE
- CWE-918 - Server-Side Request Forgery (SSRF)
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://github.com/mlflow/mlflow/tree/v3.14.0/mlflow | third-party-advisory |
Date Public
2026-08-05 06:59
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CVE-2026-8147 (GCVE-0-2026-8147)
Vulnerability from nvd – Published: 2026-07-02 07:32 – Updated: 2026-07-02 12:28
VLAI
EPSS
VEX
Title
Authorization Bypass in mlflow/mlflow
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.
Severity
8.1 (High)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-07-02 12:28 UTC
CWE
- CWE-284 - Improper Access Control
Assigner
References
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| mlflow | mlflow/mlflow |
Affected:
unspecified , < 3.14.0
(custom)
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CVE-2026-4035 (GCVE-0-2026-4035)
Vulnerability from nvd – Published: 2026-06-03 07:18 – Updated: 2026-08-14 12:04
VLAI
EPSS
VEX
Title
Environment Variable Resolution Vulnerability in mlflow/mlflow
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.
Severity
9.1 (Critical)
7.7 (High)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-06-03 13:10 UTC
CWE
- CWE-201 - Insertion of Sensitive Information Into Sent Data
Assigner
References
5 references
| URL | Tags |
|---|---|
| https://huntr.com/bounties/f8e591a0-0f19-4910-b82… | |
| https://github.com/mlflow/mlflow/commit/4a3f2f720… | |
| https://access.redhat.com/security/cve/CVE-2026-4035 | vdb-entryx_refsource_REDHAT |
| https://bugzilla.redhat.com/show_bug.cgi?id=2484318 | issue-trackingx_refsource_REDHAT |
| https://security.access.redhat.com/data/csaf/v2/v… | x_sadp-csaf-vex |
Impacted products
2 products
| Vendor | Product | Version | |
|---|---|---|---|
| mlflow | mlflow/mlflow |
Affected:
unspecified , < 3.11.0
(custom)
|
|
| Red Hat | Red Hat OpenShift AI (RHOAI) |
cpe:/a:redhat:openshift_ai
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CVE-2026-3198 (GCVE-0-2026-3198)
Vulnerability from nvd – Published: 2026-06-02 02:50 – Updated: 2026-06-02 13:32
VLAI
EPSS
VEX
Title
Improper Access Control in mlflow/mlflow
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.
Severity
6.5 (Medium)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-06-02 13:32 UTC
CWE
- CWE-284 - Improper Access Control
Assigner
References
1 reference
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| mlflow | mlflow/mlflow |
Affected:
unspecified , ≤ latest
(custom)
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CVE-2026-116692 (GCVE-0-2026-116692)
Vulnerability from cvelistv5 – Published: 2026-10-27 20:03 – Updated: 2026-10-27 20:03
VLAI
EPSS
VEX
Title
MLflow: Remote code execution on unauthenticated MLflow servers via third-party scorer deserialization importing caller-placed artifact modules
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.
Severity
9.8 (Critical)
CWE
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/mlflow/mlflow/security/advisor… | x_refsource_CONFIRM |
| https://github.com/mlflow/mlflow/pull/25906 | x_refsource_MISC |
| https://github.com/mlflow/mlflow/commit/a732443e6… | x_refsource_MISC |
| https://github.com/mlflow/mlflow/releases/tag/v3.17.0 | x_refsource_MISC |
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CVE-2026-116691 (GCVE-0-2026-116691)
Vulnerability from cvelistv5 – Published: 2026-10-27 19:56 – Updated: 2026-10-27 19:56
VLAI
EPSS
VEX
Title
MLflow CrewAI autologging records LLM provider API keys in logged trace spans
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.
Severity
6.5 (Medium)
CWE
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/mlflow/mlflow/security/advisor… | x_refsource_CONFIRM |
| https://github.com/mlflow/mlflow/pull/25845 | x_refsource_MISC |
| https://github.com/mlflow/mlflow/commit/20927930d… | x_refsource_MISC |
| https://github.com/mlflow/mlflow/releases/tag/v3.17.0 | x_refsource_MISC |
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CVE-2026-116690 (GCVE-0-2026-116690)
Vulnerability from cvelistv5 – Published: 2026-10-27 19:42 – Updated: 2026-10-27 19:42
VLAI
EPSS
VEX
Title
MLflow: Arbitrary file read on unauthenticated MLflow servers via prompt model version source
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.
Severity
7.5 (High)
CWE
- CWE-22 - Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/mlflow/mlflow/security/advisor… | x_refsource_CONFIRM |
| https://github.com/mlflow/mlflow/pull/25870 | x_refsource_MISC |
| https://github.com/mlflow/mlflow/commit/81730a2ce… | x_refsource_MISC |
| https://github.com/mlflow/mlflow/releases/tag/v3.17.0 | x_refsource_MISC |
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CVE-2026-115886 (GCVE-0-2026-115886)
Vulnerability from cvelistv5 – Published: 2026-10-23 21:17 – Updated: 2026-10-23 21:17
VLAI
EPSS
VEX
Title
MLflow: SSRF on unauthenticated MLflow servers via third-party scorer kwargs in the scorer invoke, register, and GenAI evaluate endpoints
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.
Severity
6.5 (Medium)
CWE
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/mlflow/mlflow/security/advisor… | x_refsource_CONFIRM |
| https://github.com/mlflow/mlflow/pull/25890 | x_refsource_MISC |
| https://github.com/mlflow/mlflow/commit/bac30b414… | x_refsource_MISC |
| https://github.com/mlflow/mlflow/releases/tag/v3.17.0 | x_refsource_MISC |
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CVE-2026-53995 (GCVE-0-2026-53995)
Vulnerability from cvelistv5 – Published: 2026-10-19 21:05 – Updated: 2026-10-27 10:14 X_Open Source
VLAI
EPSS
VEX
Title
MLflow 3.17.1 and prior Path Traversal via Trace ID API
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.
Severity
SSVC
Exploitation: poc
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-10-19 21:36 UTC
CWE
- CWE-22 - Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://github.com/SaiTeja-Erukude/CVE-2026-53995… | technical-descriptionexploit |
| https://github.com/mlflow/mlflow/pull/26842 | issue-tracking |
| https://www.vulncheck.com/advisories/mlflow-path-… | third-party-advisory |
Impacted products
Date Public
2026-10-17 00:00
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CVE-2026-96804 (GCVE-0-2026-96804)
Vulnerability from cvelistv5 – Published: 2026-09-23 16:35 – Updated: 2026-09-24 03:55
VLAI
EPSS
VEX
Title
CVE-2026-96804
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.
Severity
8.8 (High)
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-23 00:00 UTC
CWE
- CWE-502 Deserialization of Untrusted Data
- CWE-502 - Deserialization of Untrusted Data
Assigner
References
1 reference
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CVE-2026-96775 (GCVE-0-2026-96775)
Vulnerability from cvelistv5 – Published: 2026-09-23 16:28 – Updated: 2026-09-23 17:22
VLAI
EPSS
VEX
Title
MLflow dspy bypasses pickle deserialization control
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.
Severity
8.8 (High)
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-23 17:22 UTC
CWE
- CWE-502 Deserialization of Untrusted Data
- CWE-502 - Deserialization of Untrusted Data
Assigner
References
1 reference
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CVE-2026-79721 (GCVE-0-2026-79721)
Vulnerability from cvelistv5 – Published: 2026-09-08 18:10 – Updated: 2026-09-08 18:51
VLAI
EPSS
VEX
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.
Severity
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-08 18:51 UTC
CWE
- CWE-829 - Inclusion of functionality from untrusted control sphere
Assigner
References
1 reference
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CVE-2026-69146 (GCVE-0-2026-69146)
Vulnerability from cvelistv5 – Published: 2026-08-17 21:22 – Updated: 2026-08-18 18:16
VLAI
EPSS
VEX
Title
MLflow: LogInputs endpoint bypasses per-run UPDATE authorization in basic-auth
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.
Severity
6.5 (Medium)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-18 18:16 UTC
CWE
- CWE-862 - Missing Authorization
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/mlflow/mlflow/security/advisor… | x_refsource_CONFIRM |
| https://github.com/mlflow/mlflow/pull/24291 | x_refsource_MISC |
| https://github.com/mlflow/mlflow/commit/5c34aec56… | x_refsource_MISC |
| https://github.com/mlflow/mlflow/releases/tag/v3.15.0 | x_refsource_MISC |
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CVE-2026-69148 (GCVE-0-2026-69148)
Vulnerability from cvelistv5 – Published: 2026-08-17 21:21 – Updated: 2026-08-18 13:15
VLAI
EPSS
VEX
Title
MLflow: CreateModelVersion source validation does not check READ permission on referenced run_id
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.
Severity
7.1 (High)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-18 13:14 UTC
CWE
- CWE-862 - Missing Authorization
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/mlflow/mlflow/security/advisor… | x_refsource_CONFIRM |
| https://github.com/mlflow/mlflow/pull/24293 | x_refsource_MISC |
| https://github.com/mlflow/mlflow/commit/4bb747477… | x_refsource_MISC |
| https://github.com/mlflow/mlflow/releases/tag/v3.15.0 | x_refsource_MISC |
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CVE-2026-64849 (GCVE-0-2026-64849)
Vulnerability from cvelistv5 – Published: 2026-08-17 21:16 – Updated: 2026-10-02 18:22Title
MLflow: Unauthenticated full-read SSRF in webhook delivery: _validate_webhook_url bypassed via unvalidated HTTP redirects (and DNS rebinding)
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.
Severity
9.3 (Critical)
SSVC
Exploitation: active
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-20 03:55 UTC
CWE
- CWE-918 - Server-Side Request Forgery (SSRF)
Assigner
References
6 references
| URL | Tags |
|---|---|
| https://github.com/mlflow/mlflow/security/advisor… | x_refsource_CONFIRM |
| https://github.com/mlflow/mlflow/issues/24179 | x_refsource_MISC |
| https://github.com/mlflow/mlflow/pull/24258 | x_refsource_MISC |
| https://github.com/mlflow/mlflow/commit/ba9495224… | x_refsource_MISC |
| https://github.com/mlflow/mlflow/releases/tag/v3.15.0 | x_refsource_MISC |
| https://www.cisa.gov/known-exploited-vulnerabilit… | government-resource |
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CVE-2026-71211 (GCVE-0-2026-71211)
Vulnerability from cvelistv5 – Published: 2026-08-05 06:59 – Updated: 2026-08-10 11:45
VLAI
EPSS
VEX
Title
mlflow - Unvalidated Gateway Secret api_base Enables SSRF via Gateway Proxy Endpoint
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.
Severity
7.1 (High)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-05 13:26 UTC
CWE
- CWE-918 - Server-Side Request Forgery (SSRF)
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://github.com/mlflow/mlflow/tree/v3.14.0/mlflow | third-party-advisory |
Date Public
2026-08-05 06:59
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CVE-2026-8147 (GCVE-0-2026-8147)
Vulnerability from cvelistv5 – Published: 2026-07-02 07:32 – Updated: 2026-07-02 12:28
VLAI
EPSS
VEX
Title
Authorization Bypass in mlflow/mlflow
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.
Severity
8.1 (High)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-07-02 12:28 UTC
CWE
- CWE-284 - Improper Access Control
Assigner
References
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| mlflow | mlflow/mlflow |
Affected:
unspecified , < 3.14.0
(custom)
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CVE-2026-4035 (GCVE-0-2026-4035)
Vulnerability from cvelistv5 – Published: 2026-06-03 07:18 – Updated: 2026-08-14 12:04
VLAI
EPSS
VEX
Title
Environment Variable Resolution Vulnerability in mlflow/mlflow
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.
Severity
9.1 (Critical)
7.7 (High)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-06-03 13:10 UTC
CWE
- CWE-201 - Insertion of Sensitive Information Into Sent Data
Assigner
References
5 references
| URL | Tags |
|---|---|
| https://huntr.com/bounties/f8e591a0-0f19-4910-b82… | |
| https://github.com/mlflow/mlflow/commit/4a3f2f720… | |
| https://access.redhat.com/security/cve/CVE-2026-4035 | vdb-entryx_refsource_REDHAT |
| https://bugzilla.redhat.com/show_bug.cgi?id=2484318 | issue-trackingx_refsource_REDHAT |
| https://security.access.redhat.com/data/csaf/v2/v… | x_sadp-csaf-vex |
Impacted products
2 products
| Vendor | Product | Version | |
|---|---|---|---|
| mlflow | mlflow/mlflow |
Affected:
unspecified , < 3.11.0
(custom)
|
|
| Red Hat | Red Hat OpenShift AI (RHOAI) |
cpe:/a:redhat:openshift_ai
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CVE-2026-3198 (GCVE-0-2026-3198)
Vulnerability from cvelistv5 – Published: 2026-06-02 02:50 – Updated: 2026-06-02 13:32
VLAI
EPSS
VEX
Title
Improper Access Control in mlflow/mlflow
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.
Severity
6.5 (Medium)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-06-02 13:32 UTC
CWE
- CWE-284 - Improper Access Control
Assigner
References
1 reference
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| mlflow | mlflow/mlflow |
Affected:
unspecified , ≤ latest
(custom)
|
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