fix(deps): update dependency mlflow to v2.9.2 [security] #27
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This PR contains the following updates:
2.1.1->2.9.2==1.26.1->==2.9.2==1.25.1->==2.9.2GitHub Vulnerability Alerts
CVE-2023-1176
Impact
Users of the MLflow Open Source Project who are hosting the MLflow Model Registry using the
mlflow serverormlflow uicommands using an MLflow version older than MLflow 2.2.1 may be vulnerable to a remote file existence check exploit if they are not limiting who can query their server (for example, by using a cloud VPC, an IP allowlist for inbound requests, or authentication / authorization middleware).This issue only affects users and integrations that run the
mlflow serverandmlflow uicommands. Integrations that do not make use ofmlflow serverormlflow uiare unaffected; for example, the Databricks Managed MLflow product and MLflow on Azure Machine Learning do not make use of these commands and are not impacted by these vulnerabilities in any way.The vulnerability detailed in https://nvd.nist.gov/vuln/detail/CVE-2023-1176 enables an actor to check the existence of arbitrary files unrelated to MLflow from the host server, including any files stored in remote locations to which the host server has access.
Patches
This vulnerability has been patched in MLflow 2.2.1, which was released to PyPI on March 2nd, 2023. If you are using
mlflow serverormlflow uiwith the MLflow Model Registry, we recommend upgrading to MLflow 2.2.1 as soon as possible.Workarounds
If you are using the MLflow open source
mlflow serverormlflow uicommands, we strongly recommend limiting who can access your MLflow Model Registry and MLflow Tracking servers using a cloud VPC, an IP allowlist for inbound requests, authentication / authorization middleware, or another access restriction mechanism of your choosing.If you are using the MLflow open source
mlflow serverormlflow uicommands, we also strongly recommend limiting the remote files to which your MLflow Model Registry and MLflow Tracking servers have access. For example, if your MLflow Model Registry or MLflow Tracking server uses cloud-hosted blob storage for MLflow artifacts, make sure to restrict the scope of your server's cloud credentials such that it can only access files and directories related to MLflow.References
More information about the vulnerability is available at https://nvd.nist.gov/vuln/detail/CVE-2023-1176.
CVE-2023-1177
Impact
Users of the MLflow Open Source Project who are hosting the MLflow Model Registry using the
mlflow serverormlflow uicommands using an MLflow version older than MLflow 2.2.1 may be vulnerable to a remote file access exploit if they are not limiting who can query their server (for example, by using a cloud VPC, an IP allowlist for inbound requests, or authentication / authorization middleware).This issue only affects users and integrations that run the
mlflow serverandmlflow uicommands. Integrations that do not make use ofmlflow serverormlflow uiare unaffected; for example, the Databricks Managed MLflow product and MLflow on Azure Machine Learning do not make use of these commands and are not impacted by these vulnerabilities in any way.The vulnerability detailed in https://nvd.nist.gov/vuln/detail/CVE-2023-1177 enables an actor to download arbitrary files unrelated to MLflow from the host server, including any files stored in remote locations to which the host server has access.
Patches
This vulnerability has been patched in MLflow 2.2.1, which was released to PyPI on March 2nd, 2023. If you are using
mlflow serverormlflow uiwith the MLflow Model Registry, we recommend upgrading to MLflow 2.2.1 as soon as possible.Workarounds
If you are using the MLflow open source
mlflow serverormlflow uicommands, we strongly recommend limiting who can access your MLflow Model Registry and MLflow Tracking servers using a cloud VPC, an IP allowlist for inbound requests, authentication / authorization middleware, or another access restriction mechanism of your choosing.If you are using the MLflow open source
mlflow serverormlflow uicommands, we also strongly recommend limiting the remote files to which your MLflow Model Registry and MLflow Tracking servers have access. For example, if your MLflow Model Registry or MLflow Tracking server uses cloud-hosted blob storage for MLflow artifacts, make sure to restrict the scope of your server's cloud credentials such that it can only access files and directories related to MLflow.References
More information about the vulnerability is available at https://nvd.nist.gov/vuln/detail/CVE-2023-1177.
CVE-2023-2356
Relative Path Traversal in GitHub repository mlflow/mlflow prior to 2.3.1.
GHSA-83fm-w79m-64r5
Impact
Users of the MLflow Open Source Project who are hosting the MLflow Model Registry using the
mlflow serverormlflow uicommands using an MLflow version older than MLflow 2.3.1 may be vulnerable to a remote file access exploit if they are not limiting who can query their server (for example, by using a cloud VPC, an IP allowlist for inbound requests, or authentication / authorization middleware).This issue only affects users and integrations that run the
mlflow serverandmlflow uicommands. Integrations that do not make use ofmlflow serverormlflow uiare unaffected; for example, the Databricks Managed MLflow product and MLflow on Azure Machine Learning do not make use of these commands and are not impacted by these vulnerabilities in any way.The vulnerability is very similar to https://nvd.nist.gov/vuln/detail/CVE-2023-1177, and a separate CVE will be published and updated here shortly.
Patches
This vulnerability has been patched in MLflow 2.3.1, which was released to PyPI on April 27th, 2023. If you are using
mlflow serverormlflow uiwith the MLflow Model Registry, we recommend upgrading to MLflow 2.3.1 as soon as possible.Workarounds
If you are using the MLflow open source
mlflow serverormlflow uicommands, we strongly recommend limiting who can access your MLflow Model Registry and MLflow Tracking servers using a cloud VPC, an IP allowlist for inbound requests, authentication / authorization middleware, or another access restriction mechanism of your choosing.If you are using the MLflow open source
mlflow serverormlflow uicommands, we also strongly recommend limiting the remote files to which your MLflow Model Registry and MLflow Tracking servers have access. For example, if your MLflow Model Registry or MLflow Tracking server uses cloud-hosted blob storage for MLflow artifacts, make sure to restrict the scope of your server's cloud credentials such that it can only access files and directories related to MLflow.References
CVE-2023-2780
mlflow prior to 2.3.0 is vulnerable to path traversal due to a bypass of the fix for CVE-2023-1177.
CVE-2023-3765
Absolute Path Traversal in GitHub repository mlflow/mlflow prior to 2.5.0.
CVE-2023-4033
OS Command Injection in GitHub repository mlflow/mlflow prior to 2.6.0.
CVE-2023-6015
MLflow allowed arbitrary files to be PUT onto the server.
CVE-2023-6018
The mlflow web server includes tools for tracking experiments, packaging code into reproducible runs, and sharing and deploying models. As this vulnerability allows to write / overwrite any file on the file system, it gives a lot of ways to archive code execution (like overwriting
/home/<user>/.bashrc). A malicious user could use this issue to get command execution on the vulnerable machine and get access to data & models information.CVE-2023-43472
An issue in MLFlow versions 2.8.1 and before allows a remote attacker to obtain sensitive information via a crafted request to REST API.
CVE-2023-6568
A reflected Cross-Site Scripting (XSS) vulnerability exists in the mlflow/mlflow repository, specifically within the handling of the Content-Type header in POST requests. An attacker can inject malicious JavaScript code into the Content-Type header, which is then improperly reflected back to the user without adequate sanitization or escaping, leading to arbitrary JavaScript execution in the context of the victim's browser. The vulnerability is present in the mlflow/server/auth/init.py file, where the user-supplied Content-Type header is directly injected into a Python formatted string and returned to the user, facilitating the XSS attack.
CVE-2023-6709
Improper Neutralization of Special Elements Used in a Template Engine in GitHub repository mlflow/mlflow prior to 2.9.2.
CVE-2023-6753
Path Traversal in GitHub repository mlflow/mlflow prior to 2.9.2.
CVE-2023-6831
Path Traversal: '..\filename' in GitHub repository mlflow/mlflow prior to 2.9.2.
CVE-2023-6940
with only one user interaction(download a malicious config), attackers can gain full command execution on the victim system.
CVE-2023-6975
A malicious user could use this issue to get command execution on the vulnerable machine and get access to data & models information.
CVE-2023-30172
A directory traversal vulnerability in the /get-artifact API method of the mlflow platform prior to v2.0.0 allows attackers to read arbitrary files on the server via the path parameter.
CVE-2023-6977
This vulnerability enables malicious users to read sensitive files on the server.
CVE-2023-6974
A malicious user could use this issue to access internal HTTP(s) servers and in the worst case (ie: aws instance) it could be abused to get a remote code execution on the victim machine.
Release Notes
mlflow/mlflow (mlflow)
v2.9.2Compare Source
MLflow 2.9.2 is a patch release, containing several critical security fixes and configuration updates to support extremely large model artifacts.
Features:
mlflow.deployments.openaiAPI to simplify direct access to OpenAI services through the deployments API (#10473, @prithvikannan)Security fixes:
..path traversal queries (#10653, @B-Step62)HTTPDatasetSource(#10647, @BenWilson2)Documentation updates:
Small bug fixes and documentation updates:
#10677, #10636, @serena-ruan; #10652, #10649, #10641, @harupy; #10643, #10632, @BenWilson2
v2.9.1Compare Source
MLflow 2.9.1 is a patch release, containing a critical bug fix related to loading
pyfuncmodels that were saved in previous versions of MLflow.Bug fixes:
Small bug fixes and documentation updates:
#10625, @BenWilson2
v2.9.0Compare Source
MLflow 2.9.0 includes several major features and improvements.
MLflow AI Gateway deprecation (#10420, @harupy):
The feature previously known as MLflow AI Gateway has been moved to utilize the MLflow deployments API.
For guidance on migrating from the AI Gateway to the new deployments API, please see the [MLflow AI Gateway Migration Guide](https://mlflow.org/docs/latest/llms/gateway/migration.html.
MLflow Tracking docs overhaul (#10471, @B-Step62):
The MLflow tracking docs have been overhauled. We'd like your feedback on the new tracking docs!
Security fixes:
Three security patches have been filed with this release and CVE's have been issued with the details involved in the security patch and potential attack vectors. Please review and update your tracking server deployments if your tracking server is not securely deployed and has open access to the internet.
pathinHttpArtifactRepository.list_artifacts(#10585, @harupy)filenameinContent-Dispositionheader forHTTPDatasetSource(#10584, @harupy).Content-Typeheader to prevent POST XSS (#10526, @B-Step62)Features:
backoff_jitterwhen making HTTP requests (#10486, @ajinkyavbhandare)aggregate_resultsif the score type is numeric inmake_metricAPI (#10490, @sunishsheth2009)torch_dtypefor transformers models (#10586, @serena-ruan)ndcg_at_kto retriever evaluation (#10284, @liangz1)copy_model_version(#10308, @jerrylian-db)RunnableSequence,RunnableParallel, andRunnableBranch(#10521, #10611, @serena-ruan)Bug fixes:
Documentation updates:
Small bug fixes and documentation updates:
#10567, #10559, #10348, #10342, #10264, #10265, @B-Step62; #10595, #10401, #10418, #10394, @chenmoneygithub; #10557, @dan-licht; #10584, #10462, #10445, #10434, #10432, #10412, #10411, #10408, #10407, #10403, #10361, #10340, #10339, #10310, #10276, #10268, #10260, #10224, #10214, @harupy; #10415, @jessechancy; #10579, #10555, @annzhang-db; #10540, @wllgrnt; #10556, @smurching; #10546, @mbenoit29; #10534, @gabrielfu; #10532, #10485, #10444, #10433, #10375, #10343, #10192, @serena-ruan; #10480, #10416, #10173, @jerrylian-db; #10527, #10448, #10443, #10442, #10441, #10440, #10439, #10381, @prithvikannan; #10509, @keenranger; #10508, #10494, @WeichenXu123; #10489, #10266, #10210, #10103, @TomeHirata; #10495, #10435, #10185, @daniellok-db; #10319, @michael-berk; #10417, @bbqiu; #10379, #10372, #10282, @BenWilson2; #10297, @KonakanchiSwathi; #10226, #10223, #10221, @milinddethe15; #10222, @flooxo; #10590, @letian-w;
v2.8.1Compare Source
MLflow 2.8.1 is a patch release, containing some critical bug fixes and an update to our continued work on reworking our docs.
Notable details:
mlflow.llm.log_predictionsis being marked as deprecated, as its functionality has been incorporated intomlflow.log_table. This API will be removed in the 2.9.0 release. (#10414, @dbczumar)Bug fixes:
Azure OpenAIintegration formlflow.evaluatewhen using LLMjudgemetrics (#10291, @prithvikannan)Examplesto optional for themake_genai_metricAPI (#10353, @prithvikannan)fastapidependency when usingmlflow.evaluatefor LLM results (#10354, @prithvikannan)mlflow.login()API to catch invalid hostname configuration input errors (#10239, @chenmoneygithub)flushoperation at the conclusion of logging system metrics (#10320, @chenmoneygithub)SHAPmodel explainability functionality withinmlflow.shap.log_explanationso that duplicate or conflicting dependencies are not registered when logging (#10305, @BenWilson2)Documentation updates:
Small bug fixes and documentation updates:
#10367, #10359, #10358, #10340, #10310, #10276, #10277, #10247, #10260, #10220, #10263, #10259, #10219, @harupy; #10313, #10303, #10213, #10272, #10282, #10283, #10231, #10256, #10242, #10237, #10238, #10233, #10229, #10211, #10231, #10256, #10242, #10238, #10237, #10229, #10233, #10211, @BenWilson2; #10375, @serena-ruan; #10330, @Haxatron; #10342, #10249, #10249, @B-Step62; #10355, #10301, #10286, #10257, #10236, #10270, #10236, @prithvikannan; #10321, #10258, @jerrylian-db; #10245, @jessechancy; #10278, @daniellok-db; #10244, @gabrielfu; #10226, @milinddethe15; #10390, @bbqiu; #10232, @sunishsheth2009
v2.8.0Compare Source
MLflow 2.8.0 includes several notable new features and improvements
Features:
completionsin the OpenAI flavor (#9838, @santiagxf)copy_model_versionclient API for copying model versions across registered models (#9946, #10078, #10140, @jerrylian-db)xethubas an artifact store via a plugin extension (#9957, @Kelton8Z)Bug fixes:
Documentation updates:
mlflow.data.from_numpy()(#9885, @chenmoneygithub)Small bug fixes and documentation updates:
#10202, #10189, #10188, #10159, #10175, #10165, #10154, #10083, #10082, #10081, #10071, #10077, #10070, #10053, #10057, #10055, #10020, #9928, #9929, #9944, #9979, #9923, #9842, @annzhang-db; #10203, #10196, #10172, #10176, #10145, #10115, #10107, #10054, #10056, #10018, #9976, #9999, #9998, #9995, #9978, #9973, #9975, #9972, #9974, #9960, #9925, #9920, @prithvikannan; #10144, #10166, #10143, #10129, #10059, #10123, #9555, #9619, @bbqiu; #10187, #10191, #10181, #10179, #10151, #10148, #10126, #10119, #10099, #10100, #10097, #10089, #10096, #10091, #10085, #10068, #10065, #10064, #10060, #10023, #10030, #10028, #10022, #10007, #10006, #9988, #9961, #9963, #9954, #9953, #9937, #9932, #9931, #9910, #9901, #9852, #9851, #9848, #9847, #9841, #9844, #9825, #9820, [#9806](https://redirect.github.com/mlflow/mlflow/iss
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