Mlflow
Monthly
MLflow's `CreateModelVersion` endpoint prior to version 3.15.0 permits authenticated users to anchor a model version to another user's run or logged-model artifact directory, then retrieve those artifacts through `GET /model-versions/get-artifact` without holding the required READ permission on the source resource. This confused-deputy pattern arises because `_validate_source_run()` and `_validate_source_model()` in `mlflow/server/handlers.py` enforce only path containment - not cross-user authorization - allowing the registered model's artifact-read gate to be used as a proxy to exfiltrate a victim's ML artifacts. No active exploitation has been confirmed (not in CISA KEV), and the vendor-released fix is available in MLflow 3.15.0 alongside a public security advisory and PR diff that fully document the exploit technique.
Server-side request forgery in MLflow before 3.15.0 lets remote unauthenticated attackers coerce the server into making outbound requests to internal or cloud metadata endpoints via the POST /api/2.0/mlflow/webhooks/{id}/test endpoint. Because URL validation and the actual delivery re-resolve the hostname independently and follow redirects, an attacker can bypass the public-IP check and receive the target's response_status and response_body, enabling exfiltration of cloud credentials (e.g. from 169.254.169.254). No public exploit identified at time of analysis; the CVSS 9.3 rating and scope-change flag reflect the credential-theft potential of reaching metadata services.
MLflow's AI Gateway proxy exposes internal network services - including cloud-instance metadata endpoints - to any authenticated user, including read-only accounts, via an unvalidated api_base field in the CreateGatewaySecret endpoint. All MLflow releases through 3.14.0 are affected; the attack requires only basic credentials and no special configuration, making IAM credential theft a realistic outcome for cloud-hosted deployments. No public exploit has been identified at time of analysis, but the two-step attack (create secret, invoke proxy) involves only standard HTTP requests and no technical barrier beyond holding a valid account.
Broken access control in MLflow prior to 3.14.0 lets any authenticated user read, modify, or delete traces belonging to experiments they are not authorized to access, defeating experiment-level authorization when authentication is enabled. The flaw stems from the trace API endpoints being omitted from the `_before_request` authorization handler, so requests reach these endpoints without any validator running. No public exploit has been identified at time of analysis, though a fix commit and huntr bounty report are public.
Server-side environment variable disclosure in MLflow versions prior to 3.11.0 allows attackers to exfiltrate sensitive credentials from the MLflow AI Gateway by abusing the `$ENV_VAR` resolution feature in gateway secret configuration. By registering or modifying a gateway route where `api_key` references an environment variable like `$AWS_SECRET_ACCESS_KEY` and pointing `api_base` at an attacker-controlled endpoint, the resolved secret is transmitted in upstream provider authentication headers. Publicly available exploit code exists via the huntr.com bounty disclosure, though EPSS remains low at 0.28% (51st percentile) and the issue is not in CISA KEV.
Cross-user artifact overwrite in MLflow versions prior to 3.10.0 allows authenticated users with --serve-artifacts mode enabled to abuse unprotected multipart upload (MPU) endpoints under /mlflow-artifacts/mpu/* and tamper with models owned by other users, enabling model supply chain poisoning and arbitrary code execution when poisoned models are deserialized. The SSVC framework classifies this as having a public proof-of-concept with total technical impact, though EPSS exploitation probability is currently only 0.05% (16th percentile) and no public exploit identified at time of analysis as actively weaponized.
Cross-origin request forgery in MLflow 3.9.0's Assistant feature allows remote attackers to bypass loopback-only protections on /ajax-api endpoints when a victim visits a malicious webpage, ultimately achieving arbitrary command execution through the Claude Code sub-agent. The flaw stems from improper origin validation (CWE-346) and is fixed in version 3.10.0; no public exploit identified at time of analysis, though a detailed huntr.com report and an upstream commit are publicly available.
Local privilege escalation to arbitrary code execution in MLflow versions prior to 3.11.0 stems from insecure temporary directory permissions (0o777 and 0o770) created by NFS and model-download helpers. Any local user sharing the filesystem - particularly on Databricks where NFS is enabled by default - can overwrite cloudpickle-serialized model artifacts and gain code execution when another user's process deserializes them via cloudpickle.load(). No public exploit is identified at time of analysis, and the issue is a continuation of CVE-2025-10279 which was only partially fixed.
Authentication bypass in MLflow 3.9.0 and earlier allows unauthenticated remote attackers to access protected Job API and OpenTelemetry trace ingestion endpoints when the server runs with basic-auth enabled via uvicorn/ASGI. Attackers can submit jobs, read results, cancel operations, and inject trace data without credentials. The FastAPI permission middleware incorrectly enforced authentication only on /gateway/ routes, leaving /ajax-api/3.0/jobs/* and /v1/traces unprotected due to architectural mismatch between Flask and FastAPI authentication mechanisms. Fixed in version 3.10.0 with GitHub commit bb62e77 adding proper validators for all FastAPI routes.
Remote unauthenticated attackers can read arbitrary files from MLflow server filesystems in versions 3.9.0 and earlier. By submitting a CreateModelVersion request with the tag 'mlflow.prompt.is_prompt' and an arbitrary local filesystem path as the source, attackers bypass validation logic. The get_model_version_artifact_handler() function later serves files from that path without checking prompt status, enabling full confidentiality breach. Fixed in version 3.10.0 per commit 6e801f4 which blocks file:// URIs and absolute paths for prompt sources. CVSS 7.5 (High) reflects network attack vector with no authentication or user interaction required.
Server-Side Request Forgery in MLflow allows authenticated users to force the MLflow backend to send HTTP requests to arbitrary URLs, including internal services and cloud metadata endpoints (e.g., AWS EC2 metadata at 169.254.169.254). Affects MLflow versions prior to 3.9.0. The webhook creation endpoint accepts unvalidated user-controlled URLs that are later used in HTTP POST requests, enabling cloud credential theft, internal network reconnaissance, and data exfiltration. Vendor-released patch available in MLflow 3.9.0, confirmed by GitHub commit 64aa0ab. No active exploitation confirmed (not in CISA KEV), but publicly disclosed with detailed technical analysis from huntr.com.
MLflow's FastAPI job endpoints bypass basic-auth entirely, allowing network attackers to submit and execute jobs without credentials (CVSS 9.8, CWE-306). Affects mlflow/mlflow latest version when MLFLOW_SERVER_ENABLE_JOB_EXECUTION=true and job functions are allowlisted. Public POC exists per SSVC framework. EPSS score of 0.20% (42nd percentile) indicates low observed exploitation probability despite critical CVSS, suggesting targeted rather than widespread risk. Vendor-released patch not confirmed at time of analysis - remediation relies on configuration changes and service hardening.
Command injection in MLflow's MLServer integration allows unauthenticated adjacent network attackers to execute arbitrary commands when models are served with enable_mlserver=True. Unsanitized model_uri parameters embedded in bash -c commands enable shell metacharacter exploitation (command substitution via $() or backticks). With CVSS 9.6 (Critical) and adjacent network attack vector, this poses significant risk in multi-tenant MLOps environments where lower-privileged users can control model URIs served by higher-privileged services. No public exploit code identified at time of analysis, with EPSS data not yet available for this recent CVE.
Missing authorization enforcement on the log-inputs API endpoint in MLflow 3.13.0 through 3.14.x allows any authenticated user to inject fabricated DatasetInput records into another user's run lineage metadata without holding UPDATE permission on that run. The root cause is that the LogInputs (and LogOutputs) handler was simply never registered in the BEFORE_REQUEST_HANDLERS auth middleware map, leaving the POST /api/2.0/mlflow/runs/log-inputs endpoint unguarded. No active exploitation has been confirmed (not listed in CISA KEV), and a vendor-released patch is available in version 3.15.0.
Missing authorization in MLflow's Experiment-scoped Label Schema CRUD API allows a remote, low-privileged attacker to perform unauthorized read, create, update, or delete operations on label schema resources. The vulnerability stems from a pre-release feature that was disclosed before its authentication handlers were implemented - a GitHub maintainer confirmed: 'The auth handlers will be added before the release.' A proof-of-concept is publicly available via the referenced GitHub issue. Despite network accessibility, the CVSS 4.0 score of 1.3 reflects high attack complexity and limited impact scope, and no CISA KEV listing has been issued.
Dataset digest computation in MLflow up to version 3.10.0 uses MD5 - a cryptographically broken algorithm - to fingerprint datasets, enabling a local attacker to craft colliding inputs that undermine dataset integrity tracking. Affected functions include compute_pandas_digest, compute_numpy_digest, and hash_dict_of_arrays in mlflow/data/digest_utils.py, which use a truncated 8-character MD5 digest that further reduces the collision space. Publicly available exploit code exists; this vulnerability is not confirmed actively exploited per CISA KEV, and the CVSS 4.0 score of 1.1 reflects the constrained local-only attack surface.
Missing authorization enforcement in MLflow 3.9.0 allows any low-privileged authenticated user to enumerate all gateway secrets, endpoints, and model definitions via three unprotected Gateway API list endpoints. The root cause is an omission in the `BEFORE_REQUEST_HANDLERS` dictionary within `mlflow/server/auth/__init__.py`, which gates authorization for request handlers - three Gateway API list operations (`ListGatewaySecretInfos`, `ListGatewayEndpoints`, `ListGatewayModelDefinitions`) are absent from this registry, bypassing access control entirely when basic-auth is active. No public exploit has been identified at time of analysis, and the vulnerability is not listed in CISA KEV, but the low attack complexity and high confidentiality impact warrant prompt remediation in any deployment with multi-tenant or least-privilege access expectations.
Missing post-response authorization filtering in MLflow's self-hosted server exposes all registered model version metadata to any authenticated user, regardless of their per-model permission level. Both the REST API endpoint `SearchModelVersions` and the GraphQL query `mlflowSearchModelVersions` were absent from the authorization middleware chains in versions up to 3.9.0, allowing a low-privilege authenticated user to enumerate model names, version descriptions, source artifact URIs, tags, and other metadata across all registered models in multi-tenant deployments. No public exploit identified at time of analysis; the vendor-released patch is confirmed in version 3.10.0.
MLflow through version 3.10.1 allows authenticated users to bypass authorization controls and download model artifacts from experiments they lack permission to access via an unprotected AJAX endpoint. The vulnerability requires valid MLflow authentication but no special privileges, enabling lateral access to restricted experiment data. Patch availability confirmed via upstream pull request; CISA SSVC assessment indicates partial technical impact with automatable exploitation path but no confirmed active exploitation.
Stored cross-site scripting (XSS) in MLflow through version 3.10.1 allows authenticated attackers to inject malicious payloads via YAML-based MLmodel artifacts that execute when other users view the artifact in the web interface, enabling session hijacking or unauthorized actions on behalf of victims. CVSS 5.1 reflects low severity due to authentication requirement and user interaction; SSVC framework rates exploitation as none, automatable as no, and technical impact as partial. Upstream fix is available in a GitHub PR, though no formally released patched version has been independently confirmed from provided data.
Critical command injection in MLflow 3.8.0 enables remote code execution during model deployment when attackers supply malicious artifacts via the `env_manager=LOCAL` parameter. The `_install_model_dependencies_to_env()` function unsafely interpolates dependency specifications from `python_env.yaml` directly into shell commands without sanitization. With CVSS 10.0 (network-accessible, no authentication, no complexity) and publicly available exploit code exists (reported via Huntr bug bounty, patched in 3.8.2), this represents an immediate critical risk for organizations using MLflow model serving infrastructure. EPSS data not available, but exploitation scenario is straightforward for adversaries with model deployment access.
Path traversal in MLflow's tar.gz extraction (mlflow/mlflow versions <3.7.0) allows remote attackers to overwrite arbitrary files and potentially escape sandbox isolation via malicious archive uploads. The vulnerability affects the `extract_archive_to_dir` function which fails to validate tar member paths during extraction. Exploitation requires user interaction (CVSS UI:R) but needs no authentication (PR:N). EPSS data not provided, but no CISA KEV listing indicates no confirmed active exploitation at time of analysis. Public exploit code exists via Huntr bounty disclosure.
MLflow's basic-auth authentication system fails to protect tracing and assessment endpoints, enabling any authenticated user with no experiment permissions to read trace metadata and create unauthorized assessments. The vulnerability affects MLflow deployments running with the '--app-name=basic-auth' flag and carries a CVSS score of 8.1 (High) with network-based attack vector requiring low privilege authentication. This vulnerability was reported via the HackerOne bug bounty platform (@huntr_ai) with no public exploit identified at time of analysis.
MLflow, a popular open-source machine learning lifecycle platform, contains a path traversal vulnerability in its pyfunc extraction process that allows arbitrary file writes. The vulnerability stems from unsafe use of tarfile.extractall without proper path validation, enabling attackers to craft malicious tar.gz files with directory traversal sequences or absolute paths to write files outside the intended extraction directory. This poses critical risk in multi-tenant environments and can lead to remote code execution, with a CVSS score of 8.1 and confirmed exploit details available via Huntr.
Command injection vulnerability in MLflow versions before v3.7.0 that allows attackers to execute arbitrary commands by injecting malicious input through the --container parameter when deploying models to SageMaker. The vulnerability affects MLflow installations in development environments, CI/CD pipelines, and cloud deployments, with a CVSS score of 7.5 indicating high severity. No active exploitation or KEV listing is reported, and no EPSS data is available to assess real-world exploitation likelihood.
In mlflow version 2.20.3, the temporary directory used for creating Python virtual environments is assigned insecure world-writable permissions (0o777). [CVSS 7.0 HIGH]
MLFlow versions up to and including 3.4.0 are vulnerable to DNS rebinding attacks due to a lack of Origin header validation in the MLFlow REST server. This vulnerability allows malicious websites to bypass Same-Origin Policy protections and execute unauthorized calls against REST endpoints. [CVSS 8.1 HIGH]
gateway_proxy_handler in MLflow before 3.1.0 lacks gateway_path validation.
In mlflow/mlflow version 2.18, an admin is able to create a new user account without setting a password. Rated medium severity (CVSS 5.5), this vulnerability is remotely exploitable, low attack complexity. Public exploit code available.
A Cross-Site Request Forgery (CSRF) vulnerability exists in the Signup feature of mlflow/mlflow versions 2.17.0 to 2.20.1. Rated high severity (CVSS 7.1), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
In mlflow/mlflow version 2.17.2, the `/graphql` endpoint is vulnerable to a denial of service attack. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
A path traversal vulnerability exists in mlflow/mlflow version 2.15.1. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and EPSS exploitation probability 26.9%.
In mlflow/mlflow version v2.13.2, a vulnerability exists that allows the creation or renaming of an experiment with a large number of integers in its name due to the lack of a limit on the experiment. Rated medium severity (CVSS 5.3), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
Excessive directory permissions in MLflow leads to local privilege escalation when using spark_udf. Rated high severity (CVSS 7.0). This Incorrect Default Permissions vulnerability could allow attackers to access resources due to overly permissive default settings.
A vulnerability in mlflow/mlflow version 2.11.1 allows attackers to create multiple models with the same name by exploiting URL encoding. Rated medium severity (CVSS 5.4), this vulnerability is remotely exploitable, low attack complexity. Public exploit code available and no vendor patch available.
A Local File Inclusion (LFI) vulnerability was identified in mlflow/mlflow, specifically in version 2.9.2, which was fixed in version 2.11.3. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
A vulnerability in mlflow/mlflow version 8.2.1 allows for remote code execution due to improper neutralization of special elements used in an OS command ('Command Injection') within the. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
Remote Code Execution can occur in versions of the MLflow platform running version 1.11.0 or newer, enabling a maliciously crafted MLproject to execute arbitrary code on an end user’s system when run. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.27.0 or newer, enabling a maliciously crafted Recipe to execute arbitrary code on an end user’s system. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.5.0 or newer, enabling a maliciously uploaded PyTorch model to run arbitrary code on an end user’s. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 2.5.0 or newer, enabling a maliciously uploaded Langchain AgentExecutor model to run arbitrary code on. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 2.0.0rc0 or newer, enabling a maliciously uploaded Tensorflow model to run arbitrary code on an end. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.23.0 or newer, enabling a maliciously uploaded LightGBM scikit-learn model to run arbitrary code on an. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.24.0 or newer, enabling a maliciously uploaded pmdarima model to run arbitrary code on an end user’s. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.9.0 or newer, enabling a maliciously uploaded PyFunc model to run arbitrary code on an end user’s. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.1.0 or newer, enabling a maliciously uploaded scikit-learn model to run arbitrary code on an end. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.1.0 or newer, enabling a maliciously uploaded scikit-learn model to run arbitrary code on an end. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
A broken access control vulnerability exists in mlflow/mlflow versions before 2.10.1, where low privilege users with only EDIT permissions on an experiment can delete any artifacts. Rated medium severity (CVSS 5.4), this vulnerability is remotely exploitable, low attack complexity. Public exploit code available.
A path traversal vulnerability exists in mlflow/mlflow version 2.11.0, identified as a bypass for the previously addressed CVE-2023-6909. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
mlflow/mlflow is vulnerable to Local File Inclusion (LFI) due to improper parsing of URIs, allowing attackers to bypass checks and read arbitrary files on the system. Rated critical severity (CVSS 9.3), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
A path traversal vulnerability exists in the mlflow/mlflow repository, specifically within the handling of the `artifact_location` parameter when creating an experiment. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
A path traversal vulnerability exists in the mlflow/mlflow repository due to improper handling of URL parameters. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
A path traversal vulnerability exists in the mlflow/mlflow repository, specifically within the artifact deletion functionality. Rated high severity (CVSS 8.1), this vulnerability is remotely exploitable, low attack complexity. Public exploit code available and no vendor patch available.
A path traversal vulnerability exists in the `_create_model_version()` function within `server/handlers.py` of the mlflow/mlflow repository, due to improper validation of the `source` parameter. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
A path traversal vulnerability exists in mlflow/mlflow version 2.9.2, allowing attackers to access arbitrary files on the server. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
Insufficient sanitization in MLflow leads to XSS when running a recipe that uses an untrusted dataset. Rated critical severity (CVSS 9.6), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
Insufficient sanitization in MLflow leads to XSS when running an untrusted recipe. Rated critical severity (CVSS 9.6), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
This vulnerability enables malicious users to read sensitive files on the server. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
This vulnerability is capable of writing arbitrary files into arbitrary locations on the remote filesystem in the context of the server process. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, low attack complexity. Public exploit code available.
A malicious user could use this issue to get command execution on the vulnerable machine and get access to data & models information. Rated critical severity (CVSS 9.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
A malicious user could use this issue to access internal HTTP(s) servers and in the worst case (ie: aws instance) it could be abuse to get a remote code execution on the victim machine. Rated critical severity (CVSS 9.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
with only one user interaction(download a malicious config), attackers can gain full command execution on the victim system. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. This Command Injection vulnerability could allow attackers to inject arbitrary commands into system command execution.
Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.9.2. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.9.2. Rated high severity (CVSS 8.1), this vulnerability is remotely exploitable, low attack complexity. Public exploit code available.
Path Traversal in GitHub repository mlflow/mlflow prior to 2.9.2. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
Improper Neutralization of Special Elements Used in a Template Engine in GitHub repository mlflow/mlflow prior to 2.9.2. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, low attack complexity. Public exploit code available.
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. Rated medium severity (CVSS 6.1), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
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. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
An attacker is able to arbitrarily create an account in MLflow bypassing any authentication requirment. Rated critical severity (CVSS 9.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
An attacker can overwrite any file on the server hosting MLflow without any authentication. Rated critical severity (CVSS 9.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
MLflow allowed arbitrary files to be PUT onto the server. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
OS Command Injection in GitHub repository mlflow/mlflow prior to 2.6.0. Rated high severity (CVSS 7.8), this vulnerability is low attack complexity.
Absolute Path Traversal in GitHub repository mlflow/mlflow prior to 2.5.0. Rated critical severity (CVSS 10.0), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.3.1. Rated critical severity (CVSS 9.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
A directory traversal vulnerability in the /get-artifact API method of the mlflow platform up to v2.0.1 allows attackers to read arbitrary files on the server via the path parameter. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. No vendor patch available.
Relative Path Traversal in GitHub repository mlflow/mlflow prior to 2.3.1. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.2.1. Rated critical severity (CVSS 9.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
Absolute Path Traversal in GitHub repository mlflow/mlflow prior to 2.2.2. Rated low severity (CVSS 3.3), this vulnerability is low attack complexity. Public exploit code available.
Insecure Temporary File in GitHub repository mlflow/mlflow prior to 1.23.1. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
MLflow's `CreateModelVersion` endpoint prior to version 3.15.0 permits authenticated users to anchor a model version to another user's run or logged-model artifact directory, then retrieve those artifacts through `GET /model-versions/get-artifact` without holding the required READ permission on the source resource. This confused-deputy pattern arises because `_validate_source_run()` and `_validate_source_model()` in `mlflow/server/handlers.py` enforce only path containment - not cross-user authorization - allowing the registered model's artifact-read gate to be used as a proxy to exfiltrate a victim's ML artifacts. No active exploitation has been confirmed (not in CISA KEV), and the vendor-released fix is available in MLflow 3.15.0 alongside a public security advisory and PR diff that fully document the exploit technique.
Server-side request forgery in MLflow before 3.15.0 lets remote unauthenticated attackers coerce the server into making outbound requests to internal or cloud metadata endpoints via the POST /api/2.0/mlflow/webhooks/{id}/test endpoint. Because URL validation and the actual delivery re-resolve the hostname independently and follow redirects, an attacker can bypass the public-IP check and receive the target's response_status and response_body, enabling exfiltration of cloud credentials (e.g. from 169.254.169.254). No public exploit identified at time of analysis; the CVSS 9.3 rating and scope-change flag reflect the credential-theft potential of reaching metadata services.
MLflow's AI Gateway proxy exposes internal network services - including cloud-instance metadata endpoints - to any authenticated user, including read-only accounts, via an unvalidated api_base field in the CreateGatewaySecret endpoint. All MLflow releases through 3.14.0 are affected; the attack requires only basic credentials and no special configuration, making IAM credential theft a realistic outcome for cloud-hosted deployments. No public exploit has been identified at time of analysis, but the two-step attack (create secret, invoke proxy) involves only standard HTTP requests and no technical barrier beyond holding a valid account.
Broken access control in MLflow prior to 3.14.0 lets any authenticated user read, modify, or delete traces belonging to experiments they are not authorized to access, defeating experiment-level authorization when authentication is enabled. The flaw stems from the trace API endpoints being omitted from the `_before_request` authorization handler, so requests reach these endpoints without any validator running. No public exploit has been identified at time of analysis, though a fix commit and huntr bounty report are public.
Server-side environment variable disclosure in MLflow versions prior to 3.11.0 allows attackers to exfiltrate sensitive credentials from the MLflow AI Gateway by abusing the `$ENV_VAR` resolution feature in gateway secret configuration. By registering or modifying a gateway route where `api_key` references an environment variable like `$AWS_SECRET_ACCESS_KEY` and pointing `api_base` at an attacker-controlled endpoint, the resolved secret is transmitted in upstream provider authentication headers. Publicly available exploit code exists via the huntr.com bounty disclosure, though EPSS remains low at 0.28% (51st percentile) and the issue is not in CISA KEV.
Cross-user artifact overwrite in MLflow versions prior to 3.10.0 allows authenticated users with --serve-artifacts mode enabled to abuse unprotected multipart upload (MPU) endpoints under /mlflow-artifacts/mpu/* and tamper with models owned by other users, enabling model supply chain poisoning and arbitrary code execution when poisoned models are deserialized. The SSVC framework classifies this as having a public proof-of-concept with total technical impact, though EPSS exploitation probability is currently only 0.05% (16th percentile) and no public exploit identified at time of analysis as actively weaponized.
Cross-origin request forgery in MLflow 3.9.0's Assistant feature allows remote attackers to bypass loopback-only protections on /ajax-api endpoints when a victim visits a malicious webpage, ultimately achieving arbitrary command execution through the Claude Code sub-agent. The flaw stems from improper origin validation (CWE-346) and is fixed in version 3.10.0; no public exploit identified at time of analysis, though a detailed huntr.com report and an upstream commit are publicly available.
Local privilege escalation to arbitrary code execution in MLflow versions prior to 3.11.0 stems from insecure temporary directory permissions (0o777 and 0o770) created by NFS and model-download helpers. Any local user sharing the filesystem - particularly on Databricks where NFS is enabled by default - can overwrite cloudpickle-serialized model artifacts and gain code execution when another user's process deserializes them via cloudpickle.load(). No public exploit is identified at time of analysis, and the issue is a continuation of CVE-2025-10279 which was only partially fixed.
Authentication bypass in MLflow 3.9.0 and earlier allows unauthenticated remote attackers to access protected Job API and OpenTelemetry trace ingestion endpoints when the server runs with basic-auth enabled via uvicorn/ASGI. Attackers can submit jobs, read results, cancel operations, and inject trace data without credentials. The FastAPI permission middleware incorrectly enforced authentication only on /gateway/ routes, leaving /ajax-api/3.0/jobs/* and /v1/traces unprotected due to architectural mismatch between Flask and FastAPI authentication mechanisms. Fixed in version 3.10.0 with GitHub commit bb62e77 adding proper validators for all FastAPI routes.
Remote unauthenticated attackers can read arbitrary files from MLflow server filesystems in versions 3.9.0 and earlier. By submitting a CreateModelVersion request with the tag 'mlflow.prompt.is_prompt' and an arbitrary local filesystem path as the source, attackers bypass validation logic. The get_model_version_artifact_handler() function later serves files from that path without checking prompt status, enabling full confidentiality breach. Fixed in version 3.10.0 per commit 6e801f4 which blocks file:// URIs and absolute paths for prompt sources. CVSS 7.5 (High) reflects network attack vector with no authentication or user interaction required.
Server-Side Request Forgery in MLflow allows authenticated users to force the MLflow backend to send HTTP requests to arbitrary URLs, including internal services and cloud metadata endpoints (e.g., AWS EC2 metadata at 169.254.169.254). Affects MLflow versions prior to 3.9.0. The webhook creation endpoint accepts unvalidated user-controlled URLs that are later used in HTTP POST requests, enabling cloud credential theft, internal network reconnaissance, and data exfiltration. Vendor-released patch available in MLflow 3.9.0, confirmed by GitHub commit 64aa0ab. No active exploitation confirmed (not in CISA KEV), but publicly disclosed with detailed technical analysis from huntr.com.
MLflow's FastAPI job endpoints bypass basic-auth entirely, allowing network attackers to submit and execute jobs without credentials (CVSS 9.8, CWE-306). Affects mlflow/mlflow latest version when MLFLOW_SERVER_ENABLE_JOB_EXECUTION=true and job functions are allowlisted. Public POC exists per SSVC framework. EPSS score of 0.20% (42nd percentile) indicates low observed exploitation probability despite critical CVSS, suggesting targeted rather than widespread risk. Vendor-released patch not confirmed at time of analysis - remediation relies on configuration changes and service hardening.
Command injection in MLflow's MLServer integration allows unauthenticated adjacent network attackers to execute arbitrary commands when models are served with enable_mlserver=True. Unsanitized model_uri parameters embedded in bash -c commands enable shell metacharacter exploitation (command substitution via $() or backticks). With CVSS 9.6 (Critical) and adjacent network attack vector, this poses significant risk in multi-tenant MLOps environments where lower-privileged users can control model URIs served by higher-privileged services. No public exploit code identified at time of analysis, with EPSS data not yet available for this recent CVE.
Missing authorization enforcement on the log-inputs API endpoint in MLflow 3.13.0 through 3.14.x allows any authenticated user to inject fabricated DatasetInput records into another user's run lineage metadata without holding UPDATE permission on that run. The root cause is that the LogInputs (and LogOutputs) handler was simply never registered in the BEFORE_REQUEST_HANDLERS auth middleware map, leaving the POST /api/2.0/mlflow/runs/log-inputs endpoint unguarded. No active exploitation has been confirmed (not listed in CISA KEV), and a vendor-released patch is available in version 3.15.0.
Missing authorization in MLflow's Experiment-scoped Label Schema CRUD API allows a remote, low-privileged attacker to perform unauthorized read, create, update, or delete operations on label schema resources. The vulnerability stems from a pre-release feature that was disclosed before its authentication handlers were implemented - a GitHub maintainer confirmed: 'The auth handlers will be added before the release.' A proof-of-concept is publicly available via the referenced GitHub issue. Despite network accessibility, the CVSS 4.0 score of 1.3 reflects high attack complexity and limited impact scope, and no CISA KEV listing has been issued.
Dataset digest computation in MLflow up to version 3.10.0 uses MD5 - a cryptographically broken algorithm - to fingerprint datasets, enabling a local attacker to craft colliding inputs that undermine dataset integrity tracking. Affected functions include compute_pandas_digest, compute_numpy_digest, and hash_dict_of_arrays in mlflow/data/digest_utils.py, which use a truncated 8-character MD5 digest that further reduces the collision space. Publicly available exploit code exists; this vulnerability is not confirmed actively exploited per CISA KEV, and the CVSS 4.0 score of 1.1 reflects the constrained local-only attack surface.
Missing authorization enforcement in MLflow 3.9.0 allows any low-privileged authenticated user to enumerate all gateway secrets, endpoints, and model definitions via three unprotected Gateway API list endpoints. The root cause is an omission in the `BEFORE_REQUEST_HANDLERS` dictionary within `mlflow/server/auth/__init__.py`, which gates authorization for request handlers - three Gateway API list operations (`ListGatewaySecretInfos`, `ListGatewayEndpoints`, `ListGatewayModelDefinitions`) are absent from this registry, bypassing access control entirely when basic-auth is active. No public exploit has been identified at time of analysis, and the vulnerability is not listed in CISA KEV, but the low attack complexity and high confidentiality impact warrant prompt remediation in any deployment with multi-tenant or least-privilege access expectations.
Missing post-response authorization filtering in MLflow's self-hosted server exposes all registered model version metadata to any authenticated user, regardless of their per-model permission level. Both the REST API endpoint `SearchModelVersions` and the GraphQL query `mlflowSearchModelVersions` were absent from the authorization middleware chains in versions up to 3.9.0, allowing a low-privilege authenticated user to enumerate model names, version descriptions, source artifact URIs, tags, and other metadata across all registered models in multi-tenant deployments. No public exploit identified at time of analysis; the vendor-released patch is confirmed in version 3.10.0.
MLflow through version 3.10.1 allows authenticated users to bypass authorization controls and download model artifacts from experiments they lack permission to access via an unprotected AJAX endpoint. The vulnerability requires valid MLflow authentication but no special privileges, enabling lateral access to restricted experiment data. Patch availability confirmed via upstream pull request; CISA SSVC assessment indicates partial technical impact with automatable exploitation path but no confirmed active exploitation.
Stored cross-site scripting (XSS) in MLflow through version 3.10.1 allows authenticated attackers to inject malicious payloads via YAML-based MLmodel artifacts that execute when other users view the artifact in the web interface, enabling session hijacking or unauthorized actions on behalf of victims. CVSS 5.1 reflects low severity due to authentication requirement and user interaction; SSVC framework rates exploitation as none, automatable as no, and technical impact as partial. Upstream fix is available in a GitHub PR, though no formally released patched version has been independently confirmed from provided data.
Critical command injection in MLflow 3.8.0 enables remote code execution during model deployment when attackers supply malicious artifacts via the `env_manager=LOCAL` parameter. The `_install_model_dependencies_to_env()` function unsafely interpolates dependency specifications from `python_env.yaml` directly into shell commands without sanitization. With CVSS 10.0 (network-accessible, no authentication, no complexity) and publicly available exploit code exists (reported via Huntr bug bounty, patched in 3.8.2), this represents an immediate critical risk for organizations using MLflow model serving infrastructure. EPSS data not available, but exploitation scenario is straightforward for adversaries with model deployment access.
Path traversal in MLflow's tar.gz extraction (mlflow/mlflow versions <3.7.0) allows remote attackers to overwrite arbitrary files and potentially escape sandbox isolation via malicious archive uploads. The vulnerability affects the `extract_archive_to_dir` function which fails to validate tar member paths during extraction. Exploitation requires user interaction (CVSS UI:R) but needs no authentication (PR:N). EPSS data not provided, but no CISA KEV listing indicates no confirmed active exploitation at time of analysis. Public exploit code exists via Huntr bounty disclosure.
MLflow's basic-auth authentication system fails to protect tracing and assessment endpoints, enabling any authenticated user with no experiment permissions to read trace metadata and create unauthorized assessments. The vulnerability affects MLflow deployments running with the '--app-name=basic-auth' flag and carries a CVSS score of 8.1 (High) with network-based attack vector requiring low privilege authentication. This vulnerability was reported via the HackerOne bug bounty platform (@huntr_ai) with no public exploit identified at time of analysis.
MLflow, a popular open-source machine learning lifecycle platform, contains a path traversal vulnerability in its pyfunc extraction process that allows arbitrary file writes. The vulnerability stems from unsafe use of tarfile.extractall without proper path validation, enabling attackers to craft malicious tar.gz files with directory traversal sequences or absolute paths to write files outside the intended extraction directory. This poses critical risk in multi-tenant environments and can lead to remote code execution, with a CVSS score of 8.1 and confirmed exploit details available via Huntr.
Command injection vulnerability in MLflow versions before v3.7.0 that allows attackers to execute arbitrary commands by injecting malicious input through the --container parameter when deploying models to SageMaker. The vulnerability affects MLflow installations in development environments, CI/CD pipelines, and cloud deployments, with a CVSS score of 7.5 indicating high severity. No active exploitation or KEV listing is reported, and no EPSS data is available to assess real-world exploitation likelihood.
In mlflow version 2.20.3, the temporary directory used for creating Python virtual environments is assigned insecure world-writable permissions (0o777). [CVSS 7.0 HIGH]
MLFlow versions up to and including 3.4.0 are vulnerable to DNS rebinding attacks due to a lack of Origin header validation in the MLFlow REST server. This vulnerability allows malicious websites to bypass Same-Origin Policy protections and execute unauthorized calls against REST endpoints. [CVSS 8.1 HIGH]
gateway_proxy_handler in MLflow before 3.1.0 lacks gateway_path validation.
In mlflow/mlflow version 2.18, an admin is able to create a new user account without setting a password. Rated medium severity (CVSS 5.5), this vulnerability is remotely exploitable, low attack complexity. Public exploit code available.
A Cross-Site Request Forgery (CSRF) vulnerability exists in the Signup feature of mlflow/mlflow versions 2.17.0 to 2.20.1. Rated high severity (CVSS 7.1), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
In mlflow/mlflow version 2.17.2, the `/graphql` endpoint is vulnerable to a denial of service attack. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
A path traversal vulnerability exists in mlflow/mlflow version 2.15.1. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and EPSS exploitation probability 26.9%.
In mlflow/mlflow version v2.13.2, a vulnerability exists that allows the creation or renaming of an experiment with a large number of integers in its name due to the lack of a limit on the experiment. Rated medium severity (CVSS 5.3), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
Excessive directory permissions in MLflow leads to local privilege escalation when using spark_udf. Rated high severity (CVSS 7.0). This Incorrect Default Permissions vulnerability could allow attackers to access resources due to overly permissive default settings.
A vulnerability in mlflow/mlflow version 2.11.1 allows attackers to create multiple models with the same name by exploiting URL encoding. Rated medium severity (CVSS 5.4), this vulnerability is remotely exploitable, low attack complexity. Public exploit code available and no vendor patch available.
A Local File Inclusion (LFI) vulnerability was identified in mlflow/mlflow, specifically in version 2.9.2, which was fixed in version 2.11.3. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
A vulnerability in mlflow/mlflow version 8.2.1 allows for remote code execution due to improper neutralization of special elements used in an OS command ('Command Injection') within the. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
Remote Code Execution can occur in versions of the MLflow platform running version 1.11.0 or newer, enabling a maliciously crafted MLproject to execute arbitrary code on an end user’s system when run. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.27.0 or newer, enabling a maliciously crafted Recipe to execute arbitrary code on an end user’s system. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.5.0 or newer, enabling a maliciously uploaded PyTorch model to run arbitrary code on an end user’s. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 2.5.0 or newer, enabling a maliciously uploaded Langchain AgentExecutor model to run arbitrary code on. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 2.0.0rc0 or newer, enabling a maliciously uploaded Tensorflow model to run arbitrary code on an end. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.23.0 or newer, enabling a maliciously uploaded LightGBM scikit-learn model to run arbitrary code on an. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.24.0 or newer, enabling a maliciously uploaded pmdarima model to run arbitrary code on an end user’s. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.9.0 or newer, enabling a maliciously uploaded PyFunc model to run arbitrary code on an end user’s. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.1.0 or newer, enabling a maliciously uploaded scikit-learn model to run arbitrary code on an end. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.1.0 or newer, enabling a maliciously uploaded scikit-learn model to run arbitrary code on an end. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
A broken access control vulnerability exists in mlflow/mlflow versions before 2.10.1, where low privilege users with only EDIT permissions on an experiment can delete any artifacts. Rated medium severity (CVSS 5.4), this vulnerability is remotely exploitable, low attack complexity. Public exploit code available.
A path traversal vulnerability exists in mlflow/mlflow version 2.11.0, identified as a bypass for the previously addressed CVE-2023-6909. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
mlflow/mlflow is vulnerable to Local File Inclusion (LFI) due to improper parsing of URIs, allowing attackers to bypass checks and read arbitrary files on the system. Rated critical severity (CVSS 9.3), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
A path traversal vulnerability exists in the mlflow/mlflow repository, specifically within the handling of the `artifact_location` parameter when creating an experiment. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
A path traversal vulnerability exists in the mlflow/mlflow repository due to improper handling of URL parameters. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
A path traversal vulnerability exists in the mlflow/mlflow repository, specifically within the artifact deletion functionality. Rated high severity (CVSS 8.1), this vulnerability is remotely exploitable, low attack complexity. Public exploit code available and no vendor patch available.
A path traversal vulnerability exists in the `_create_model_version()` function within `server/handlers.py` of the mlflow/mlflow repository, due to improper validation of the `source` parameter. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
A path traversal vulnerability exists in mlflow/mlflow version 2.9.2, allowing attackers to access arbitrary files on the server. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
Insufficient sanitization in MLflow leads to XSS when running a recipe that uses an untrusted dataset. Rated critical severity (CVSS 9.6), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
Insufficient sanitization in MLflow leads to XSS when running an untrusted recipe. Rated critical severity (CVSS 9.6), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
This vulnerability enables malicious users to read sensitive files on the server. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
This vulnerability is capable of writing arbitrary files into arbitrary locations on the remote filesystem in the context of the server process. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, low attack complexity. Public exploit code available.
A malicious user could use this issue to get command execution on the vulnerable machine and get access to data & models information. Rated critical severity (CVSS 9.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
A malicious user could use this issue to access internal HTTP(s) servers and in the worst case (ie: aws instance) it could be abuse to get a remote code execution on the victim machine. Rated critical severity (CVSS 9.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
with only one user interaction(download a malicious config), attackers can gain full command execution on the victim system. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. This Command Injection vulnerability could allow attackers to inject arbitrary commands into system command execution.
Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.9.2. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.9.2. Rated high severity (CVSS 8.1), this vulnerability is remotely exploitable, low attack complexity. Public exploit code available.
Path Traversal in GitHub repository mlflow/mlflow prior to 2.9.2. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
Improper Neutralization of Special Elements Used in a Template Engine in GitHub repository mlflow/mlflow prior to 2.9.2. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, low attack complexity. Public exploit code available.
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. Rated medium severity (CVSS 6.1), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
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. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
An attacker is able to arbitrarily create an account in MLflow bypassing any authentication requirment. Rated critical severity (CVSS 9.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
An attacker can overwrite any file on the server hosting MLflow without any authentication. Rated critical severity (CVSS 9.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
MLflow allowed arbitrary files to be PUT onto the server. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
OS Command Injection in GitHub repository mlflow/mlflow prior to 2.6.0. Rated high severity (CVSS 7.8), this vulnerability is low attack complexity.
Absolute Path Traversal in GitHub repository mlflow/mlflow prior to 2.5.0. Rated critical severity (CVSS 10.0), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.3.1. Rated critical severity (CVSS 9.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
A directory traversal vulnerability in the /get-artifact API method of the mlflow platform up to v2.0.1 allows attackers to read arbitrary files on the server via the path parameter. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. No vendor patch available.
Relative Path Traversal in GitHub repository mlflow/mlflow prior to 2.3.1. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.2.1. Rated critical severity (CVSS 9.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
Absolute Path Traversal in GitHub repository mlflow/mlflow prior to 2.2.2. Rated low severity (CVSS 3.3), this vulnerability is low attack complexity. Public exploit code available.
Insecure Temporary File in GitHub repository mlflow/mlflow prior to 1.23.1. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.