GHSA-wp72-7hj9-5265 BIT-mlflow-2023-1176 CVE-2023-1176 PYSEC-2023-28
Remote file existence check vulnerability in `mlflow server` and `mlflow ui` CLIs
- Severity
- 0.0 / 10
- Low · CVSS 4.0
- Exploit chance (EPSS)
- 0.58%
- Chance of exploitation in the next 30 days. Higher than 47.89% of scored vulnerabilities.
- Known exploited
- No
- Not in CISA's catalog
- Weakness
- CWE-36 Absolute Path Traversal
- Fix
2.2.1- Upgrade mlflow to this version or later
Description
Impact
Users of the MLflow Open Source Project who are hosting the MLflow Model Registry using the mlflow server or mlflow ui commands 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 server and mlflow ui commands. Integrations that do not make use of mlflow server or mlflow ui are 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 server or mlflow ui with 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 server or mlflow ui commands, 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 server or mlflow ui commands, 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.
Affected versions
PyPI
How to fix
Move to the fixed version or later with your package manager, update the lockfile, and run your tests. If the package only arrives through another dependency, upgrade that dependency first. If none of its releases pulls a fixed version yet, force the version with your package manager's override.
mlflow PyPI → 2.2.1
- pip
pip install --upgrade "mlflow>=2.2.1"- poetry
poetry add "mlflow>=2.2.1"- uv
uv add "mlflow>=2.2.1"
Severity breakdown
CVSS 4.0 0.0
CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:L/VI:N/VA:N/SC:N/SI:N/SA:N
- Attack vector
- Local
- Attack complexity
- Low
- Attack requirements
- None
- Privileges required
- Low
- User interaction
- None
- Confidentiality (vulnerable system)
- Low
- Integrity (vulnerable system)
- None
- Availability (vulnerable system)
- None
- Confidentiality (subsequent systems)
- None
- Integrity (subsequent systems)
- None
- Availability (subsequent systems)
- None
CVSS 3.1 3.3
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:N
- Attack vector
- Local
- Attack complexity
- Low
- Privileges required
- Low
- User interaction
- None
- Scope
- Unchanged
- Confidentiality
- Low
- Integrity
- None
- Availability
- None
Weakness
CWE-36: Absolute Path Traversal
The product uses external input to construct a pathname that should be within a restricted directory, but it does not properly neutralize absolute path sequences such as "/abs/path" that can resolve to a location that is outside of that directory.
How to prevent it
- Implementation: Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does. When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue." Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright. When validating filenames, use stringent allowlists that limit the character set to be used. If feasible, only allow a single "." character in the filename to avoid weaknesses such as CWE-23, and exclude directory separators such as "/" to avoid CWE-36. Use a list of allowable file extensions, which will help to avoid CWE-434. Do not rely exclusively on a filtering mechanism that removes potentially dangerous characters. This is equivalent to a denylist, which may be incomplete (CWE-184). For example, filtering "/" is insufficient protection if the filesystem also supports the use of "\" as a directory separator. Another possible error could occur when the filtering is applied in a way that still produces dangerous data (CWE-182). For example, if "../" sequences are removed from the ".../...//" string in a sequential fashion, two instances of "../" would b
- Implementation: Inputs should be decoded and canonicalized to the application's current internal representation before being validated (CWE-180). Make sure that the application does not decode the same input twice (CWE-174). Such errors could be used to bypass allowlist validation schemes by introducing dangerous inputs after they have been checked.
- Operation: Use an application firewall that can detect attacks against this weakness. It can be beneficial in cases in which the code cannot be fixed (because it is controlled by a third party), as an emergency prevention measure while more comprehensive software assurance measures are applied, or to provide defense in depth [REF-1481].
References
Advisories
Packages
Other links
Timeline
- Published
- Last updated by the source
- Latest EPSS score
Related advisories
| Advisory | Severity | Why it's related |
|---|---|---|
| GHSA-7gwp-5pfp-969j MLflow: Unauthenticated full-read SSRF in webhook delivery: _validate_webhook_url bypassed via unvalidated HTTP redirects (and DNS rebinding) | Critical Known exploited | Also affects mlflow |
| GHSA-26p8-2jq9-3vq9 MLflow vulnerable to remote code execution on unauthenticated servers via third-party scorer deserialization importing caller-placed artifact modules | Critical | Also affects mlflow |
| GHSA-j7qq-vr8c-xvw3 MLflow CrewAI autologging records LLM provider API keys in logged trace spans | Medium | Also affects mlflow |
| GHSA-m65p-9j7q-6w33 MLflow vulnerable to arbitrary file read on unauthenticated servers via prompt model version source | High | Also affects mlflow |
| GHSA-2wqp-q3c6-ccj5 MLflow: SSRF on unauthenticated MLflow servers via third-party scorer kwargs in the scorer invoke, register, and GenAI evaluate endpoints | Medium | Also affects mlflow |
| GHSA-wqmh-qccw-pqwf MLflow FileStore trace IDs allow path traversal and arbitrary file writes | High | Also affects mlflow |
| GHSA-gqvg-gmmx-x4hm MLFLOW_ALLOW_PICKLE_DESERIALIZATION=False safety control bypassed by mlflow.statsmodels flavor — RCE via crafted model artifact | High | Also affects mlflow |
| GHSA-h7x2-h6g9-p789 MLflow AI Gateway permits SSRF through an unvalidated api_base | High | Also affects mlflow |
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