CVE-2024-1483: MLflow: path traversal exposes arbitrary server files
HIGH PoC AVAILABLE NUCLEI TEMPLATE CISA: TRACK*MLflow 2.9.2 allows unauthenticated attackers to read any file on the server via crafted artifact_location parameters — no credentials needed, network access is enough. If MLflow is reachable from untrusted networks (including internal segments with lateral movement risk), treat this as critical. Patch immediately or isolate the service behind a VPN/firewall; assume credential exposure if exploitation cannot be ruled out.
What is the risk?
Effective risk is CRITICAL in typical MLOps deployments despite the 7.5 CVSS score. The zero-auth requirement (PR:N, UI:N, AC:L) means any network-adjacent attacker can exfiltrate files without prior foothold. MLflow instances routinely store environment files, cloud provider credentials, database connection strings, and model configs in accessible paths. In organizations where MLflow is exposed on internal networks with flat segmentation, lateral movement significantly amplifies the blast radius.
What systems are affected?
| Package | Ecosystem | Vulnerable Range | Patched |
|---|---|---|---|
| MLflow | pip | — | No patch |
Do you use MLflow? You're affected.
How severe is it?
What is the attack surface?
What should I do?
5 steps-
PATCH
Upgrade MLflow to the latest release immediately; the fix was applied post-2.9.2.
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ISOLATE
Restrict MLflow tracking server to internal VPN-only access; block port 5000 from untrusted segments via firewall or security group rules.
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AUDIT
Review server access logs for anomalous POST requests to /api/2.0/mlflow/runs/create or experiment endpoints with artifact_location or source parameters containing '../', '%2e%2e', or '#' characters.
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ROTATE
If exposure cannot be ruled out, rotate all credentials (cloud keys, DB passwords, API tokens) accessible from the MLflow server filesystem.
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DETECT
Add WAF rule or IDS signature matching path traversal patterns in artifact_location/source parameters in POST bodies to MLflow endpoints.
What does CISA's SSVC say?
Source: CISA Vulnrichment (SSVC v2.0). Decision based on the CISA Coordinator decision tree.
How is it classified?
Which compliance frameworks are affected?
This CVE is relevant to:
Frequently Asked Questions
What is CVE-2024-1483?
MLflow 2.9.2 allows unauthenticated attackers to read any file on the server via crafted artifact_location parameters — no credentials needed, network access is enough. If MLflow is reachable from untrusted networks (including internal segments with lateral movement risk), treat this as critical. Patch immediately or isolate the service behind a VPN/firewall; assume credential exposure if exploitation cannot be ruled out.
Is CVE-2024-1483 actively exploited?
Proof-of-concept exploit code is publicly available for CVE-2024-1483, increasing the risk of exploitation.
How to fix CVE-2024-1483?
1. PATCH: Upgrade MLflow to the latest release immediately; the fix was applied post-2.9.2. 2. ISOLATE: Restrict MLflow tracking server to internal VPN-only access; block port 5000 from untrusted segments via firewall or security group rules. 3. AUDIT: Review server access logs for anomalous POST requests to /api/2.0/mlflow/runs/create or experiment endpoints with artifact_location or source parameters containing '../', '%2e%2e', or '#' characters. 4. ROTATE: If exposure cannot be ruled out, rotate all credentials (cloud keys, DB passwords, API tokens) accessible from the MLflow server filesystem. 5. DETECT: Add WAF rule or IDS signature matching path traversal patterns in artifact_location/source parameters in POST bodies to MLflow endpoints.
What systems are affected by CVE-2024-1483?
This vulnerability affects the following AI/ML architecture patterns: MLOps platforms, training pipelines, experiment tracking infrastructure, model registry, CI/CD for ML.
What is the CVSS score for CVE-2024-1483?
CVE-2024-1483 has a CVSS v3.1 base score of 7.5 (HIGH). The EPSS exploitation probability is 2.72%.
What is the AI security impact?
Affected AI Architectures
MITRE ATLAS Techniques
AML.T0007 Discover AI Artifacts AML.T0025 Exfiltration via Cyber Means AML.T0035 AI Artifact Collection AML.T0037 Data from Local System AML.T0049 Exploit Public-Facing Application Compliance Controls Affected
What are the technical details?
Original Advisory
A path traversal vulnerability exists in mlflow/mlflow version 2.9.2, allowing attackers to access arbitrary files on the server. By crafting a series of HTTP POST requests with specially crafted 'artifact_location' and 'source' parameters, using a local URI with '#' instead of '?', an attacker can traverse the server's directory structure. The issue occurs due to insufficient validation of user-supplied input in the server's handlers.
Exploitation Scenario
An adversary scanning an internal data science network finds an exposed MLflow tracking server. Using a single unauthenticated HTTP POST to the runs/create API with artifact_location set to a local URI like file:///etc/../etc/passwd#experiment or a crafted source parameter, the attacker reads /etc/passwd to enumerate users. Escalating, they target common MLflow artifact storage paths to retrieve .env files containing AWS_ACCESS_KEY_ID and database DSNs used by the training pipeline. With cloud credentials in hand, the attacker pivots to S3/GCS to exfiltrate proprietary model weights and training datasets — all without ever compromising a user account.
Weaknesses (CWE)
CWE-22 — Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal'): The product uses external input to construct a pathname that is intended to identify a file or directory that is located underneath a restricted parent directory, but the product does not properly neutralize special elements within the pathname that can cause the pathname to resolve to a location that is outside of the restricted directory.
- [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, denylis
- [Architecture and Design] For any security checks that are performed on the client side, ensure that these checks are duplicated on the server side, in order to avoid CWE-602. Attackers can bypass the client-side checks by modifying values after the checks have been performed, or by changing the client to remove the client-side checks entirely. Then, these modified values would be submitted to the server.
Source: MITRE CWE corpus.
CVSS Vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N References
- huntr.com/bounties/52a3855d-93ff-4460-ac24-9c7e4334198d Exploit Issue 3rd Party
- github.com/ARPSyndicate/cve-scores Exploit
Timeline
Scanner Template Available
A Nuclei vulnerability scanner template exists for this CVE. You can scan your infrastructure for this vulnerability immediately.
View template on GitHubnuclei -t http/cves/2024/CVE-2024-1483.yaml -u https://target.example.com Related Vulnerabilities
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