CVE-2023-1177: MLflow: path traversal allows arbitrary file read/write
CRITICALACTIVELY EXPLOITEDPoC AVAILABLENUCLEI TEMPLATECISA: TRACK*Unauthenticated path traversal in MLflow (CVSS 9.8) lets any network-reachable attacker read or write arbitrary files on your ML platform server. If MLflow is reachable from untrusted networks — including internal segments without strong access controls — treat this as active compromise risk: attackers can steal models, training data, and credentials stored on the filesystem. Patch to 2.2.1 immediately and audit all MLflow network exposure.
What is the risk?
Extremely high. CVSS 9.8 with no authentication, no user interaction, and full CIA impact. MLflow servers are routinely deployed without authentication enabled on internal networks or even internet-facing, making this trivially exploitable by automated scanners. MLflow instances hold high-value AI assets — trained models, experiment artifacts, hyperparameters, cloud credentials — making them an attractive target. The low attack complexity means weaponized exploits are accessible to low-skill actors.
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?
6 stepsPATCH
Upgrade MLflow to 2.2.1 or later immediately — this is the only complete fix.
ISOLATE
Ensure MLflow is not exposed to the public internet; restrict access via firewall rules and network segmentation.
AUTHENTICATE
Enable authentication if supported by your version; otherwise place behind an authenticated reverse proxy (nginx + OAuth2 proxy, Cloudflare Access).
AUDIT LOGS
Search server logs for path traversal patterns — sequences containing ../, ..\ or URL-encoded equivalents (%2e%2e%2f) in artifact or file endpoint requests.
ROTATE CREDENTIALS
Assume all secrets stored on the MLflow server filesystem (API keys, .env files, ~/.aws/credentials, database passwords) are compromised; rotate immediately.
INVENTORY
Identify all MLflow instances across environments (dev, staging, prod) and confirm patch status for each.
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-2023-1177?
Unauthenticated path traversal in MLflow (CVSS 9.8) lets any network-reachable attacker read or write arbitrary files on your ML platform server. If MLflow is reachable from untrusted networks — including internal segments without strong access controls — treat this as active compromise risk: attackers can steal models, training data, and credentials stored on the filesystem. Patch to 2.2.1 immediately and audit all MLflow network exposure.
Is CVE-2023-1177 actively exploited?
Yes, CVE-2023-1177 is confirmed actively exploited and listed in CISA Known Exploited Vulnerabilities catalog.
How to fix CVE-2023-1177?
1. PATCH: Upgrade MLflow to 2.2.1 or later immediately — this is the only complete fix. 2. ISOLATE: Ensure MLflow is not exposed to the public internet; restrict access via firewall rules and network segmentation. 3. AUTHENTICATE: Enable authentication if supported by your version; otherwise place behind an authenticated reverse proxy (nginx + OAuth2 proxy, Cloudflare Access). 4. AUDIT LOGS: Search server logs for path traversal patterns — sequences containing ../, ..\ or URL-encoded equivalents (%2e%2e%2f) in artifact or file endpoint requests. 5. ROTATE CREDENTIALS: Assume all secrets stored on the MLflow server filesystem (API keys, .env files, ~/.aws/credentials, database passwords) are compromised; rotate immediately. 6. INVENTORY: Identify all MLflow instances across environments (dev, staging, prod) and confirm patch status for each.
What systems are affected by CVE-2023-1177?
This vulnerability affects the following AI/ML architecture patterns: MLOps platforms, model registry, training pipelines, experiment tracking systems, model serving infrastructure.
What is the CVSS score for CVE-2023-1177?
CVE-2023-1177 has a CVSS v3.1 base score of 9.8 (CRITICAL). The EPSS exploitation probability is 69.68%.
What is the AI security impact?
Affected AI Architectures
MITRE ATLAS Techniques
AML.T0010.001AI SoftwareAML.T0018Manipulate AI ModelAML.T0025Exfiltration via Cyber MeansAML.T0035AI Artifact CollectionAML.T0037Data from Local SystemAML.T0049Exploit Public-Facing ApplicationAML.T0055Unsecured CredentialsCompliance Controls Affected
What are the technical details?
Original Advisory
Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.2.1.
Exploitation Scenario
An adversary scans corporate cloud environments and identifies an unpatched MLflow tracking server accessible on an internal VPC segment. Without credentials, they send a crafted HTTP GET to the artifact API with a path traversal sequence to enumerate filesystem structure. Within minutes they retrieve ~/.aws/credentials, obtaining cloud access keys with broad S3 and SageMaker permissions. They then read stored model artifact files — obtaining proprietary model weights trained on sensitive data. As a final stage, they overwrite a registered production model binary with a backdoored version that, when loaded by the serving infrastructure, silently exfiltrates a copy of every inference request payload to an attacker-controlled endpoint. The attack produces no authentication failures and blends into normal artifact access patterns.
Weaknesses (CWE)
CWE-22Improper Limitation of a Pathname to a Restricted DirectoryCWE-29Path Traversal: '\..\filename'CWE-22 — Improper Limitation of a Pathname to a Restricted Directory: 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:H/A:HReferences
- github.com/mlflow/mlflow/pull/7891/commits/7162a50c654792c21f3e4a160eb1a0e6a34f6e6ePatch
- huntr.dev/bounties/1fe8f21a-c438-4cba-9add-e8a5dab94e28ExploitPatch3rd Party
- github.com/0day404/vulnerability-pocExploit
- github.com/ARPSyndicate/cvemonExploit
- github.com/EssenceCyber/Exploit-ListExploit
- github.com/J1ezds/Vulnerability-Wiki-pageExploit
- github.com/KayCHENvip/vulnerability-pocExploit
- github.com/SpycioKon/CVE-2023-1177-rebuildExploit
- github.com/Threekiii/Awesome-POCExploit
- github.com/XiaomingX/awesome-poc-for-red-teamExploit
- github.com/alphandbelt1/CVE-2023-1177-MLFlowExploit
- github.com/charlesgargasson/CVE-2023-1177Exploit
- github.com/charlesgargasson/charlesgargassonExploit
- github.com/d4n-sec/d4n-sec.github.ioExploit
- github.com/google/tsunami-security-scanner-pluginsExploit
- github.com/hh-hunter/ml-CVE-2023-1177Exploit
- github.com/intercladire/Awesome-POCExploit
- github.com/iumiro/CVE-2023-1177-MLFlowExploit
- github.com/nomi-sec/PoC-in-GitHubExploit
- github.com/paultheal1en/CVE-2023-1177-PoC-reproduceExploit
- github.com/plzheheplztrying/cve_monitorExploit
- github.com/powned/Snaike-MLflowExploit
- github.com/protectai/Snaike-MLflowExploit
- github.com/saimahmed/MLflow-VulnExploit
- github.com/tiyeume25112004/CVE-2023-1177-rebuildExploit
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/2023/CVE-2023-1177.yaml -u https://target.example.comRelated Vulnerabilities
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