Vulnerabilities (CVE)

Valkey-Bloom is a Rust based Valkey module which brings a Bloom Filter (Module) data type into the Valkey distributed key-value database. Prior to commit a68614b6e3845777d383b3a513cedcc08b3b7ccd, a specially crafted `RESTORE` command can cause Valkey to hit an assertion, causes the server to shutdown. Valkey modules are required to handle errors in RDB parsing by using `VALKEYMODULE_OPTIONS_HANDLE_IO_ERRORS` flag. If this flag is not set, errors encountered during parsing result in a system asse ... Valkey-Bloom is a Rust based Valkey module which brings a Bloom Filter (Module) data type into the Valkey distributed key-value database. Prior to commit a68614b6e3845777d383b3a513cedcc08b3b7ccd, a specially crafted `RESTORE` command can cause Valkey to hit an assertion, causes the server to shutdown. Valkey modules are required to handle errors in RDB parsing by using `VALKEYMODULE_OPTIONS_HANDLE_IO_ERRORS` flag. If this flag is not set, errors encountered during parsing result in a system assertion which shuts down the system. Even though the Valkey-bloom module correctly handled the parsing, it did not originally set the flag. Commit a68614b6e3845777d383b3a513cedcc08b3b7ccd contains a patch. One may mitigate this defect by disabling the `RESTORE` command if it is unused by one's application.

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Valkey is a distributed key-value database. Starting in version 9.0.0 and prior to version 9.0.3, a malicious actor with network access to Valkey can cause the system to abort by triggering an assertion. When processing incoming requests, the Valkey system does not properly reset the networking state after processing an empty request. A malicious actor can then send a request that the server incorrectly identifies as breaking server side invariants, which results in the server shutting down. Ver ... Valkey is a distributed key-value database. Starting in version 9.0.0 and prior to version 9.0.3, a malicious actor with network access to Valkey can cause the system to abort by triggering an assertion. When processing incoming requests, the Valkey system does not properly reset the networking state after processing an empty request. A malicious actor can then send a request that the server incorrectly identifies as breaking server side invariants, which results in the server shutting down. Version 9.0.3 fixes the issue. As an additional mitigation, properly isolate Valkey deployments so that only trusted users have access.

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Valkey is a distributed key-value database. Prior to versions 9.0.2, 8.1.6, 8.0.7, and 7.2.12, a malicious actor with access to the Valkey clusterbus port can send an invalid packet that may cause an out bound read, which might result in the system crashing. The Valkey clusterbus packet processing code does not validate that a clusterbus ping extension packet is located within buffer of the clusterbus packet before attempting to read it. Versions 9.0.2, 8.1.6, 8.0.7, and 7.2.12 fix the issue. As ... Valkey is a distributed key-value database. Prior to versions 9.0.2, 8.1.6, 8.0.7, and 7.2.12, a malicious actor with access to the Valkey clusterbus port can send an invalid packet that may cause an out bound read, which might result in the system crashing. The Valkey clusterbus packet processing code does not validate that a clusterbus ping extension packet is located within buffer of the clusterbus packet before attempting to read it. Versions 9.0.2, 8.1.6, 8.0.7, and 7.2.12 fix the issue. As an additional mitigation, don't expose the cluster bus connection directly to end users, and protect the connection with its own network ACLs.

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Valkey is a distributed key-value database. Prior to versions 9.0.2, 8.1.6, 8.0.7, and 7.2.12, a malicious user can use scripting commands to inject arbitrary information into the response stream for the given client, potentially corrupting or returning tampered data to other users on the same connection. The error handling code for lua scripts does not properly handle null characters. Versions 9.0.2, 8.1.6, 8.0.7, and 7.2.12 fix the issue.

Redis is an open source, in-memory database that persists on disk. In versions starting at 2.6 and prior to 7.4.3, An unauthenticated client can cause unlimited growth of output buffers, until the server runs out of memory or is killed. By default, the Redis configuration does not limit the output buffer of normal clients (see client-output-buffer-limit). Therefore, the output buffer can grow unlimitedly over time. As a result, the service is exhausted and the memory is unavailable. When passwor ... Redis is an open source, in-memory database that persists on disk. In versions starting at 2.6 and prior to 7.4.3, An unauthenticated client can cause unlimited growth of output buffers, until the server runs out of memory or is killed. By default, the Redis configuration does not limit the output buffer of normal clients (see client-output-buffer-limit). Therefore, the output buffer can grow unlimitedly over time. As a result, the service is exhausted and the memory is unavailable. When password authentication is enabled on the Redis server, but no password is provided, the client can still cause the output buffer to grow from "NOAUTH" responses until the system will run out of memory. This issue has been patched in version 7.4.3. An additional workaround to mitigate this problem without patching the redis-server executable is to block access to prevent unauthenticated users from connecting to Redis. This can be done in different ways. Either using network access control tools like firewalls, iptables, security groups, etc, or enabling TLS and requiring users to authenticate using client side certificates.

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Anthropic's MCP TypeScript SDK versions up to and including 1.25.1 contain a regular expression denial of service (ReDoS) vulnerability in the UriTemplate class when processing RFC 6570 exploded array patterns. The dynamically generated regular expression used during URI matching contains nested quantifiers that can trigger catastrophic backtracking on specially crafted inputs, resulting in excessive CPU consumption. An attacker can exploit this by supplying a malicious URI that causes the Node. ... Anthropic's MCP TypeScript SDK versions up to and including 1.25.1 contain a regular expression denial of service (ReDoS) vulnerability in the UriTemplate class when processing RFC 6570 exploded array patterns. The dynamically generated regular expression used during URI matching contains nested quantifiers that can trigger catastrophic backtracking on specially crafted inputs, resulting in excessive CPU consumption. An attacker can exploit this by supplying a malicious URI that causes the Node.js process to become unresponsive, leading to a denial of service.

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Minder is a software supply chain security platform. Prior to version 0.0.33, a Minder user can use the endpoints `GetRepositoryByName`, `DeleteRepositoryByName`, and `GetArtifactByName` to access any repository in the database, irrespective of who owns the repo and any permissions present. The database query checks by repo owner, repo name and provider name (which is always `github`). These query values are not distinct for the particular user - as long as the user has valid credentials and a p ... Minder is a software supply chain security platform. Prior to version 0.0.33, a Minder user can use the endpoints `GetRepositoryByName`, `DeleteRepositoryByName`, and `GetArtifactByName` to access any repository in the database, irrespective of who owns the repo and any permissions present. The database query checks by repo owner, repo name and provider name (which is always `github`). These query values are not distinct for the particular user - as long as the user has valid credentials and a provider, they can set the repo owner/name to any value they want and the server will return information on this repo. Version 0.0.33 contains a patch for this issue.

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MLflow Weak Password Requirements Authentication Bypass Vulnerability. This vulnerability allows remote attackers to bypass authentication on affected installations of MLflow. Authentication is not required to exploit this vulnerability. The specific flaw exists within the handling of passwords. The issue results from weak password requirements. An attacker can leverage this vulnerability to bypass authentication on the system. Was ZDI-CAN-26916.

Apptainer is an open source container platform. In Apptainer versions less than 1.4.5, a container can disable two of the forms of the little used --security option, in particular the forms --security=apparmor:<profile> and --security=selinux:<label> which otherwise put restrictions on operations that containers can do. The --security option has always been mentioned in Apptainer documentation as being a feature for the root user, although these forms do also work for unprivileged users on syste ... Apptainer is an open source container platform. In Apptainer versions less than 1.4.5, a container can disable two of the forms of the little used --security option, in particular the forms --security=apparmor:<profile> and --security=selinux:<label> which otherwise put restrictions on operations that containers can do. The --security option has always been mentioned in Apptainer documentation as being a feature for the root user, although these forms do also work for unprivileged users on systems where the corresponding feature is enabled. Apparmor is enabled by default on Debian-based distributions and SElinux is enabled by default on RHEL-based distributions, but on SUSE it depends on the distribution version. This vulnerability is fixed in 1.4.5.

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Redis is an open source, in-memory database that persists on disk. Versions 8.2.1 and below allow an authenticated user to use a specially crafted Lua script to manipulate the garbage collector, trigger a use-after-free and potentially lead to remote code execution. The problem exists in all versions of Redis with Lua scripting. This issue is fixed in version 8.2.2. To workaround this issue without patching the redis-server executable is to prevent users from executing Lua scripts. This can be d ... Redis is an open source, in-memory database that persists on disk. Versions 8.2.1 and below allow an authenticated user to use a specially crafted Lua script to manipulate the garbage collector, trigger a use-after-free and potentially lead to remote code execution. The problem exists in all versions of Redis with Lua scripting. This issue is fixed in version 8.2.2. To workaround this issue without patching the redis-server executable is to prevent users from executing Lua scripts. This can be done using ACL to restrict EVAL and EVALSHA commands.

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MLflow Tracking Server Model Creation Directory Traversal Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of MLflow Tracking Server. Authentication is not required to exploit this vulnerability. The specific flaw exists within the handling of model file paths. The issue results from the lack of proper validation of a user-supplied path prior to using it in file operations. An attacker can leverage this vulnerabi ... MLflow Tracking Server Model Creation Directory Traversal Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of MLflow Tracking Server. Authentication is not required to exploit this vulnerability. The specific flaw exists within the handling of model file paths. The issue results from the lack of proper validation of a user-supplied path prior to using it in file operations. An attacker can leverage this vulnerability to execute code in the context of the service account. Was ZDI-CAN-26921.

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In mlflow/mlflow version 2.17.2, the `/graphql` endpoint is vulnerable to a denial of service attack. An attacker can create large batches of queries that repeatedly request all runs from a given experiment. This can tie up all the workers allocated by MLFlow, rendering the application unable to respond to other requests. This vulnerability is due to uncontrolled resource consumption.

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 `mlflow.data.http_dataset_source.py` module. Specifically, when loading a dataset from a source URL with an HTTP scheme, the filename extracted from the `Content-Disposition` header or the URL path is used to generate the final file path without proper sanitization. This flaw enables an attacker to control the fi ... 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 `mlflow.data.http_dataset_source.py` module. Specifically, when loading a dataset from a source URL with an HTTP scheme, the filename extracted from the `Content-Disposition` header or the URL path is used to generate the final file path without proper sanitization. This flaw enables an attacker to control the file path fully by utilizing path traversal or absolute path techniques, such as '../../tmp/poc.txt' or '/tmp/poc.txt', leading to arbitrary file write. Exploiting this vulnerability could allow a malicious user to execute commands on the vulnerable machine, potentially gaining access to data and model information. The issue is fixed in version 2.9.0.

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A Cross-Site Request Forgery (CSRF) vulnerability exists in the Signup feature of mlflow/mlflow versions 2.17.0 to 2.20.1. This vulnerability allows an attacker to create a new account, which may be used to perform unauthorized actions on behalf of the malicious user.

A path traversal vulnerability exists in mlflow/mlflow version 2.15.1. When users configure and use the dbfs service, concatenating the URL directly into the file protocol results in an arbitrary file read vulnerability. This issue occurs because only the path part of the URL is checked, while parts such as query and parameters are not handled. The vulnerability is triggered if the user has configured the dbfs service, and during usage, the service is mounted to a local directory.

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 name. This can cause the MLflow UI panel to become unresponsive, leading to a potential denial of service. Additionally, there is no character limit in the `artifact_location` parameter while creating the experiment.

In mlflow/mlflow version 2.18, an admin is able to create a new user account without setting a password. This vulnerability could lead to security risks, as accounts without passwords may be susceptible to unauthorized access. Additionally, this issue violates best practices for secure user account management. The issue is fixed in version 2.19.0.

FP.io VPP (Vector Packet Processor) 22.10, 22.06, 22.02, 21.10, 21.06, 21.01, 20.09, 20.05, 20.01, 19.08, and 19.04 Generates a Predictable IV with CBC Mode.

Minder is a Software Supply Chain Security Platform. In version 0.0.31 and earlier, it is possible for an attacker to register a repository with a invalid or differing upstream ID, which causes Minder to report the repository as registered, but not remediate any future changes which conflict with policy (because the webhooks for the repo do not match any known repository in the database). When attempting to register a repo with a different repo ID, the registered provider must have admin on the ... Minder is a Software Supply Chain Security Platform. In version 0.0.31 and earlier, it is possible for an attacker to register a repository with a invalid or differing upstream ID, which causes Minder to report the repository as registered, but not remediate any future changes which conflict with policy (because the webhooks for the repo do not match any known repository in the database). When attempting to register a repo with a different repo ID, the registered provider must have admin on the named repo, or a 404 error will result. Similarly, if the stored provider token does not have repo access, then the remediations will not apply successfully. Lastly, it appears that reconciliation actions do not execute against repos with this type of mismatch. This appears to primarily be a potential denial-of-service vulnerability. This vulnerability is patched in version 0.20240226.1425+ref.53868a8.

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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. This issue arises due to the lack of proper validation for DELETE requests by users with EDIT permissions, allowing them to perform unauthorized deletions of artifacts. The vulnerability specifically affects the handling of artifact deletions within the application, as demonstrated by the ability of a low privilege u ... 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. This issue arises due to the lack of proper validation for DELETE requests by users with EDIT permissions, allowing them to perform unauthorized deletions of artifacts. The vulnerability specifically affects the handling of artifact deletions within the application, as demonstrated by the ability of a low privilege user to delete a directory inside an artifact using a DELETE request, despite the official documentation stating that users with EDIT permission can only read and update artifacts, not delete them.

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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. The issue arises from the 'is_local_uri' function's failure to properly handle URIs with empty or 'file' schemes, leading to the misclassification of URIs as non-local. Attackers can exploit this by crafting malicious model versions with specially crafted 'source' parameters, enabling the reading of sensitive files within at least ... 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. The issue arises from the 'is_local_uri' function's failure to properly handle URIs with empty or 'file' schemes, leading to the misclassification of URIs as non-local. Attackers can exploit this by crafting malicious model versions with specially crafted 'source' parameters, enabling the reading of sensitive files within at least two directory levels from the server's root.

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A path traversal vulnerability exists in the mlflow/mlflow repository due to improper handling of URL parameters. By smuggling path traversal sequences using the ';' character in URLs, attackers can manipulate the 'params' portion of the URL to gain unauthorized access to files or directories. This vulnerability allows for arbitrary data smuggling into the 'params' part of the URL, enabling attacks similar to those described in previous reports but utilizing the ';' character for parameter smugg ... A path traversal vulnerability exists in the mlflow/mlflow repository due to improper handling of URL parameters. By smuggling path traversal sequences using the ';' character in URLs, attackers can manipulate the 'params' portion of the URL to gain unauthorized access to files or directories. This vulnerability allows for arbitrary data smuggling into the 'params' part of the URL, enabling attacks similar to those described in previous reports but utilizing the ';' character for parameter smuggling. Successful exploitation could lead to unauthorized information disclosure or server compromise.

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A path traversal vulnerability exists in the mlflow/mlflow repository, specifically within the handling of the `artifact_location` parameter when creating an experiment. Attackers can exploit this vulnerability by using a fragment component `#` in the artifact location URI to read arbitrary files on the server in the context of the server's process. This issue is similar to CVE-2023-6909 but utilizes a different component of the URI to achieve the same effect.

A path traversal vulnerability exists in the mlflow/mlflow repository, specifically within the artifact deletion functionality. Attackers can bypass path validation by exploiting the double decoding process in the `_delete_artifact_mlflow_artifacts` handler and `local_file_uri_to_path` function, allowing for the deletion of arbitrary directories on the server's filesystem. This vulnerability is due to an extra unquote operation in the `delete_artifacts` function of `local_artifact_repo.py`, whic ... A path traversal vulnerability exists in the mlflow/mlflow repository, specifically within the artifact deletion functionality. Attackers can bypass path validation by exploiting the double decoding process in the `_delete_artifact_mlflow_artifacts` handler and `local_file_uri_to_path` function, allowing for the deletion of arbitrary directories on the server's filesystem. This vulnerability is due to an extra unquote operation in the `delete_artifacts` function of `local_artifact_repo.py`, which fails to properly sanitize user-supplied paths. The issue is present up to version 2.9.2, despite attempts to fix a similar issue in CVE-2023-6831.

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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. Attackers can exploit this vulnerability by crafting a `source` parameter that bypasses the `_validate_non_local_source_contains_relative_paths(source)` function's checks, allowing for arbitrary file read access on the server. The issue arises from the handling of unquoted URL characters and the subseque ... 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. Attackers can exploit this vulnerability by crafting a `source` parameter that bypasses the `_validate_non_local_source_contains_relative_paths(source)` function's checks, allowing for arbitrary file read access on the server. The issue arises from the handling of unquoted URL characters and the subsequent misuse of the original `source` value for model version creation, leading to the exposure of sensitive files when interacting with the `/model-versions/get-artifact` handler.

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Excessive directory permissions in MLflow leads to local privilege escalation when using spark_udf. This behavior can be exploited by a local attacker to gain elevated permissions by using a ToCToU attack. The issue is only relevant when the spark_udf() MLflow API is called.

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.

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.

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 when run.

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 system when interacted with.

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 an end user’s system when interacted with.

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 user’s system when interacted with.

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 end user’s system when interacted with.

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 system when interacted with.

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 system when interacted with.

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 user’s system when interacted with.

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 user’s system when interacted with.

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.

A path traversal vulnerability exists in mlflow/mlflow version 2.11.0, identified as a bypass for the previously addressed CVE-2023-6909. The vulnerability arises from the application's handling of artifact URLs, where a '#' character can be used to insert a path into the fragment, effectively skipping validation. This allows an attacker to construct a URL that, when processed, ignores the protocol scheme and uses the provided path for filesystem access. As a result, an attacker can read arbitra ... A path traversal vulnerability exists in mlflow/mlflow version 2.11.0, identified as a bypass for the previously addressed CVE-2023-6909. The vulnerability arises from the application's handling of artifact URLs, where a '#' character can be used to insert a path into the fragment, effectively skipping validation. This allows an attacker to construct a URL that, when processed, ignores the protocol scheme and uses the provided path for filesystem access. As a result, an attacker can read arbitrary files, including sensitive information such as SSH and cloud keys, by exploiting the way the application converts the URL into a filesystem path. The issue stems from insufficient validation of the fragment portion of the URL, leading to arbitrary file read through path traversal.

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Insufficient sanitization in MLflow leads to XSS when running an untrusted recipe. This issue leads to a client-side RCE when running an untrusted recipe in Jupyter Notebook. The vulnerability stems from lack of sanitization over template variables.