#git

A stored Cross-Site Scripting (XSS) vulnerability was identified in the zenml-io/zenml repository, specifically within the 'logo_url' field. By injecting malicious payloads into this field, an attacker could send harmful messages to other users, potentially compromising their accounts. The vulnerability affects version 0.55.3 and was fixed in version 0.56.2. The impact of exploiting this vulnerability could lead to user account compromise.

A vulnerability in mlflow/mlflow version 2.11.1 allows attackers to create multiple models with the same name by exploiting URL encoding. This flaw can lead to Denial of Service (DoS) as an authenticated user might not be able to use the intended model, as it will open a different model each time. Additionally, an attacker can exploit this vulnerability to perform data model poisoning by creating a model with the same name, potentially causing an authenticated user to become a victim by using the poisoned model. The issue stems from inadequate validation of model names, allowing for the creation of models with URL-encoded names that are treated as distinct from their URL-decoded counterparts.

A Denial-of-Service (DoS) vulnerability exists in the `SitemapLoader` class of the `langchain-ai/langchain` repository, affecting all versions. The `parse_sitemap` method, responsible for parsing sitemaps and extracting URLs, lacks a mechanism to prevent infinite recursion when a sitemap URL refers to the current sitemap itself. This oversight allows for the possibility of an infinite loop, leading to a crash by exceeding the maximum recursion depth in Python. This vulnerability can be exploited to occupy server socket/port resources and crash the Python process, impacting the availability of services relying on this functionality.

An issue was discovered in zenml-io/zenml versions up to and including 0.55.4. Due to improper authentication mechanisms, an attacker with access to an active user session can change the account password without needing to know the current password. This vulnerability allows for unauthorized account takeover by bypassing the standard password change verification process. The issue was fixed in version 0.56.3.

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.

# Summary The CVE allows unauthorized access to the sensitive settings exposed by /api/v1/settings endpoint without authentication. # Details ## **Unauthenticated Access:** ### Endpoint: /api/v1/settings Description: This endpoint is accessible without any form of authentication as expected. All sensitive settings are hidden except `passwordPattern`. Patches A patch for this vulnerability has been released in the following Argo CD versions: v2.11.3 v2.10.12 v2.9.17 # Impact ## Unauthenticated Access: * Type: Unauthorized Information Disclosure. * Affected Parties: All users and administrators of the Argo CD instance. * Potential Risks: Exposure of sensitive configuration data, including but not limited to deployment settings, security configurations, and internal network information.

### Summary Jupyter Server on Windows has a vulnerability that lets unauthenticated attackers leak the NTLMv2 password hash of the Windows user running the Jupyter server. An attacker can crack this password to gain access to the Windows machine hosting the Jupyter server, or access other network-accessible machines or 3rd party services using that credential. Or an attacker perform an NTLM relay attack without cracking the credential to gain access to other network-accessible machines.

The number of alleged hacks targeting the customers of cloud storage firm Snowflake appears to be snowballing into one of the biggest data breaches of all time.

Boelter Blue System Management version 1.3 suffers from a remote SQL injection vulnerability.

### Impact _What kind of vulnerability is it? Who is impacted?_ At the moment, users are able to delegate tokens that have not yet been vested. This affects employees and grantees who have funds managed via `ClawbackVestingAccount`. ### Patches _Has the problem been patched? What versions should users upgrade to?_ [The PR linked to this advisory](https://github.com/evmos/evmos-ghsa-7hrh-v6wp-53vw/pull/1) includes part of the fix. The remainder is in a [second advisory on the Cosmos SDK fork](https://github.com/evmos/cosmos-sdk/security/advisories/GHSA-wj6f-x5wv-8pqv). ### Workarounds _Is there a way for users to fix or remediate the vulnerability without upgrading?_ There is no effective workaround to fix or remediate this issue without a new release. The best solution is to contain the information about this vulnerability to minimize the number of users who know about it and can thus exploit it. ### References _Are there any links users can visit to find out more?_ See the inte...