Vulnerabilities

Dask distributed is a distributed task scheduler for Dask. Prior to 2026.1.0, when Jupyter Lab, jupyter-server-proxy, and Dask distributed are all run together, it is possible to craft a URL which will result in code being executed by Jupyter due to a cross-side-scripting (XSS) bug in the Dask dashboard. It is possible for attackers to craft a phishing URL that assumes Jupyter Lab and Dask may be running on localhost and using default ports. If a user clicks on the malicious link it will open an error page in the Dask Dashboard via the Jupyter Lab proxy which will cause code to be executed by the default Jupyter Python kernel. This vulnerability is fixed in 2026.1.0.

Miniconda3 macOS installers before 23.11.0-1 contain a local privilege escalation vulnerability when installed outside the user's home directory. During installation, world-writable files are created and executed with root privileges. This flaw allows a local low-privileged user to inject arbitrary commands, leading to code execution as the root user.

Anaconda3 macOS installers before 2024.06-1 contain a local privilege escalation vulnerability when installed outside the user's home directory. During installation, world-writable files are created and executed with root privileges. This allows a local low-privileged user to inject arbitrary commands, leading to code execution as the root user.

Conda-build contains commands and tools to build conda packages. Prior to version 25.4.0, the conda-build recipe processing logic has been found to be vulnerable to arbitrary code execution due to unsafe evaluation of recipe selectors. Currently, conda-build uses the eval function to process embedded selectors in meta.yaml files. This approach evaluates user-defined expressions without proper sanitization, which allows arbitrary code to be executed during the build process. As a result, the integrity of the build environment is compromised, and unauthorized commands or file operations may be performed. The vulnerability stems from the inherent risk of using eval() on untrusted input in a context intended to control dynamic build configurations. By directly interpreting selector expressions, conda-build creates a potential execution pathway for malicious code, violating security assumptions. This issue has been patched in version 25.4.0.

conda-forge openssl-feedstock before 066e83c (2024-05-20), on Microsoft Windows, configures OpenSSL to use an OPENSSLDIR file path that can be written to by non-privilged local users. By writing a specially crafted openssl.cnf file in OPENSSLDIR, a non-privileged local user can execute arbitrary code with the privileges of the user or process loading openssl-feedstock DLLs. Miniforge before 24.5.0 is also affected.

Anaconda 3 2023.03-1-Linux allows local users to disrupt TLS certificate validation by modifying the cacert.pem file used by the installed pip program. This occurs because many files are installed as world-writable on Linux, ignoring umask, even when these files are installed as root. Miniconda is also affected.

Certain Anaconda3 2021.05 are affected by OS command injection. When a user installs Anaconda, an attacker can create a new file and write something in usercustomize.py. When the user opens the terminal or activates Anaconda, the command will be executed.

Anaconda Anaconda3 (Anaconda Distribution) through 2021.11.0.0 and Miniconda3 through 4.11.0.0 can create a world-writable directory under %PROGRAMDATA% and place that directory into the system PATH environment variable. Thus, for example, local users can gain privileges by placing a Trojan horse file into that directory. (This problem can only happen in a non-default installation. The person who installs the product must specify that it is being installed for all users. Also, the person who installs the product must specify that the system PATH should be changed.

An issue was discovered in the Dask distributed package before 2021.10.0 for Python. Single machine Dask clusters started with dask.distributed.LocalCluster or dask.distributed.Client (which defaults to using LocalCluster) would mistakenly configure their respective Dask workers to listen on external interfaces (typically with a randomly selected high port) rather than only on localhost. A Dask cluster created using this method (when running on a machine that has an applicable port exposed) could be used by a sophisticated attacker to achieve remote code execution.