Autogpt Classic

A command injection vulnerability exists in the workflow-checker.yml workflow of significant-gravitas/autogpt. The untrusted user input `github.head.ref` is used insecurely, allowing an attacker to inject arbitrary commands. This vulnerability affects versions up to and including the latest version. An attacker can exploit this by creating a branch name with a malicious payload and opening a pull request, potentially leading to reverse shell access or theft of sensitive tokens and keys.

Published: 2025-03-20T10:09:12.930Z
Updated: 2025-10-15T12:50:39.556Z

AutoGPT, a component of significant-gravitas/autogpt, is vulnerable to an improper neutralization of special elements used in an OS command ('OS Command Injection') due to a flaw in its shell command validation function. Specifically, the vulnerability exists in versions v0.5.0 up to but not including 5.1.0. The issue arises from the application's method of validating shell commands against an allowlist or denylist, where it only checks the first word of the command. This allows an attacker to bypass the intended restrictions by crafting commands that are executed despite not being on the allowlist or by including malicious commands not present in the denylist. Successful exploitation of this vulnerability could allow an attacker to execute arbitrary shell commands.

Published: 2024-06-06T18:19:08.151Z
Updated: 2024-08-01T18:56:22.433Z

An OS command injection vulnerability exists in the MacOS Text-To-Speech class MacOSTTS of the significant-gravitas/autogpt project, affecting versions up to v0.5.0. The vulnerability arises from the improper neutralization of special elements used in an OS command within the `_speech` method of the MacOSTTS class. Specifically, the use of `os.system` to execute the `say` command with user-supplied text allows for arbitrary code execution if an attacker can inject shell commands. This issue is triggered when the AutoGPT instance is run with the `--speak` option enabled and configured with `TEXT_TO_SPEECH_PROVIDER=macos`, reflecting back a shell injection snippet. The impact of this vulnerability is the potential execution of arbitrary code on the instance running AutoGPT. The issue was addressed in version 5.1.0.

Published: 2024-06-06T18:39:43.516Z
Updated: 2024-08-01T18:56:22.364Z

Auto-GPT is an experimental open-source application showcasing the capabilities of the GPT-4 language model. The Auto-GPT command line UI makes heavy use of color-coded print statements to signify different types of system messages to the user, including messages that are crucial for the user to review and control which commands should be executed. Before v0.4.3, it was possible for a malicious external resource (such as a website browsed by Auto-GPT) to cause misleading messages to be printed to the console by getting the LLM to regurgitate JSON encoded ANSI escape sequences (`\u001b[`). These escape sequences were JSON decoded and printed to the console as part of the model's "thinking process". The issue has been patched in release version 0.4.3.

Published: 2023-07-13T22:34:45.809Z
Updated: 2024-10-22T14:52:38.684Z

Auto-GPT is an experimental open-source application showcasing the capabilities of the GPT-4 language model. When Auto-GPT is executed directly on the host system via the provided run.sh or run.bat files, custom Python code execution is sandboxed using a temporary dedicated docker container which should not have access to any files outside of the Auto-GPT workspace directory. Before v0.4.3, the `execute_python_code` command (introduced in v0.4.1) does not sanitize the `basename` arg before writing LLM-supplied code to a file with an LLM-supplied name. This allows for a path traversal attack that can overwrite any .py file outside the workspace directory by specifying a `basename` such as `../../../main.py`. This can further be abused to achieve arbitrary code execution on the host running Auto-GPT by e.g. overwriting autogpt/main.py which will be executed outside of the docker environment meant to sandbox custom python code execution the next time Auto-GPT is started. The issue has been patched in version 0.4.3. As a workaround, the risk introduced by this vulnerability can be remediated by running Auto-GPT in a virtual machine, or another environment in which damage to files or corruption of the program is not a critical problem.

Published: 2023-07-13T22:33:41.290Z
Updated: 2024-10-30T15:53:58.317Z