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.
Metrics
CVSS Version: 3.1 |
Base Score: 7.6 HIGH Vector: CVSS:3.1/AV:L/AC:H/PR:L/UI:R/S:C/C:H/I:H/A:H
l➤ Exploitability Metrics: Attack Vector (AV)* LOCAL Attack Complexity (AC)* HIGH Privileges Required (PR)* LOW User Interaction (UI)* REQUIRED Scope (S)* CHANGED
l➤ Impact Metrics: Confidentiality Impact (C)* HIGH Integrity Impact (I)* HIGH Availability Impact (A)* HIGH
Weakness Enumeration (CWE)
CWE-ID: CWE-94 CWE Name: CWE-94: Improper Control of Generation of Code (
Code Injection
) Source: Significant-Gravitas
Common Attack Pattern Enumeration and Classification (CAPEC)