CVE-2023-37274
Auto-GPT Path Traversal Vulnerability
Description
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.
INFO
Published Date :
July 13, 2023, 11:15 p.m.
Last Modified :
July 27, 2023, 2:54 p.m.
Source :
[email protected]
Remotely Exploitable :
No
Impact Score :
5.9
Exploitability Score :
1.8
References to Advisories, Solutions, and Tools
Here, you will find a curated list of external links that provide in-depth
information, practical solutions, and valuable tools related to
CVE-2023-37274
.
URL | Resource |
---|---|
https://github.com/Significant-Gravitas/Auto-GPT/pull/4756 | Patch Third Party Advisory |
https://github.com/Significant-Gravitas/Auto-GPT/security/advisories/GHSA-5h38-mgp9-rj5f | Patch Third Party Advisory |
We scan GitHub repositories to detect new proof-of-concept exploits. Following list is a collection of public exploits and proof-of-concepts, which have been published on GitHub (sorted by the most recently updated).
Results are limited to the first 15 repositories due to potential performance issues.
The following list is the news that have been mention
CVE-2023-37274
vulnerability anywhere in the article.
The following table lists the changes that have been made to the
CVE-2023-37274
vulnerability over time.
Vulnerability history details can be useful for understanding the evolution of a vulnerability, and for identifying the most recent changes that may impact the vulnerability's severity, exploitability, or other characteristics.
-
CVE Modified by [email protected]
May. 14, 2024
Action Type Old Value New Value -
Initial Analysis by [email protected]
Jul. 27, 2023
Action Type Old Value New Value Added CVSS V3.1 NIST AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H Changed Reference Type https://github.com/Significant-Gravitas/Auto-GPT/pull/4756 No Types Assigned https://github.com/Significant-Gravitas/Auto-GPT/pull/4756 Patch, Third Party Advisory Changed Reference Type https://github.com/Significant-Gravitas/Auto-GPT/security/advisories/GHSA-5h38-mgp9-rj5f No Types Assigned https://github.com/Significant-Gravitas/Auto-GPT/security/advisories/GHSA-5h38-mgp9-rj5f Patch, Third Party Advisory Added CPE Configuration OR *cpe:2.3:a:agpt:auto-gpt:*:*:*:*:*:*:*:* versions up to (excluding) 0.4.3
CWE - Common Weakness Enumeration
While CVE identifies
specific instances of vulnerabilities, CWE categorizes the common flaws or
weaknesses that can lead to vulnerabilities. CVE-2023-37274
is
associated with the following CWEs:
Common Attack Pattern Enumeration and Classification (CAPEC)
Common Attack Pattern Enumeration and Classification
(CAPEC)
stores attack patterns, which are descriptions of the common attributes and
approaches employed by adversaries to exploit the CVE-2023-37274
weaknesses.
Exploit Prediction
EPSS is a daily estimate of the probability of exploitation activity being observed over the next 30 days.
0.04 }} 0.00%
score
0.05726
percentile