Docker
CVE-2023-37274
HIGH
Severity by source
AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
Primary rating from NVD · only source for this CVE.
CVSS VectorNVD
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
Lifecycle Timeline
1DescriptionNVD
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.
AnalysisAI
Auto-GPT is an experimental open-source application showcasing the capabilities of the GPT-4 language model. Rated high severity (CVSS 7.8), this vulnerability is low attack complexity. This Code Injection vulnerability could allow attackers to inject and execute arbitrary code within the application.
Technical ContextAI
This vulnerability is classified as Code Injection (CWE-94), which allows attackers to inject and execute arbitrary code within the application. 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. Affected products include: Agpt Autogpt Classic. Version information: version 0.4.3..
RemediationAI
A vendor patch is available. Apply the latest security update as soon as possible. Never evaluate user-controlled input as code. Use sandboxing, disable dangerous functions, apply strict input validation.
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Same weakness CWE-94 – Code Injection
View allSame technique Path Traversal
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External POC / Exploit Code
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