CVE-2024-4343
Official description Straight from the sourceThe vendor's or NVD's own wording, published unedited. Authoritative, but often terse — it says what broke, rarely what to do.
NVD · uneditedA Python command injection vulnerability exists in the `SagemakerLLM` class's `complete()` method within `./private_gpt/components/llm/custom/sagemaker.py` of the imartinez/privategpt application, versions up to and including 0.3.0. The vulnerability arises due to the use of the `eval()` function to parse a string received from a remote AWS SageMaker LLM endpoint into a dictionary. This method of parsing is unsafe as it can execute arbitrary Python code contained within the response. An attacker can exploit this vulnerability by manipulating the response from the AWS SageMaker LLM endpoint to include malicious Python code, leading to potential execution of arbitrary commands on the system hosting the application. The issue is fixed in version 0.6.0.
Technical summary Written by usOur analysis, written from the advisory, the CVSS vector and the affected-version data. It adds context the advisory leaves out, and never invents facts that are not in the source.
dbcve analysisUser input reaches a shell or system command without being fully separated from the command itself, so an attacker can append their own instructions. Because these run with the application's privileges, it often means full control of the host. The lasting fix is to avoid the shell entirely — call APIs directly and pass arguments as data, never as concatenated strings.
General guidance for the os command injection class — the official description and references above are authoritative for this specific CVE. Want a bespoke review and a reviewed fix? Ask our team →
Affected products & versions What the vendor confirmedThe version ranges the vendor confirmed as vulnerable. If your version sits inside a range here, treat yourself as exposed until you have upgraded.
NVD · CPE data< 0.6.0CVSS breakdown How the score is builtThe industry scoring standard. It rates how the flaw is reached, what it takes to exploit, and what an attacker gains — the score is derived from those, not the other way round.
From the vector- Attack vector
- Network
- Complexity
- Low
- Privileges
- None
- User interaction
- None
- Scope
- Unchanged
- Confidentiality
- High
- Integrity
- High
- Availability
- High
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
Remediation Closing itWhat it takes to close this. Where a vendor fix exists we point at it; where none exists we say so plainly, and can build one. Effort estimates are scoped from the advisory, not from your codebase.
dbcve · scoped0.6.0
0.6.0 (or latest stable release)
- 1. Backup your current PrivateGPT installation and any custom configurations
- 2. Identify your current installation method (e.g., pip install, Docker, or from source)
- 3. If using pip: run `pip install privategpt==0.6.0` to upgrade to the fixed version
- 4. If using Docker: update your image tag to use version 0.6.0 or the latest stable release
- 5. If running from source: checkout the tagged release v0.6.0 or pull the latest code which contains the fix
- 6. Review the SagemakerLLM configuration in your settings to ensure proper integration with AWS SageMaker
- 7. Restart the PrivateGPT service to load the updated code
- 8. Test the SagemakerLLM integration to confirm the component functions correctly
Generated from the published advisory — verify against the referenced sources before acting.
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Free · runs locallyCheck whether your project pulls in CVE-2024-4343 — or any other known-vulnerable package — straight from your lock files. Free and open source; it runs locally and uploads nothing.
References Go to the primary sourcePrimary sources — vendor advisories, patches and trackers. Where our summary and a reference disagree, the reference wins.
Primary sourcesPractitioner notes
ContributedPeer-ranked notes from engineers who’ve handled CVE-2024-4343 in production — separate from our analysis above.
The advisory tells you what broke. It rarely tells you what actually worked. If you’ve dealt with this one, that detail is what the next engineer is searching for.
- The version that genuinely resolved it — not the one the vendor claimed
- A config change or rule that shut the vector down
- A gotcha in the upgrade path that cost you an afternoon
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- Version or environment caveats, and links to real fixes
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