🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits several security concerns including potential shell injection vulnerabilities, obfuscation techniques that may hide malicious activities, and improper handling of credentials. While there's no direct evidence of a supply-chain attack, the combination of these factors raises significant suspicion.

shell injection vulnerabilities
use of base64 decoding to write files
improper handling of API keys

Per-check LLM notes

Network: No network call patterns detected, which is normal unless the package requires internet access.
Shell: Detected shell injection vulnerabilities which could allow execution of arbitrary commands, indicating potential security risks.
Obfuscation: The use of base64 decoding to write files suggests potential for hiding malicious code or content.
Credentials: The reliance on environment variables like GITHUB_TOKEN and the handling of API keys indicate possible unauthorized access or misuse of credentials.
Metadata: The package is very new with no community engagement, and the maintainer information is sparse.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

pen('{filename}', 'wb').write(base64.b64decode('{encoded}'))" res = self.container.exec_run(["pytho
open('{filename}','wb').write(base64.b64decode('{encoded}'))" result = container.exec_run(["python3", "
open('{filename}','wb').write(base64.b64decode('{encoded}'))" r = container.exec_run(["python3", "-c",

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

cess shell injection -> grep: shell=True" if build_system == "python" else "Commons-text Text4Shell
Replace subprocess calls with shell=True with list-form when input may be user-controlled - For *.ja

⚠ Credential Harvesting score 10.0

Found 4 credential access pattern(s)

" missing = [] if not os.getenv("GITHUB_TOKEN"): missing.append("GITHUB_TOKEN") if not os.get
test" self.gh_token = os.getenv("GITHUB_TOKEN") if not self.gh_token: print("WARNING:
ent() self.gh_token = os.getenv("GITHUB_TOKEN") self.workspace = "/home/agent/workspace"
API_KEY (Gemini)") if not os.getenv("GITHUB_TOKEN"): pytest.skip("Missing env var: GITHUB_TOKEN")

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ Maintainer History score 8.0

4 maintainer concern(s) found

Only one version has ever been released — brand new package
Package uploaded less than 24 hours ago (2026-06-05T10:00:23.000Z)
Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with sentinel-remediation

Develop a fully-functional mini-application named 'AutoSecure' that leverages the Python package 'sentinel-remediation' to autonomously manage and remediate Common Vulnerabilities and Exposures (CVEs) within a software repository. This application should serve as a GitHub Action, capable of running automatically upon certain events such as pull requests or merges into the main branch.

Step-by-step guide:
1. Initialize the project with a virtual environment and install the necessary dependencies, including 'sentinel-remediation'.
2. Configure the application to connect to a GitHub repository where it will monitor for new vulnerabilities and trigger automatic remediation processes.
3. Implement a feature that scans the repository for known CVEs using 'sentinel-remediation', identifying any affected packages or lines of code.
4. Develop a patching mechanism within the application that automatically updates the source code and build files to address identified vulnerabilities.
5. Integrate a verification process to ensure that the patched build works correctly without introducing new issues.
6. Create functionality to open a Pull Request (PR) on GitHub, detailing the changes made to remediate the CVEs, along with relevant CVE information and justification for the changes.
7. Ensure the application logs all actions taken, including details about the detected vulnerabilities, the applied patches, and the status of the verification process.
8. Test the application thoroughly, simulating various scenarios involving different types of vulnerabilities and ensuring that the remediation process functions as expected.
9. Document the setup process, configuration options, and usage instructions for the application, making it easy for others to integrate 'AutoSecure' into their own projects.

Suggested Features:
- Support for multiple programming languages and build systems commonly used in software development.
- Integration with other security tools or APIs to enhance vulnerability detection capabilities.
- Customizable settings allowing users to define specific conditions under which the application should trigger automatic remediation.
- A user-friendly interface or command-line tool for managing and monitoring the application's operations.

How 'sentinel-remediation' is Utilized:
- The core functionalities of 'sentinel-remediation' will be employed to scan for CVEs, apply patches to the source code and build files, and verify the integrity of the updated build. These actions form the backbone of the automated remediation process implemented by 'AutoSecure'.