AI Analysis
Final verdict: SUSPICIOUS
The package exhibits moderate risks due to external network calls and obfuscation techniques, despite having no clear signs of shell execution or credential harvesting. The incomplete metadata adds a layer of uncertainty.
- Moderate network risk
- Some level of obfuscation
- Incomplete author metadata
Per-check LLM notes
- Network: The package makes external network calls which could potentially be used for unexpected data retrieval or updates, indicating a medium risk.
- Shell: No shell execution patterns detected, suggesting low risk for direct system command execution.
- Obfuscation: The base64 decoding suggests some level of obfuscation, but without context it's hard to determine if it's malicious or for legitimate purposes like data encoding.
- Credentials: No clear patterns indicative of credential harvesting were found.
- Metadata: The author's information is incomplete and they may be new or inactive, raising some suspicion but not definitive proof of malintent.
Package Quality Overall: Medium (5.2/10)
◈ Medium
Test Suite
6.0
Partial test coverage signals detected
1 test file(s) detected (e.g. test_arc_sentry.py)
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (11003 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
74 type-annotated function signatures detected in source
◈ Medium
Multiple Contributors
6.0
Limited contributor diversity
2 unique contributor(s) across 100 commits in 9hannahnine-jpg/arc-sentryTwo distinct contributors found
Heuristic Checks
Outbound Network Calls
score 3.0
Found 2 network call pattern(s)
ity state module...") urllib.request.urlretrieve( "https://raw.githubusercontent.com/try: r = requests.post(BASE_URL, headers={
Code Obfuscation
score 2.0
Found 1 obfuscation pattern(s)
ry: decoded = base64.b64decode(chunk).decode('utf-8', errors='ignore') if l
Shell / Subprocess Execution
No shell execution patterns detected
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: gmail.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository 9hannahnine-jpg/arc-sentry appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 arc-sentry
Create a Python-based mini-application that monitors and detects potential prompt injection attacks on a self-hosted large language model (LLM). This application will utilize the 'arc-sentry' package to ensure the security and integrity of your LLM deployment. Here’s a step-by-step guide on how to develop this application: 1. **Project Setup**: Begin by setting up a virtual environment for your project and install necessary dependencies including 'arc-sentry'. Additionally, include other required packages such as Flask for creating a web interface. 2. **LLM Integration**: Integrate your chosen LLM into the application. For demonstration purposes, you may use a pre-trained model like Mistral 7B, Qwen 2.5 7B, or Llama 3.1 8B. Ensure that the model is accessible within your application environment. 3. **Calibration Phase**: Implement a calibration phase where 'arc-sentry' learns the normal behavior of the LLM based on typical user interactions. This phase is crucial for accurate detection later on. 4. **Detection Mechanism**: Utilize 'arc-sentry' to monitor incoming prompts and detect any deviations that might indicate a prompt injection attack. The application should be able to flag suspicious activities without generating false positives. 5. **Alert System**: Develop an alert system that notifies administrators about detected threats through emails or SMS messages. The alert system should provide details about the suspicious activity and suggest steps to mitigate the threat. 6. **User Interface**: Create a simple web interface using Flask that allows users to interact with the LLM and view real-time monitoring data. The UI should display information about the current status of the LLM, recent interactions, and any alerts generated by 'arc-sentry'. 7. **Documentation**: Write comprehensive documentation explaining how to set up and run the application, including instructions on integrating different LLMs and customizing the alert system. Suggested Features: - Real-time interaction logging - Detailed analytics on detected threats - Customizable alert settings - Support for multiple LLMs - User-friendly web interface for monitoring By following these steps, you will create a robust mini-application that leverages 'arc-sentry' to protect your LLM against prompt injection attacks while providing valuable insights into its behavior.
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