aipea

v1.7.0 suspicious
6.0
Medium Risk

AI Prompt Engineer Agent — prompt preprocessing, security screening, and context enrichment for LLM systems

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to its network activity and shell execution capabilities, which could potentially be exploited for malicious purposes. However, there is no concrete evidence of malicious intent.

  • moderate network risk
  • high shell risk
Per-check LLM notes
  • Network: The use of HTTP POST requests suggests external API calls which could be legitimate, but may also indicate data exfiltration or C2 communication.
  • Shell: Subprocess execution can be used for various purposes, but the invocation of 'ollama list' and custom Python commands might indicate an attempt to execute unintended actions on the user's system.
  • Obfuscation: The use of zlib compression and base64 encoding suggests some form of data obfuscation, but without context, it's hard to determine if it's malicious.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The author details are incomplete and the maintainer seems to be new or inactive, which raises some concerns but not enough to conclude malice.

📦 Package Quality Overall: Medium (7.6/10)

✦ High Test Suite 9.0

Test suite present — 25 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 25 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 7.0

Some documentation present

  • 6 documentation file(s) (e.g. aipea-agent-framework.py)
  • Detailed PyPI description (14783 chars)
✦ High Contributing Guide 9.0

Has contribution guidelines and governance files

  • Governance file: security.py
  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • Type checker (mypy / pyright / pytype) referenced in project
  • 933 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in undercurrentai/AIPEA
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

  • try: resp = httpx.post( api_url, headers={"x-api-ke
  • try: resp = httpx.post( api_url, headers={
  • try: async with httpx.AsyncClient(timeout=_resolve_http_timeout()) as client:
  • try: async with httpx.AsyncClient(timeout=float(time_limit + 30)) as client: r
  • else: async with httpx.AsyncClient(timeout=self.timeout) as client: for _ in ra
⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • = { "snapshot_date": __import__("datetime").date.today().isoformat(), "bright_line": {
  • ent for use""" return zlib.decompress(self.compressed_content).decode('utf-8') def update
  • content = zlib.decompress(compressed_content).decode('utf-8')
  • ry: content = zlib.decompress(row["compressed_content"]).decode("utf-8") c
  • content = zlib.decompress(compressed_content).decode("utf-8") cre
  • content = zlib.decompress(compressed_content).decode("utf-8") # P
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • ol: try: result = subprocess.run( ["ollama", "list"], capture_output=
  • e stdout.""" result = subprocess.run( [sys.executable, "-c", "import aipea; print('OK
  • nv.update(env) return subprocess.run( [sys.executable, "-m", "aipea", *args],
  • """ try: result = subprocess.run(["ollama", "list"], capture_output=True, text=True, timeout=
  • point(self): result = subprocess.run( [sys.executable, "-m", "aipea", "info"],
  • """ try: result = subprocess.run( ["ollama", "list"], capture_output=
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: undercurrentholdings.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository undercurrentai/AIPEA appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • 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 aipea 
Create a mini-app called 'PromptPro' which aims to streamline the process of generating and refining prompts for large language models (LLMs). The app should leverage the 'aipea' package to preprocess prompts, conduct security screenings, and enrich them with contextual information. Here’s a detailed plan on how to develop 'PromptPro':

1. **Setup Environment**: Start by setting up your development environment. Ensure you have Python installed, then install the 'aipea' package using pip.

2. **User Interface Design**: Develop a simple but intuitive user interface where users can input their initial prompts. This could be a basic web interface or a command-line interface depending on your preference.

3. **Prompt Preprocessing**: Implement functionality to clean and format the user’s input using 'aipea'. This includes removing unnecessary characters, standardizing formatting, and ensuring the prompt is clear and concise.

4. **Security Screening**: Integrate 'aipea' to screen the processed prompts for potential security risks such as harmful content or inappropriate language. The app should flag any issues and allow users to edit their prompts before proceeding.

5. **Context Enrichment**: Use 'aipea' to enhance the prompt with relevant context based on the user’s input. This could include adding related topics, historical data, or other pertinent information to make the prompt more informative and effective.

6. **Output Display**: Once the prompt has been processed, enriched, and deemed safe, display it back to the user in a clear manner. Optionally, provide an explanation of the changes made during each phase of processing.

7. **Feedback Loop**: Incorporate a feature where users can provide feedback on the effectiveness of the processed prompts. This feedback will help improve the app over time.

8. **Documentation & Testing**: Write comprehensive documentation for both users and developers. Conduct thorough testing to ensure all features work as expected.

By following these steps, you’ll create a valuable tool that helps users optimize their interactions with LLMs by providing a streamlined, secure, and enriched prompting experience.

💬 Discussion Feed

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