🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits significant obfuscation and credential harvesting risks, alongside shell execution activities that require scrutiny. These factors, combined with lower levels of network and metadata risks, suggest potential malicious intent and pose a notable threat.

High obfuscation risk
Medium credential risk
Shell execution patterns warranting further investigation

Per-check LLM notes

Network: No network calls detected, which is normal and does not indicate risk.
Shell: Shell execution patterns are present and could be legitimate if the package requires command-line tools or integration with external systems, but warrant further investigation to ensure there's no unauthorized access or behavior.
Obfuscation: The presence of patterns resembling obfuscation techniques and potential code execution suggests a high risk of malicious intent.
Credentials: The detection of patterns related to checking system files for credentials indicates a medium risk of attempting to harvest sensitive information.
Metadata: The low activity in the git repository and the author's limited presence on PyPI suggest potential unreliability.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

tion evaluation without using eval(). Supported condition patterns: - "'substring' in error_
\(", re.IGNORECASE), # eval()/exec() re.compile(r"\brm\s+(-[rf]+\s+)?/", re.IGNORECA
h.read_text(encoding="utf-8") exec(compile(_source, str(_cli_py_path), "exec"), globals(), globals())
) command substitution re.compile(r"\b(eval|exec)\s*\(", re.IGNORECASE), # eval()/exec() re.compi

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

(", re.IGNORECASE), # os.system() re.compile(r"\bsubprocess\.(run|call|Popen)\s*\(", re.
n":"1.0"}}}\n' proc = subprocess.Popen( [sys.executable, "-m", "borg.integrations.mcp_s
= [] try: proc = subprocess.Popen( [command, *args], stdin=subprocess.
try: proc = subprocess.run( cmd, cwd=cwd,
st_ok=True) result = subprocess.run( ["gh", "repo", "clone", target_repo, str(tmp_di
ir / "repo" result = subprocess.run( ["git", "checkout", "-b", branch_name],

⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

ame network, " "check /etc/hosts inside container, restart containers to pick up DNS changes

✓ 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 2.0

1 maintainer concern(s) found

Author "Borg contributors" 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 agent-borg

Create a mini-application called 'CodeGuard' that acts as a personal coding assistant with robust error tracking and learning capabilities. CodeGuard will use the 'agent-borg' package to maintain a failure memory database, which stores past errors encountered during code execution along with their resolutions. This way, when similar issues arise in the future, CodeGuard can suggest solutions based on historical data. Here’s how you can develop it step-by-step:

1. **Setup Project**: Initialize a new Python project and install 'agent-borg'.
2. **CLI Integration**: Develop a command-line interface (CLI) where users can input their code snippets.
3. **Error Detection**: Implement basic error detection logic that checks for syntax errors and common runtime errors.
4. **Failure Memory**: Utilize 'agent-borg' to log these errors into a structured database. Each entry should include the error message, code snippet causing the error, timestamp, and any relevant context.
5. **Resolution Suggestion**: When an error is logged, CodeGuard should attempt to find similar entries in the failure memory database using 'agent-borg'. If a match is found, it should suggest the resolution from the matched entry.
6. **Learning Mechanism**: Allow users to manually add new error-resolution pairs to the database through the CLI, enhancing the system's learning over time.
7. **Reporting Tool**: Build a reporting tool within the CLI that allows users to view past errors, their resolutions, and statistics about the most frequent types of errors.
8. **MCP Server Setup**: Optionally, set up a simple Multi-Client Processing (MCP) server using 'agent-borg' to allow multiple instances of CodeGuard to share the same failure memory database, improving the learning speed and accuracy across different environments.

Suggested Features:
- Real-time error detection and suggestion integration with popular IDEs.
- Advanced pattern matching for more sophisticated error resolution suggestions.
- A web dashboard to visualize error trends and user interactions with CodeGuard.
- Support for multiple programming languages.

By following these steps, you'll create a valuable tool that not only helps developers quickly resolve common coding issues but also learns from each interaction to improve its performance over time.