🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits significant risks related to shell execution and obfuscation techniques, which could be used to hide malicious activities. Additionally, there is a high risk of credential harvesting.

High shell risk due to system command execution
Significant obfuscation risk with use of eval and dynamic imports

Per-check LLM notes

Network: Network calls appear to be for standard HTTP requests and timeouts, which are common in legitimate packages.
Shell: Shell execution patterns detected include system commands that could potentially execute arbitrary code, indicating a higher risk for potential misuse or malicious activities.
Obfuscation: The use of eval and dynamic imports with exception handling suggests potential code obfuscation to hide malicious activities.
Credentials: Accessing environment variables for tokens and reading system files indicate possible attempts to harvest credentials or sensitive information.
Metadata: The maintainer has a new or inactive account and the git repository is not found, raising some suspicion but not conclusive evidence of malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

:60]}...") resp = requests.get(url, timeout=10) resp.raise_for_status()
络问题时卡住 self.session = aiohttp.ClientSession( timeout=aiohttp.ClientTimeout(total=10, connect
) self.session = aiohttp.ClientSession( timeout=timeout, connector=
y: async with aiohttp.ClientSession( timeout=aiohttp.ClientTimeout(total=5)

⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

last_result = eval(last_line, exec_globals, exec_locals) else:
last_result = eval(last_line, exec_globals, exec_locals) except Excepti
try: __import__(mod) except Exception as e: errors.a

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

tual environment...") os.system(f"{sys.executable} -m venv .venv") print("✅ Virtual
in/pip" install_result = os.system( f"{pip_cmd} install -r requirements.txt -i https://
try: result = subprocess.run( ["git", "status", "--porcelain"],
result = subprocess.run([sys.executable, str(rel_path)],
] + args result = subprocess.run( full_cmd, capture_output=True, text=True, t
icGet 版本 result = subprocess.run( ["wmic", "datafile", "where", f"name='{exec

⚠ Credential Harvesting score 5.0

Found 2 credential access pattern(s)

{} github_token = os.getenv("GITHUB_TOKEN") if github_token: headers["Aut
"/etc", # 系统配置（/etc/hosts, /etc/resolv.conf, /etc/os-release 等） "/pro

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "xiefujin" 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 aacode

Create a code completion tool named 'CodeMate' that leverages the 'aacode' package to assist developers in generating code snippets and completing their coding tasks more efficiently. CodeMate should integrate seamlessly into any text editor or IDE and provide context-aware suggestions based on the current file being edited.

Step 1: Set up the environment.
- Install Python and the necessary libraries including 'aacode'.
- Ensure your development environment supports integration with external tools.

Step 2: Define the core functionalities of CodeMate.
- Implement a function that reads the current file content and provides contextual suggestions based on the code written so far.
- Integrate the ReAct architecture from 'aacode' to enhance the accuracy of suggestions by understanding the context of the code.
- Develop a feature that allows users to request specific code snippets or modifications directly through prompts.

Step 3: Enhance user interaction.
- Design an intuitive interface or API that allows users to interact with CodeMate easily.
- Add options for users to customize settings such as language preferences and suggestion styles.
- Implement real-time feedback and error correction mechanisms to improve user experience.

Step 4: Test and refine.
- Conduct thorough testing across various programming languages and scenarios to ensure reliability.
- Gather feedback from users and make iterative improvements.
- Optimize performance and reduce latency.

Suggested Features:
- Support for multiple programming languages.
- Integration with popular text editors and IDEs like VSCode, PyCharm, and IntelliJ.
- A learning mode where CodeMate can adapt to the developer's coding style over time.
- A documentation generator that creates API documentation based on comments and structure in the code.

How 'aacode' is Utilized:
- Use 'aacode' to process and understand the context of the code snippet currently being edited.
- Leverage 'aacode's file-based context management to provide relevant suggestions without overwhelming the user.
- Employ 'aacode's ReAct architecture to generate accurate and contextually appropriate code completions.