SigmaFlow

v0.0.74 suspicious
7.0
High Risk

SigmaFlow is a Python package designed to optimize the performance of task-flow related to LLMs/MLLMs or Multi-agent

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits significant risks associated with shell execution, obfuscation techniques, and credential handling, suggesting potential security vulnerabilities or malicious intent. However, without concrete evidence of malicious behavior, it cannot be conclusively labeled as malicious.

  • High shell execution risk
  • Obfuscation through eval()
  • Fetching of sensitive environment variables
Per-check LLM notes
  • Network: Network calls suggest external API interactions which could be legitimate, but require verification of the API endpoints and purpose.
  • Shell: Shell execution commands pose higher risk due to potential arbitrary code execution, indicating possible security vulnerabilities or malicious intent.
  • Obfuscation: The use of eval() with potentially dynamic strings suggests an attempt to obfuscate code execution, which can be risky.
  • Credentials: Fetching environment variables that appear to contain sensitive information such as API keys indicates a high risk of credential harvesting.
  • Metadata: The maintainer's author name is missing and they appear to be new or inactive, which raises some concern but does not strongly indicate malicious intent.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • _rerank": False } r = requests.post(RAG_ENDPOINT, json=d, timeout=300) j = r.json() retu
  • P_CONF self.session = requests.Session() self.session.headers.update(HEADERS) # sel
  • [str(text)])} response = requests.post(url, json=j, verify=False) if response.status_code != 2
  • scription_key} response = requests.get(endpoint, headers=headers, params=params) response.raise
  • 000.0, "http_client": httpx.AsyncClient(verify=conf.get("SSL", False)), } if client_param["b
  • : try: async with httpx.AsyncClient(verify=False) as client: async with client.strea
⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

  • turn_key: o = eval(f"resp{self.return_key}") self.log("return_k
  • pipe_conf["format"][k] = eval( v.replace("<class '", "").r
  • } out = eval(code.format(**inps_dict)) else: cond = i
  • } out = eval(code.format(**inps_dict)) self.set_out(out, data, q
⚠ Shell / Subprocess Execution score 10.0

Found 5 shell execution pattern(s)

  • blocked" try: r = subprocess.run(command, shell=True, cwd=WORKDIR,
  • oc -o -" try: r = subprocess.run(command, shell=True, cwd=WORKDIR,
  • try: r = subprocess.run( command, shell=True, cwd=WORKDIR,
  • r = subprocess.run(command, shell=True, cwd=WORKDIR, capture_output=Tru
  • run( command, shell=True, cwd=WORKDIR, capture_output=True, text=Tru
⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • os.getenv("MODEL") API_KEY = os.getenv("API_KEY") ANTHROPIC_BASE_URL = os.getenv("ANTHROPIC_BASE_URL") OPEN
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: pjlab.org.cn>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository maokangkun/SigmaFlow 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 SigmaFlow 
Create a mini-application named 'TaskFlowOptimizer' that leverages the capabilities of the 'SigmaFlow' package to enhance the efficiency of workflows involving large language models (LLMs) and multi-agent systems. The application should have a user-friendly interface and provide real-time feedback on the optimization process. Here are the steps and features your project should include:

1. **Project Setup**: Begin by setting up a virtual environment and installing necessary packages including SigmaFlow. Ensure all dependencies are listed in a requirements.txt file.
2. **User Interface**: Develop a simple command-line interface (CLI) where users can input their workflow tasks. This could include specifying tasks such as text generation, question answering, and agent coordination.
3. **Workflow Definition**: Allow users to define their workflows by specifying a sequence of tasks and their parameters. Users should be able to add, modify, or delete tasks from their workflow.
4. **Optimization Process**: Implement a feature within TaskFlowOptimizer that uses SigmaFlow to analyze the defined workflow and suggest optimizations. This could involve reducing redundancy, improving task sequencing, or enhancing resource allocation.
5. **Execution & Monitoring**: After optimization, the application should execute the workflow and monitor its performance in real-time. Users should receive updates on the status of each task, any errors encountered, and overall execution time.
6. **Report Generation**: Upon completion of the workflow, generate a comprehensive report detailing the optimized workflow, performance metrics, and any recommendations for further improvements.
7. **Integration with External Tools**: Optionally, integrate TaskFlowOptimizer with external tools or services like cloud platforms for better scalability and flexibility.

Throughout the development process, ensure that you utilize key functionalities provided by SigmaFlow such as task scheduling, performance tuning, and dynamic resource management. This project aims to showcase how SigmaFlow can significantly streamline and enhance the performance of complex workflows involving AI and multi-agent systems.