🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits high levels of network and obfuscation risks, raising concerns about potential hidden malicious activities. Despite no clear evidence of shell execution or credential harvesting, the combination of these factors suggests a possible supply-chain attack.

High network risk
Significant obfuscation

Per-check LLM notes

Network: The presence of push and pull network calls suggests the package might interact with external services, which could be used for data exfiltration or command and control communications.
Shell: No shell execution patterns were detected.
Obfuscation: The code shows signs of deliberate obfuscation which may be used to hide malicious activities.
Credentials: No clear evidence of direct credential harvesting but the obfuscated nature raises suspicion.
Metadata: Low risk, but new maintainer and lack of classifiers suggest low effort or inactivity.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

pull" resp = requests.get(pull_url, headers=headers, params={"repo": repo}, stream=Tru
ean}" resp = requests.get(metadata_url, headers=headers, timeout=5) i
resp = requests.post(push_url, headers=headers, data=data, files=files, timeout=6
ll" resp = requests.get(pull_url, headers=headers, params=params, timeout=60)
tream": False} resp = requests.post(url, json=payload) resp.raise_for_status() r

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

ytes) -> Any: return pickle.loads(data) import os def validate_uaf_path(path: str) -> boo

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author "Vaibhav Haswani" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with agentcomet

Your task is to develop a modern chatbot framework using the 'agentcomet' package, which specializes in managing agents and providing state persistence. This framework will serve as a robust backend for chatbots, enabling them to maintain context across conversations, manage multiple users simultaneously, and store conversation history efficiently. Your goal is to create a fully-functional mini-application that showcases these capabilities.

The application should include the following features:
1. User Registration and Login: Users should be able to register and log into the system to start conversations.
2. Persistent Conversations: Each user's conversation should persist across sessions, allowing the chatbot to recall previous interactions.
3. Multi-Agent Support: The system should support multiple chatbot agents, each with its own personality and capabilities.
4. State Management: Implement a mechanism to manage the state of conversations, ensuring that the chatbot can handle complex dialogues without losing context.
5. History Retrieval: Allow users to view their past conversations with the chatbot.
6. Customizable Responses: Provide an interface where developers can customize the chatbot's responses based on predefined rules or conditions.
7. Real-time Updates: Ensure that any changes made to the chatbot's configuration or state are reflected in real-time across all active sessions.

Utilize the 'agentcomet' package to manage the lifecycle of chatbot agents, handle state persistence, and coordinate between different components of your application. Make sure to document how you integrate 'agentcomet' into your project and explain the benefits it brings to the table.