🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits several concerning behaviors including potential network risks and shell execution, which could be exploited for malicious purposes. However, there is no clear evidence of malicious intent.

High network and shell execution risks
Potential data obfuscation techniques

Per-check LLM notes

Network: The network calls suggest the package may communicate with external services, which could be legitimate but also indicative of potential C2 or data exfiltration activities.
Shell: The shell execution patterns indicate that the package runs external commands and scripts, which can be risky if not properly sanitized or controlled, potentially leading to code injection or other malicious actions.
Obfuscation: The use of base64 decoding and AESGCM encryption suggests data obfuscation or secure handling, but the incomplete and potentially obfuscated code raises suspicion.
Credentials: No clear patterns indicating credential harvesting were found.
Metadata: The maintainer has a new or inactive PyPI account and the repository lacks community engagement.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

ompt}).encode() req = urllib.request.Request( f"{router_url}/query", data
POST", ) with urllib.request.urlopen(req, timeout=timeout) as resp: data = js
domain async with httpx.AsyncClient(timeout=10.0) as client: r = await client.ge
try: with httpx.Client(timeout=2.0) as client: r = client.get(url)
try: async with httpx.AsyncClient() as client: response = await client.post(
try: async with httpx.AsyncClient(timeout=self._timeout) as client: async with

⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

ort AESGCM raw = base64.b64decode(value[len(_ENCRYPTED_PREFIX) :]) nonce, cipherte
───────── @main.group() def eval(): """Run evaluation datasets against the live AUA route
ne]: try: compile(output, "<string>", "exec") return True, None except SyntaxError

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

try: _result = subprocess.run(["ollama", "list"], capture_output=True, text=True, timeout=
subprocess result = subprocess.run( ["aua", "eval", "run", "--dataset", ds, "--conf
= f.name result = subprocess.run(["python3", fname], capture_output=True, text=True, timeout=
llama"): result = subprocess.run(["ollama", "--version"], capture_output=True, text=True)
try: r = subprocess.run( [ "nvidia-smi",
try: r = subprocess.run( ["rocm-smi", "--showmeminfo", "vram", "--no

✓ 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 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 "Praneeth Tota" 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 adaptive-utility-agent

Create a fully-functional mini-app named 'AdaptiveChat' using the 'adaptive-utility-agent' package. This app will serve as a simple yet powerful chatbot system that can adapt its responses based on user interactions and context. The goal is to demonstrate the capabilities of 'adaptive-utility-agent' in building adaptable and intelligent systems without deep machine learning expertise.

Step 1: Set up your development environment with Python and install the 'adaptive-utility-agent'.

Step 2: Define the basic structure of your chatbot application. It should include a user interface where users can input messages and receive responses from the bot.

Step 3: Utilize 'adaptive-utility-agent' to create models that can handle different types of queries. For example, one model could be dedicated to answering factual questions, another to providing recommendations, and a third for handling general conversational topics.

Step 4: Implement a mechanism within your application to dynamically switch between these models based on the nature of the user's query. This could involve keyword detection or more sophisticated natural language processing techniques.

Step 5: Integrate a feedback loop where users can rate the quality of responses. Use this data to improve the performance of your models over time, showcasing the 'adaptive' aspect of the package.

Suggested Features:
- User authentication to track individual interaction histories.
- Integration with external APIs for enhanced functionality (e.g., weather updates).
- A dashboard for administrators to manage and monitor the chatbot's performance.

The 'adaptive-utility-agent' package should be utilized throughout the development process to streamline the creation of models, manage their interactions, and facilitate the adaptation of the chatbot's behavior based on user engagement.