🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits high risks in shell execution and credential harvesting, alongside moderate network and obfuscation concerns. While its intended functionality is plausible, the combination of these factors suggests potential malicious activity.

High shell risk indicating possible unauthorized system changes.
High credential risk suggesting potential for sensitive data harvesting.

Per-check LLM notes

Network: Network calls to a gateway and telemetry URL might be legitimate for functionality like model updates or usage reporting.
Shell: Shell execution patterns indicate interaction with 'ollama' commands, which could be part of the package's intended functionality but may also suggest potential for unauthorized system changes.
Obfuscation: The code shows signs of obfuscation that may hinder analysis and understanding, which could be indicative of malicious intent.
Credentials: Detected patterns suggest potential attempts to harvest credentials or sensitive information, posing a significant risk.
Metadata: The maintainer has a new or inactive account and lacks a proper author name, which raises some suspicion but not enough to conclusively determine malice.

📦 Package Quality Overall: Medium (6.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

2 test file(s) detected (e.g. test_injection.py)

◈ Medium Documentation 7.0

Some documentation present

Documentation URL: "Documentation" -> https://github.com/airblackbox/airblackbox/tree/main/docs
Detailed PyPI description (2621 chars)

○ Low Contributing Guide 4.0

No contributing guide or governance files found

Development Status classifier >= Beta

◈ Medium Type Annotations 5.0

Partial type annotation coverage

258 type-annotated function signatures detected in source

✦ High Multiple Contributors 8.0

Active multi-contributor project

4 unique contributor(s) across 100 commits in airblackbox/airblackbox
Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

try: r = httpx.get(f"{gateway}/v1/audit", timeout=5.0) if r.status_
try: r = httpx.get(f"{gateway}/v1/models", timeout=5.0) if r.status
s_dir() self.client = httpx.Client(timeout=5.0) def get_status(self) -> GatewayStatus:
import httpx httpx.post( TELEMETRY_URL, json=event,

⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

g data 2. Splits into train/eval (90/10, stratified by article) 3. Writes and executes the U
tool_abuse", regex=re.compile( r"(?:call|execute|run|invoke)\s+(?:the\s+)?" r"(?:function|too
ver", "generated_at": __import__("datetime").datetime.utcnow().isoformat() + "Z", "models": sort
on_id, "created": __import__("datetime").datetime.utcnow().isoformat(), "note": "Anonymo

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

try: result = subprocess.run(["ollama", "--version"], capture_output=True, text=True, tim
") try: result = subprocess.run( ["ollama", "pull", "airblackbox/air-compliance"
reate local alias subprocess.run( ["ollama", "cp", "airblackbox/air-complianc
.") try: result = subprocess.run(["ollama", "list"], capture_output=True, text=True, timeout=
try: proc = subprocess.run( ["ollama", "list"], capture
e) try: result = subprocess.run( ["ollama", "run", model, prompt], c

⚠ Credential Harvesting score 5.0

Found 2 credential access pattern(s)

and": "ls -la", "file_path": "/etc/passwd"}) assert "ls -la" in text assert "/etc/passwd" in
"ls -la" in text assert "/etc/passwd" in text text = _extract_text_from_input({"content": "

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository airblackbox/airblackbox 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 air-blackbox

Create a mini-application called 'AI Compliance Monitor' that leverages the 'air-blackbox' package to ensure compliance and manage incidents for AI agents within an organization. This application should have the following core functionalities:

1. **Compliance Check**: The application should be able to scan and check if all AI agents deployed in the organization comply with the latest regulatory standards. It should provide a report on any non-compliant AI agents.
2. **Inventory Management**: Maintain a comprehensive inventory of all AI agents in the organization, including details like deployment location, type of AI model, and purpose of use.
3. **Incident Response**: Implement a system to quickly identify and respond to any incidents caused by AI agents. This includes logging the incident, notifying relevant stakeholders, and providing steps to mitigate the impact.
4. **Audit Trail**: Keep a detailed audit trail of all actions performed by the AI agents, which can be reviewed for compliance and security audits.

To achieve these functionalities, you will need to utilize the core features provided by the 'air-blackbox' package, such as compliance checks, inventory management, incident response, and audit capabilities. Your task is to design and implement the 'AI Compliance Monitor' application from scratch, ensuring it is user-friendly and efficient. Additionally, document your development process and the integration of 'air-blackbox' into your application.