agentic-threat-hunting-framework

v0.13.0 suspicious
4.0
Medium Risk

Agentic Threat Hunting Framework - Memory and AI for threat hunters

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential misuse with medium risk levels in shell execution and credential handling. However, these could also be justified within the context of its purpose as a threat hunting framework.

  • Shell risk due to potential unapproved commands
  • Credential risk due to AWS credential checks
Per-check LLM notes
  • Network: The network calls appear to be fetching STIX data, which is common for threat hunting frameworks.
  • Shell: Shell execution patterns indicate package installation and dependency management, which could be legitimate but warrant scrutiny for unapproved commands or actions.
  • Obfuscation: No signs of malicious obfuscation detected.
  • Credentials: The code checks for environment variables related to AWS credentials which could indicate an attempt to harvest secrets, but it may also be a legitimate need for accessing AWS services.
  • Metadata: The author's information is incomplete and the account seems new or inactive, which could indicate potential risks but does not confirm malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • json" ) try: urllib.request.urlretrieve(_STIX_URL, str(stix_path)) _sanitize_sti
  • encode("utf-8") req = urllib.request.Request(url, data=data, headers={"Content-Type": "applicatio
  • try: resp = urllib.request.urlopen(req, timeout=30) except urllib.error.URLErro
  • error try: req = urllib.request.Request("{}/api/version".format(base_url)) resp = ur
  • mat(base_url)) resp = urllib.request.urlopen(req, timeout=2) return bool(resp.status == 2
  • logic self.session = requests.Session() retry_strategy = Retry( total=3,
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • ...[/cyan]") try: subprocess.run( [python, "-m", "venv", ".venv"], ch
  • ...[/cyan]") try: subprocess.run( [str(pip_path), "install", "--upgrade", "pip"],
  • nd("[dev]") try: subprocess.run( install_cmd, check=True,
  • ...[/cyan]") try: subprocess.run( [str(pip_path), "install", "scikit-learn"],
  • ion try: result = subprocess.run( [str(python_path), "--version"], ch
  • tr] try: result = subprocess.run( [str(pip_path), "list", "--format", "freeze"],
⚠ Credential Harvesting score 10.0

Found 6 credential access pattern(s)

  • self.region = region or os.getenv("AWS_REGION", os.getenv("AWS_DEFAULT_REGION", "us-east-1"))
  • on or os.getenv("AWS_REGION", os.getenv("AWS_DEFAULT_REGION", "us-east-1")) self._client = None
  • credentials -> Bedrock if os.getenv("AWS_PROFILE") or os.getenv("AWS_ACCESS_KEY_ID"): detecte
  • f os.getenv("AWS_PROFILE") or os.getenv("AWS_ACCESS_KEY_ID"): detected_model = model or "us.anthr
  • >>> validate_hunt_id("../../etc/passwd") False >>> validate_hunt_id("H-0001/../sec
  • >>> validate_file_path(Path("/etc/passwd"), base) False """ try: # Resolve t
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: nebulock.io>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Nebulock-Inc/agentic-threat-hunting-framework 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 agentic-threat-hunting-framework 
Create a mini-application named 'ThreatHunterAssistant' that leverages the 'agentic-threat-hunting-framework' Python package to assist cybersecurity professionals in identifying potential threats within their network environments. The application should include the following key functionalities:

1. **User Interface**: Develop a simple yet intuitive graphical user interface (GUI) using Tkinter or a similar library. This GUI should allow users to input network data, such as IP addresses, domain names, and log files.
2. **Data Analysis**: Utilize the framework's memory and AI capabilities to analyze the provided data for signs of malicious activity. The application should be able to detect anomalies, suspicious patterns, and known threats based on predefined rules and signatures.
3. **Real-Time Monitoring**: Implement real-time monitoring functionality where the application continuously scans incoming network traffic for threats. Users should be able to specify which types of data streams they want to monitor (e.g., HTTP requests, DNS queries).
4. **Reporting**: The application should generate comprehensive reports detailing any identified threats, including timestamps, affected entities, and recommended actions. These reports should be easily exportable in PDF or CSV format.
5. **Customization**: Allow users to customize the threat detection criteria by uploading their own lists of suspicious activities, IP addresses, or domains.
6. **Integration with External Tools**: Provide options for integrating the application with external tools like SIEM systems for enhanced threat management.

To achieve these goals, you will need to familiarize yourself with the 'agentic-threat-hunting-framework' package's documentation and APIs. Focus on utilizing its memory and AI components effectively to enhance the threat hunting process. Additionally, ensure that the application is scalable and can handle large volumes of data efficiently.