immunoorg/knowledge_base.py

"""
RAG Knowledge Base for CVEs
===========================
Simulates a Retrieval-Augmented Generation system that provides the agent
with real-world technical details about vulnerabilities.
"""

from __future__ import annotations
import random
from typing import Any

class CVEKnowledgeBase:
    """
    A mock RAG system that provides technical details for CVEs.
    In production, this would connect to the NIST NVD API or a Vector DB.
    """
    
    def __init__(self):
        # Simulated Vector DB of CVEs
        self.cve_library = {
            "sql_injection": {
                "cve_id": "CVE-2023-1234",
                "technical_detail": "Improper neutralization of special elements used in an SQL Command. Common in legacy PHP apps.",
                "best_mitigation": "Use parameterized queries (Prepared Statements) and input validation.",
                "risk_level": "Critical"
            },
            "xss": {
                "cve_id": "CVE-2023-5678",
                "technical_detail": "Failure to encode user-supplied data before rendering it in the browser.",
                "best_mitigation": "Implement Content Security Policy (CSP) and output encoding.",
                "risk_level": "Medium"
            },
            "credential_stuffing": {
                "cve_id": "CVE-2024-0001",
                "technical_detail": "Automated injection of stolen username/password pairs.",
                "best_mitigation": "Enforce Multi-Factor Authentication (MFA) and rate-limiting on login endpoints.",
                "risk_level": "High"
            },
            "apt_backdoor": {
                "cve_id": "CVE-2024-9999",
                "technical_detail": "Persistent stealthy access via modified system binaries (Rootkit).",
                "best_mitigation": "File Integrity Monitoring (FIM) and mandatory access control (SELinux).",
                "risk_level": "Critical"
            },
            "supply_chain": {
                "cve_id": "CVE-2023-4444",
                "technical_detail": "Malicious code injected into a trusted third-party dependency (Typosquatting).",
                "best_mitigation": "Implement SBOM (Software Bill of Materials) and dependency pinning.",
                "risk_level": "High"
            },
            "privilege_escalation": {
                "cve_id": "CVE-2024-1111",
                "technical_detail": "Exploitation of misconfigured setuid binaries or kernel vulnerabilities to gain root access.",
                "best_mitigation": "Apply latest kernel patches and implement Principle of Least Privilege (PoLP).",
                "risk_level": "High"
            },
            "lateral_movement": {
                "cve_id": "CVE-2023-2222",
                "technical_detail": "Use of Pass-the-Hash (PtH) or SMB relay to pivot between systems.",
                "best_mitigation": "Implement network segmentation and disable LLMNR/NBT-NS.",
                "risk_level": "High"
            },
            "ransomware": {
                "cve_id": "CVE-2024-3333",
                "technical_detail": "Encryption of critical data using asymmetric keys after disabling backup services.",
                "best_mitigation": "Maintain offline, immutable backups and use EDR for behavior-based detection.",
                "risk_level": "Critical"
            },
            "ddos": {
                "cve_id": "CVE-2023-4445",
                "technical_detail": "Amplification attack using UDP reflection (e.g., DNS or NTP).",
                "best_mitigation": "Deploy cloud-based DDoS mitigation (e.g., Cloudflare) and configure rate limits.",
                "risk_level": "Medium"
            },
            "zero_day": {
                "cve_id": "CVE-2024-XXXX",
                "technical_detail": "Previously unknown vulnerability in a proprietary protocol implementation.",
                "best_mitigation": "Implement anomaly-based detection and rapid patching cycle.",
                "risk_level": "Critical"
            },
        }

    def retrieve_cve_info(self, vector: str) -> str:
        """Simulate a RAG retrieval step with semantic fallback."""
        # Normalize vector name to match library keys
        key = vector.lower().replace(" ", "_")
        info = self.cve_library.get(key)
        
        if info:
            return (f"[RAG RETRIEVAL - {info['cve_id']}]: {info['technical_detail']} "
                    f"Recommended Mitigation: {info['best_mitigation']} (Risk: {info['risk_level']})")
        
        # Semantic Fallback: Try to find a related CVE if exact match fails
        for k, v in self.cve_library.items():
            if k in key or key in k:
                return (f"[RAG SEMANTIC MATCH - {v['cve_id']}]: Related to {vector}. {v['technical_detail']} "
                        f"Recommended Mitigation: {v['best_mitigation']} (Risk: {v['risk_level']})")
        
        return "No specific CVE records found for this attack vector in the Knowledge Base."