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"""
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."
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