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import json
import requests
from langchain_tavily import TavilySearch
from langchain.chat_models import init_chat_model
from langsmith import traceable
from src.agents.cti_agent.config import (
IOC_EXTRACTION_PROMPT,
THREAT_ACTOR_PROMPT,
MITRE_EXTRACTION_PROMPT,
)
class CTITools:
"""Collection of specialized tools for CTI analysis."""
def __init__(self, llm, search: TavilySearch):
"""
Initialize CTI tools.
Args:
llm: Language model for analysis
search: Search tool for finding CTI reports
"""
self.llm = llm
self.search = search
@traceable(name="cti_search_reports")
def search_cti_reports(self, query: str) -> str:
"""
Specialized search for CTI reports with enhanced queries.
Args:
query: Search query for CTI reports
Returns:
JSON string with search results
"""
try:
# Enhance query with CTI-specific terms if not already present
enhanced_query = query
if "report" not in query.lower() and "analysis" not in query.lower():
enhanced_query = f"{query} threat intelligence report"
results = self.search.invoke(enhanced_query)
# Format results for better parsing
formatted_results = {
"query": enhanced_query,
"found": len(results.get("results", [])),
"reports": [],
}
for idx, result in enumerate(results.get("results", [])[:5]):
formatted_results["reports"].append(
{
"index": idx + 1,
"title": result.get("title", "No title"),
"url": result.get("url", ""),
"snippet": result.get("content", "")[:500],
"score": result.get("score", 0),
}
)
return json.dumps(formatted_results, indent=2)
except Exception as e:
return json.dumps({"error": str(e), "query": query})
@traceable(name="cti_extract_url_from_search")
def extract_url_from_search(self, search_result: str, index: int = 0) -> str:
"""
Extract a specific URL from search results JSON.
Args:
search_result: JSON string from SearchCTIReports
index: Which report URL to extract (default: 0 for first)
Returns:
Extracted URL string
"""
try:
import json
data = json.loads(search_result)
if "reports" in data and len(data["reports"]) > index:
url = data["reports"][index]["url"]
return url
return "Error: No URL found at specified index in search results"
except Exception as e:
return f"Error extracting URL: {str(e)}"
@traceable(name="cti_fetch_report")
def fetch_report(self, url: str) -> str:
"""Fetch with universal content cleaning."""
try:
import requests
from bs4 import BeautifulSoup
import PyPDF2
import io
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
}
response = requests.get(url, headers=headers, timeout=30)
response.raise_for_status()
content_type = response.headers.get("content-type", "").lower()
# Handle PDF files
if "pdf" in content_type or url.lower().endswith(".pdf"):
try:
pdf_file = io.BytesIO(response.content)
pdf_reader = PyPDF2.PdfReader(pdf_file)
text_content = []
# Extract text from first 10 pages (to avoid excessive content)
max_pages = min(len(pdf_reader.pages), 10)
for page_num in range(max_pages):
page = pdf_reader.pages[page_num]
page_text = page.extract_text()
if page_text.strip():
text_content.append(page_text)
if text_content:
full_text = "\n\n".join(text_content)
# Clean and truncate the text
cleaned_text = self._clean_content(full_text)
return f"PDF Report Content from {url}:\n\n{cleaned_text[:3000]}..."
else:
return f"Could not extract readable text from PDF: {url}"
except Exception as pdf_error:
return f"Error processing PDF {url}: {str(pdf_error)}"
# Handle web pages
else:
soup = BeautifulSoup(response.content, "html.parser")
# Remove unwanted elements
for element in soup(
["script", "style", "nav", "footer", "header", "aside"]
):
element.decompose()
# Try to find main content areas
main_content = (
soup.find("main")
or soup.find("article")
or soup.find(
"div", class_=["content", "main-content", "post-content"]
)
or soup.find("body")
)
if main_content:
text = main_content.get_text(separator=" ", strip=True)
else:
text = soup.get_text(separator=" ", strip=True)
cleaned_text = self._clean_content(text)
return f"Report Content from {url}:\n\n{cleaned_text[:3000]}..."
except Exception as e:
return f"Error fetching report from {url}: {str(e)}"
def _clean_content(self, text: str) -> str:
"""Clean and normalize text content."""
import re
# Remove excessive whitespace
text = re.sub(r"\s+", " ", text)
# Remove common navigation/UI text
noise_patterns = [
r"cookie policy.*?accept",
r"privacy policy",
r"terms of service",
r"subscribe.*?newsletter",
r"follow us on",
r"share this.*?social",
r"back to top",
r"skip to.*?content",
]
for pattern in noise_patterns:
text = re.sub(pattern, "", text, flags=re.IGNORECASE)
# Clean up extra spaces again
text = re.sub(r"\s+", " ", text).strip()
return text
@traceable(name="cti_extract_iocs")
def extract_iocs(self, content: str) -> str:
"""
Extract Indicators of Compromise from report content using LLM.
Args:
content: Report content to analyze
Returns:
Structured IOCs in JSON format
"""
try:
prompt = IOC_EXTRACTION_PROMPT.format(content=content)
response = self.llm.invoke(prompt)
result_text = (
response.content if hasattr(response, "content") else str(response)
)
return result_text
except Exception as e:
return json.dumps({"error": str(e), "iocs": []})
@traceable(name="cti_identify_threat_actors")
def identify_threat_actors(self, content: str) -> str:
"""
Identify threat actors, APT groups, and campaigns.
Args:
content: Report content to analyze
Returns:
Threat actor identification and attribution
"""
try:
prompt = THREAT_ACTOR_PROMPT.format(content=content)
response = self.llm.invoke(prompt)
result_text = (
response.content if hasattr(response, "content") else str(response)
)
return result_text
except Exception as e:
return f"Error identifying threat actors: {str(e)}"
def extract_mitre_techniques(
self, content: str, framework: str = "Enterprise"
) -> str:
"""
Extract MITRE ATT&CK techniques from report content using LLM.
Args:
content: Report content to analyze
framework: MITRE framework (Enterprise, Mobile, ICS)
Returns:
Structured MITRE techniques in JSON format
"""
try:
prompt = MITRE_EXTRACTION_PROMPT.format(
content=content, framework=framework
)
response = self.llm.invoke(prompt)
result_text = (
response.content if hasattr(response, "content") else str(response)
)
return result_text
except Exception as e:
return json.dumps({"error": str(e), "techniques": []})
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