
Detecting Attacks on Historian Servers
FreeMonitor and secure OT historian servers from cyber threats.
Free · Opens the source repo
What Detecting Attacks on Historian Servers does
This skill provides a Python-based detector specifically designed to identify cyber attacks on operational technology (OT) historian servers such as OSIsoft PI, Ignition, GE Proficy, and Wonderware InSQL. It focuses on monitoring for unauthorized queries, data manipulation, and lateral movement indicators that often occur when historians bridge IT and OT networks. By utilizing this skill, users can effectively flag potential security breaches and maintain the integrity of critical historian data.
The skill operates by establishing a baseline of authorized clients and query patterns, which allows it to detect anomalies in real-time. It checks active connections to the historian servers and alerts users to any unauthorized access attempts. Additionally, it validates data integrity by examining historical data for signs of manipulation, such as missing data points or suspiciously uniform values. This is particularly useful in scenarios where there is a need to investigate specific vulnerabilities or to respond to alerts regarding potential exploitation of historian-specific CVEs.
This tool is aimed at cybersecurity professionals and system administrators who manage historian servers in environments where IT and OT systems interact. It is particularly relevant for those tasked with monitoring and securing these systems against emerging threats. The skill requires a foundational understanding of historian architecture and API access to function effectively, making it suitable for users with intermediate to advanced knowledge in cybersecurity and OT systems.
Overall, this skill is essential for organizations that rely on historian servers for data collection and analysis, providing them with a robust mechanism to detect and respond to cyber threats in real-time, thus ensuring the integrity and availability of their operational data.
When to use it
Use this skill when monitoring historian servers for signs of compromise, unauthorized queries, or data integrity issues, especially in mixed IT/OT environments.
When not to use it
This skill is not suitable for general database security monitoring or for securing historian deployment and configuration in isolation from OT threats.
What you can build with it
Monitoring for Unauthorized Access
Use this skill to continuously monitor historian servers for unauthorized access attempts and alert security teams.
Validating Data Integrity Post-Incident
Employ the skill to validate the integrity of historian data following a suspected security incident or breach.
Investigating Lateral Movement
Utilize this tool to investigate and track lateral movement through historian servers between IT and OT networks.
How to install Detecting Attacks on Historian Servers
View source1. Install with the skills CLI
npx skills add mukul975/anthropic-cybersecurity-skills/detecting-attacks-on-historian-servers --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by mukul975Detecting Attacks on Historian Servers
When to Use
- When monitoring historian servers that bridge IT and OT networks for compromise indicators
- When detecting unauthorized queries or data manipulation in process historian databases
- When investigating lateral movement through historian servers between IT and OT zones
- When responding to alerts about exploitation of historian-specific vulnerabilities (CVE-2025-0921)
- When validating historian data integrity after a suspected OT security incident
Do not use for general database security monitoring (see database security skills), for historian deployment and configuration, or for IT-only data warehouse security.
Prerequisites
- Historian server inventory (OSIsoft PI, Ignition, GE Proficy, Wonderware InSQL)
- Network monitoring on historian network segments (both IT-facing and OT-facing interfaces)
- Historian API access for data integrity validation
- Baseline of normal historian query patterns (which applications query which tags)
- Understanding of historian architecture (data sources, interfaces, client connections)
Workflow
Step 1: Monitor Historian for Attack Indicators
#!/usr/bin/env python3
"""OT Historian Attack Detector.
Monitors historian servers for unauthorized access, data manipulation,
lateral movement indicators, and exploitation of historian-specific
vulnerabilities. Supports OSIsoft PI and Ignition platforms.
"""
import json
import sys
from collections import defaultdict
from datetime import datetime, timedelta
from typing import Dict, List, Optional
try:
import requests
except ImportError:
print("Install requests: pip install requests")
sys.exit(1)
class HistorianAttackDetector:
"""Detects attacks targeting OT historian servers."""
def __init__(self, historian_type: str, historian_url: str,
api_credentials: dict, verify_ssl: bool = False):
self.historian_type = historian_type
self.historian_url = historian_url.rstrip("/")
self.credentials = api_credentials
self.verify_ssl = verify_ssl
self.alerts = []
self.authorized_clients = set()
self.authorized_queries = {}
def set_baseline(self, authorized_clients: List[str],
authorized_query_patterns: Dict[str, List[str]]):
"""Set baseline of authorized historian clients and query patterns."""
self.authorized_clients = set(authorized_clients)
self.authorized_queries = authorized_query_patterns
def check_active_connections(self) -> List[dict]:
"""Check for unauthorized connections to historian."""
connections = []
if self.historian_type == "osisoft_pi":
try:
resp = requests.get(
f"{self.historian_url}/piwebapi/system/status",
auth=(self.credentials.get("username"), self.credentials.get("password")),
verify=self.verify_ssl,
timeout=10,
)
if resp.status_code == 200:
data = resp.json()
connections = data.get("ConnectedClients", [])
except requests.RequestException as e:
print(f"[!] PI Web API error: {e}")
elif self.historian_type == "ignition":
try:
resp = requests.get(
f"{self.historian_url}/data/status/connections",
headers={"Authorization": f"Bearer {self.credentials.get('token')}"},
verify=self.verify_ssl,
timeout=10,
)
if resp.status_code == 200:
connections = resp.json().get("connections", [])
except requests.RequestException as e:
print(f"[!] Ignition API error: {e}")
# Check for unauthorized clients
for conn in connections:
client_ip = conn.get("client_ip", conn.get("address", ""))
if self.authorized_clients and client_ip not in self.authorized_clients:
self.alerts.append({
"severity": "HIGH",
"type": "UNAUTHORIZED_HISTORIAN_CLIENT",
"timestamp": datetime.now().isoformat(),
"source_ip": client_ip,
"details": f"Unauthorized client {client_ip} connected to {self.historian_type} historian",
"mitre": "T0802 - Automated Collection",
})
return connections
def check_data_integrity(self, tags: List[str], hours_back: int = 24):
"""Check historian data for manipulation indicators."""
print(f"[*] Checking data integrity for {len(tags)} tags over last {hours_back}h")
integrity_issues = []
for tag in tags:
try:
if self.historian_type == "osisoft_pi":
resp = requests.get(
f"{self.historian_url}/piwebapi/streams/{tag}/recorded",
params={"startTime": f"*-{hours_back}h", "endTime": "*"},
auth=(self.credentials.get("username"), self.credentials.get("password")),
verify=self.verify_ssl,
timeout=15,
)
if resp.status_code == 200:
items = resp.json().get("Items", [])
# Check for suspicious patterns
if len(items) == 0:
integrity_issues.append({
"tag": tag, "issue": "NO_DATA",
"detail": "No data points in expected timeframe - possible deletion",
})
else:
values = [i.get("Value", 0) for i in items if isinstance(i.get("Value"), (int, float))]
if values and len(set(values)) == 1 and len(values) > 100:
integrity_issues.append({
"tag": tag, "issue": "FLATLINE",
"detail": f"Constant value {values[0]} for {len(values)} points - possible replay/spoofing",
})
except requests.RequestException:
pass
for issue in integrity_issues:
self.alerts.append({
"severity": "HIGH",
"type": f"DATA_INTEGRITY_{issue['issue']}",
"timestamp": datetime.now().isoformat(),
"tag": issue["tag"],
"details": issue["detail"],
"mitre": "T0809 - Data Destruction" if issue["issue"] == "NO_DATA" else "T0832 - Manipulation of View",
})
return integrity_issues
def check_lateral_movement_indicators(self):
"""Check for indicators of historian being used as pivot point."""
indicators = []
# Check 1: Historian making outbound connections to Level 1 devices
# (Historian should receive data, not initiate connections to PLCs)
indicators.append({
"check": "Outbound connections to PLC subnets",
"description": "Historian initiating connections to Level 1 devices may indicate compromise",
"detection": "Monitor firewall logs for historian IP connecting to PLC ports (502, 102, 44818)",
})
# Check 2: New processes or services on historian
indicators.append({
"check": "Unauthorized processes on historian server",
"description": "Attackers may install tools on historian for lateral movement",
"detection": "Monitor process creation events (Sysmon EventID 1) on historian",
})
# Check 3: Unusual authentication to historian
indicators.append({
"check": "Authentication from unexpected sources",
"description": "Compromised IT systems authenticating to historian for pivoting",
"detection": "Monitor Windows Security Event 4624 for logons from non-baseline sources",
})
return indicators
def generate_report(self):
"""Generate historian attack detection report."""
print(f"\n{'='*70}")
print("HISTORIAN ATTACK DETECTION REPORT")
print(f"{'='*70}")
print(f"Historian Type: {self.historian_type}")
print(f"Historian URL: {self.historian_url}")
print(f"Report Time: {datetime.now().isoformat()}")
print(f"Total Alerts: {len(self.alerts)}")
if self.alerts:
print(f"\n--- ALERTS ---")
for alert in self.alerts:
print(f"\n [{alert['severity']}] {alert['type']}")
print(f" Time: {alert['timestamp']}")
print(f" Detail: {alert['details']}")
print(f" MITRE ICS: {alert.get('mitre', 'N/A')}")
print(f"\n--- LATERAL MOVEMENT CHECKS ---")
for indicator in self.check_lateral_movement_indicators():
print(f"\n Check: {indicator['check']}")
print(f" Risk: {indicator['description']}")
print(f" Detection: {indicator['detection']}")
if __name__ == "__main__":
detector = HistorianAttackDetector(
historian_type="osisoft_pi",
historian_url="https://pi-server.plant.local",
api_credentials={"username": "pi_reader", "password": "api_key_here"},
)
detector.set_baseline(
authorized_clients=["10.10.2.10", "10.10.2.20", "10.10.3.50", "10.10.150.10"],
authorized_query_patterns={},
)
detector.check_active_connections()
detector.check_data_integrity(tags=["REACTOR_01.TEMP", "PUMP_03.FLOW"], hours_back=24)
detector.generate_report()
Key Concepts
| Term | Definition |
|---|---|
| OT Historian | Database server (OSIsoft PI, Ignition, Wonderware) storing time-series process data from SCADA/DCS systems |
| Pivot Point | Historian's position between IT and OT networks makes it a prime target for attackers to move between zones |
| Data Replay Attack | Feeding historical data to an HMI to mask real-time process manipulation (Stuxnet technique) |
| OSIsoft PI | Most widely deployed OT historian, used by 65% of Global 500 process companies |
| Ignition | Inductive Automation SCADA platform with historian module, increasingly targeted due to Python scripting capabilities |
| CVE-2025-0921 | Ignition SCADA privileged file system vulnerability allowing escalation through malicious project files |
Output Format
HISTORIAN ATTACK DETECTION REPORT
====================================
Historian: [type and hostname]
Date: YYYY-MM-DD
CONNECTION ANALYSIS:
Authorized Clients: [count]
Unauthorized Clients Detected: [count with IPs]
DATA INTEGRITY:
Tags Checked: [count]
Integrity Issues: [count]
Flatline Detections: [count]
Data Gaps: [count]
LATERAL MOVEMENT INDICATORS:
Outbound PLC Connections: [found/not found]
Unauthorized Processes: [found/not found]
Anomalous Authentication: [found/not found]
Frequently asked questions about Detecting Attacks on Historian Servers
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