detecting-attacks-on-historian-servers

Detect cyber attacks targeting OT historian servers (OSIsoft PI, Ignition, Wonderware) that sit at the IT/OT boundary and serve as pivot points for lateral movement between enterprise and control networks, including data manipulation, unauthorized queries, and exploitation of historian-specific vulnerabilities.

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Install Skill

Run in your terminal

$npx skills install mukul975/Anthropic-Cybersecurity-Skills/detecting-attacks-on-historian-servers

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Installation Guide

How to use detecting-attacks-on-historian-servers on Cursor

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1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your machine
  • Node.js 16+ with npm — verify with node --version
  • Active project directory where you want to add detecting-attacks-on-historian-servers
2

Run the install command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills install mukul975/Anthropic-Cybersecurity-Skills/detecting-attacks-on-historian-servers

Fetches detecting-attacks-on-historian-servers from mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/detecting-attacks-on-historian-servers

Restart Cursor to activate detecting-attacks-on-historian-servers. Access via /detecting-attacks-on-historian-servers in your agent's command palette.

Security Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.

Documentation

name
detecting-attacks-on-historian-servers
description
'Detect cyber attacks targeting OT historian servers (OSIsoft PI, Ignition, Wonderware) that sit at the IT/OT boundary and serve as pivot points for lateral movement between enterprise and control networks, including data manipulation, unauthorized queries, and exploitation of historian-specific vulnerabilities. '
domain
cybersecurity
subdomain
ot-ics-security
tags
- ot-security - ics - historian - osisoft-pi - ignition - pivot-point - data-integrity - lateral-movement
version
'1.0'
author
mahipal
license
Apache-2.0
nist_csf
- PR.IR-01 - DE.CM-01 - ID.AM-05 - GV.OC-02

Detecting 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

TermDefinition
OT HistorianDatabase server (OSIsoft PI, Ignition, Wonderware) storing time-series process data from SCADA/DCS systems
Pivot PointHistorian's position between IT and OT networks makes it a prime target for attackers to move between zones
Data Replay AttackFeeding historical data to an HMI to mask real-time process manipulation (Stuxnet technique)
OSIsoft PIMost widely deployed OT historian, used by 65% of Global 500 process companies
IgnitionInductive Automation SCADA platform with historian module, increasingly targeted due to Python scripting capabilities
CVE-2025-0921Ignition 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]

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Steps

  1. 1Install skill using provided installation command
  2. 2Test with simple use case relevant to your work
  3. 3Evaluate output quality and relevance
  4. 4Iterate on prompts to improve results
  5. 5Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use when

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid when

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Related Skills

Reviews

4.449 reviews
  • D
    Daniel TorresDec 28, 2024

    We added detecting-attacks-on-historian-servers from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • V
    Valentina ZhangDec 20, 2024

    detecting-attacks-on-historian-servers fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • H
    Harper KhanDec 4, 2024

    detecting-attacks-on-historian-servers is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • M
    Mei KhannaDec 4, 2024

    Solid pick for teams standardizing on skills: detecting-attacks-on-historian-servers is focused, and the summary matches what you get after install.

  • H
    Harper RahmanNov 23, 2024

    Solid pick for teams standardizing on skills: detecting-attacks-on-historian-servers is focused, and the summary matches what you get after install.

  • L
    Li KhannaNov 23, 2024

    detecting-attacks-on-historian-servers is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • A
    Ava GillNov 7, 2024

    detecting-attacks-on-historian-servers fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • A
    Anaya ParkOct 26, 2024

    We added detecting-attacks-on-historian-servers from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • H
    Harper NdlovuOct 18, 2024

    detecting-attacks-on-historian-servers fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • K
    Kwame LiOct 14, 2024

    detecting-attacks-on-historian-servers has been reliable in day-to-day use. Documentation quality is above average for community skills.

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