Agent skill

Detecting Attacks On Historian Servers

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Detect cyber attacks on OT historian servers (OSIsoft PI, Ignition, GE Proficy, Wonderware InSQL) using a Python detector that flags unauthorized queries, data manipulation, and lateral-movement…

Apache-2.0Auto-check passedSecurity

Install Detecting Attacks On Historian Servers

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-attacks-on-historian-servers -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills detecting-attacks-on-historian-servers --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/detecting-attacks-on-historian-servers .claude/skills/detecting-attacks-on-historian-servers && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
detecting-attacks-on-historian-servers
GitHub stars
34k
Token cost
~3k tokens
SKILL.md length
245 words
Files
4 (incl. scripts, references)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Detect cyber attacks on OT historian servers (OSIsoft PI, Ignition, GE Proficy, Wonderware InSQL) using a Python detector that flags unauthorized queries, data manipulation, and lateral-movement…

  • Monitoring historians bridging IT/OT zones for compromise
  • SKILL.md covers When to Use, Prerequisites, Workflow and Key Concepts, plus 1 more section
  • Runs Python scripts from its folder
  • Investigating historian-specific CVE exploitation

What it does

Detecting Attacks On Historian Servers is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Detect cyber attacks on OT historian servers (OSIsoft PI, Ignition, GE Proficy, Wonderware InSQL) using a Python detector that flags unauthorized queries, data manipulation, and lateral-movement indicators as historians pivot between IT and OT networks. Use when monitoring historians bridging IT/OT zones for compromise, investigating historian-specific CVE exploitation, or validating historian data integrity after a suspected OT incident.

Its SKILL.md is about 3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/api-reference.md` and `scripts/agent.py`).

It sits in Security, covering Penetration testing, Red teaming and adversary simulation and DataFrames. It works with Python. The repository describes itself as: 817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io…. The licence is Apache-2.0.

When your agent uses it

  • Monitoring historians bridging IT/OT zones for compromise
  • Investigating historian-specific CVE exploitation
  • Validating historian data integrity after a suspected OT incident

Example prompts

  • “/detecting-attacks-on-historian-servers”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 54a7988. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Detecting Attacks On Historian Servers loads about 3k tokens when it runs, and up to ~3.5k if it reads all its reference files. Until then it costs about 120 tokens; SKILL.md has 245 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~120
When it runs · the whole SKILL.md, loaded when a task matches
~3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from mukul975/Anthropic-Cybersecurity-Skills at commit 54a7988, republished under its Apache-2.0 licence (© mukul975). 245 words, ~3,000 tokens.

Download SKILL.mdSave it as .claude/skills/detecting-attacks-on-historian-servers/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
detecting-attacks-on-historian-servers
description
Detect cyber attacks on OT historian servers (OSIsoft PI, Ignition, GE Proficy, Wonderware InSQL) using a Python detector that flags unauthorized queries, data manipulation, and lateral-movement indicators as historians pivot between IT and OT networks. Use when monitoring historians bridging IT/OT zones for compromise, investigating historian-specific CVE exploitation, or validating historian data integrity after a suspected OT incident.
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
mitre_attack
T0811, T0882, T0888, T0846, T0859

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
python
#!/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]

© mukul975, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts, references) in skills/detecting-attacks-on-historian-servers of mukul975/Anthropic-Cybersecurity-Skills.

  • SKILL.md
  • LICENSE
  • references/api-reference.md
  • scripts/agent.py

Open the folder on GitHubat commit 54a7988

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Works with

Categories

Questions about Detecting Attacks On Historian Servers

What does Detecting Attacks On Historian Servers do?

Detect cyber attacks on OT historian servers (OSIsoft PI, Ignition, GE Proficy, Wonderware InSQL) using a Python detector that flags unauthorized queries, data manipulation, and lateral-movement…. Detecting Attacks On Historian Servers is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Detect cyber attacks on OT historian servers (OSIsoft PI, Ignition, GE Proficy, Wonderware InSQL) using a Python detector that flags unauthorized queries, data manipulation, and lateral-movement indicators as historians pivot between IT and OT networks.

When should I use Detecting Attacks On Historian Servers?

Detecting Attacks On Historian Servers fits situations like: monitoring historians bridging IT/OT zones for compromise; investigating historian-specific CVE exploitation; validating historian data integrity after a suspected OT incident.

How do I install Detecting Attacks On Historian Servers in Claude Code?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-attacks-on-historian-servers -a claude-code`. Or copy the skill folder (skills/detecting-attacks-on-historian-servers in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/detecting-attacks-on-historian-servers in your project. Claude Code loads it when a task matches its description.

How do I install Detecting Attacks On Historian Servers in Codex?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-attacks-on-historian-servers -a codex`. Or copy the skill folder (skills/detecting-attacks-on-historian-servers in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/detecting-attacks-on-historian-servers in your project. Codex loads it when a task matches its description.

Can I use Detecting Attacks On Historian Servers in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-attacks-on-historian-servers -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/detecting-attacks-on-historian-servers, .gemini/skills/detecting-attacks-on-historian-servers, .github/skills/detecting-attacks-on-historian-servers and .opencode/skills/detecting-attacks-on-historian-servers in your project.

What does Detecting Attacks On Historian Servers need to run?

Going by SKILL.md and its folder, Detecting Attacks On Historian Servers needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Detecting Attacks On Historian Servers access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Detecting Attacks On Historian Servers safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Detecting Attacks On Historian Servers use?

Detecting Attacks On Historian Servers is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Detecting Attacks On Historian Servers use?

About 3k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 489 tokens, read only when the agent opens those files.

What are the alternatives to Detecting Attacks On Historian Servers?

Skills that share tags, products or a category with Detecting Attacks On Historian Servers: Cybersecurity (ohmyjahh/xquads-squads, 277 stars), Security Auditor (eigent-ai/eigent, 15k stars), Code Audit (3stoneBrother/code-audit, 892 stars) and CodeQL Security Scan (trailofbits/skills, 7.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Detecting Attacks On Historian Servers?

mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 34,116 GitHub stars. The repository holds 644 skills in this directory. The repository was last updated on August 31, 2026.

Source: mukul975/Anthropic-Cybersecurity-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.