Agent skill

Detecting Stuxnet Style Attacks

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Detects sophisticated cyber-physical attacks that follow the Stuxnet pattern of modifying PLC logic while spoofing sensor readings to hide the manipulation, using PLC logic integrity monitoring…

Apache-2.0Auto-check passedSecurity

Install Detecting Stuxnet Style Attacks

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-stuxnet-style-attacks -a claude-code

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

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills detecting-stuxnet-style-attacks --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-stuxnet-style-attacks .claude/skills/detecting-stuxnet-style-attacks && 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-stuxnet-style-attacks
GitHub stars
34k
Token cost
~4.9k tokens
SKILL.md length
358 words
Files
4 (incl. scripts, references)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Detects sophisticated cyber-physical attacks that follow the Stuxnet pattern of modifying PLC logic while spoofing sensor readings to hide the manipulation, using PLC logic integrity monitoring…

  • Works in 3 steps: Understand the Stuxnet Attack Chain → Implement PLC Logic Integrity Monitoring → Deploy Physics-Based Process Anomaly…
  • Hunting for IT-to-OT lateral movement
  • SKILL.md covers When to Use, Prerequisites, Workflow and Key Concepts, plus 2 more sections
  • Runs Python scripts from its folder

What it does

Detecting Stuxnet Style Attacks is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Detects sophisticated cyber-physical attacks that follow the Stuxnet pattern of modifying PLC logic while spoofing sensor readings to hide the manipulation, using PLC logic integrity monitoring (Claroty xDome, Nozomi Guardian) and physics-based process anomaly detection. Use when hunting for IT-to-OT lateral movement or discrepancies between PLC program state and physical process behavior in ICS/SCADA environments.

Its SKILL.md is about 4.9k 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 Red teaming and adversary simulation and Anomaly detection. 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

  • Hunting for IT-to-OT lateral movement
  • Discrepancies between PLC program state and physical process behavior in ICS/SCADA environments

Example prompts

  • “Use the detecting-stuxnet-style-attacks skill to detect sophisticated cyber-physical attacks that follow the Stuxnet pattern of modifying PLC logic…”
  • “/detecting-stuxnet-style-attacks”

Requirements

  • Python 3

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Understand the Stuxnet Attack Chain
  2. Implement PLC Logic Integrity Monitoring
  3. Deploy Physics-Based Process Anomaly Detection

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 Stuxnet Style Attacks loads about 4.9k tokens when it runs, and up to ~5.4k if it reads all its reference files. Until then it costs about 113 tokens; SKILL.md has 358 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~113
When it runs · the whole SKILL.md, loaded when a task matches
~4.9k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.4k

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). 358 words, ~4,889 tokens.

Download SKILL.mdSave it as .claude/skills/detecting-stuxnet-style-attacks/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
detecting-stuxnet-style-attacks
description
Detects sophisticated cyber-physical attacks that follow the Stuxnet pattern of modifying PLC logic while spoofing sensor readings to hide the manipulation, using PLC logic integrity monitoring (Claroty xDome, Nozomi Guardian) and physics-based process anomaly detection. Use when hunting for IT-to-OT lateral movement or discrepancies between PLC program state and physical process behavior in ICS/SCADA environments.
domain
cybersecurity
subdomain
ot-ics-security
tags
ot-security, ics, scada, industrial-control, iec62443, stuxnet, plc-integrity, apt
version
1.0.0
author
mahipal
license
Apache-2.0
nist_csf
PR.IR-01, DE.CM-01, ID.AM-05, GV.OC-02
mitre_attack
T0853, T0831, T0809, T0807

Detecting Stuxnet-Style Attacks

When to Use

  • When implementing advanced threat detection for high-value OT targets (nuclear, chemical, critical infrastructure)
  • When building detection for APT-style attacks targeting PLC logic and process manipulation
  • When establishing PLC logic integrity monitoring to detect unauthorized modifications
  • When investigating suspected process anomalies that may indicate cyber-physical attacks
  • When designing defense-in-depth strategies against nation-state level OT threats

Do not use for basic OT intrusion detection (see detecting-attacks-on-scada-systems), for malware analysis of Stuxnet samples (see malware reverse engineering skills), or for PLC programming and logic development.

Prerequisites

  • Detailed understanding of the Stuxnet attack chain and MITRE ATT&CK for ICS framework
  • PLC logic backup repository with known-good baseline copies of all PLC programs
  • Engineering workstation monitoring (EDR with OT awareness)
  • Physics-based process models for the controlled physical process
  • Network monitoring for industrial protocol traffic analysis

Workflow

Step 1: Understand the Stuxnet Attack Chain

Map detection opportunities across the multi-stage Stuxnet-style attack chain.

yaml
# Stuxnet-Style Attack Chain and Detection Points
attack_chain:
  stage_1_initial_access:
    technique: "USB-borne malware targeting air-gapped network"
    mitre_ics: "T0847 - Replication Through Removable Media"
    detection:
      - "USB device connection logging on engineering workstations"
      - "Removable media scanning with OT-approved AV"
      - "Application allowlisting blocking unauthorized executables"
      - "Windows autorun disabled via Group Policy"
    indicators:
      - "New USB device connections to engineering workstations"
      - "Execution of unsigned binaries from removable media"
      - "LNK file exploitation patterns"

  stage_2_lateral_movement:
    technique: "Exploitation of Windows vulnerabilities for network propagation"
    mitre_ics: "T0866 - Exploitation of Remote Services"
    detection:
      - "Network IDS detecting exploit traffic (MS08-067, MS10-061)"
      - "Unusual SMB traffic between engineering workstations"
      - "Windows event logs showing privilege escalation"
      - "New scheduled tasks or services created"
    indicators:
      - "Lateral movement between Level 3-4 Windows systems"
      - "WMI/PsExec execution from unexpected sources"
      - "Pass-the-hash authentication patterns"

  stage_3_ews_compromise:
    technique: "Compromise of engineering workstation with PLC programming software"
    mitre_ics: "T0862 - Supply Chain Compromise (Step-7 hooking)"
    detection:
      - "File integrity monitoring on Step-7/TIA Portal directories"
      - "DLL injection detection in PLC programming software"
      - "Monitoring s7otbxdx.dll for Stuxnet-specific hook"
      - "Unexpected modifications to PLC project files"
    indicators:
      - "Modified DLLs in Siemens STEP 7 installation directory"
      - "Rootkit hiding files on engineering workstation"
      - "PLC programming software behaving abnormally"

  stage_4_plc_logic_modification:
    technique: "Injecting malicious OB/FC blocks into PLC program"
    mitre_ics: "T0839 - Module Firmware / T0833 - Modify Control Logic"
    detection:
      - "PLC logic integrity comparison against known-good baseline"
      - "S7comm upload/download traffic from unauthorized sources"
      - "New OB/FC/FB blocks appearing in PLC program"
      - "Modification of OB1 (main scan) or OB35 (cyclic interrupt)"
    indicators:
      - "PLC program block count changes"
      - "PLC program size changes"
      - "Upload of unknown program blocks"

  stage_5_process_manipulation:
    technique: "Manipulating physical process while spoofing sensor readings"
    mitre_ics: "T0836 - Modify Parameter / T0856 - Spoof Reporting Message"
    detection:
      - "Physics-based anomaly detection (process model deviation)"
      - "Cross-validation of independent sensors"
      - "Vibration analysis and mechanical signature monitoring"
      - "Comparison of PLC-reported values vs independent measurements"
    indicators:
      - "Motor/pump operating outside normal parameters"
      - "Sensor readings diverging from physics model predictions"
      - "Process efficiency metrics deviating unexpectedly"
Step 2: Implement PLC Logic Integrity Monitoring

Continuously monitor PLC program integrity by comparing running logic against known-good baselines.

python
#!/usr/bin/env python3
"""PLC Logic Integrity Monitor.

Periodically retrieves PLC program block information and compares
against known-good baselines to detect unauthorized modifications
(Stuxnet-style logic injection).
"""

import hashlib
import json
import sys
import time
from dataclasses import dataclass, field, asdict
from datetime import datetime


@dataclass
class PLCBlock:
    """Represents a PLC program block."""
    block_type: str  # OB, FC, FB, DB
    block_number: int
    name: str
    size_bytes: int
    checksum: str
    last_modified: str
    author: str = ""


@dataclass
class IntegrityAlert:
    alert_id: str
    timestamp: str
    severity: str
    plc_name: str
    plc_ip: str
    alert_type: str
    description: str
    baseline_value: str
    current_value: str
    mitre_technique: str


class PLCIntegrityMonitor:
    """Monitors PLC program integrity against baselines."""

    def __init__(self):
        self.baselines = {}  # plc_name -> list of PLCBlock
        self.alerts = []
        self.alert_counter = 1

    def load_baseline(self, plc_name, baseline_file):
        """Load known-good PLC program baseline."""
        with open(baseline_file) as f:
            data = json.load(f)
        blocks = [PLCBlock(**b) for b in data.get("blocks", [])]
        self.baselines[plc_name] = {
            "blocks": {f"{b.block_type}{b.block_number}": b for b in blocks},
            "total_blocks": len(blocks),
            "loaded_at": datetime.now().isoformat(),
        }
        print(f"[*] Loaded baseline for {plc_name}: {len(blocks)} blocks")

    def check_integrity(self, plc_name, plc_ip, current_blocks):
        """Compare current PLC program against baseline."""
        baseline = self.baselines.get(plc_name)
        if not baseline:
            print(f"[WARN] No baseline for {plc_name}")
            return

        baseline_blocks = baseline["blocks"]
        current_block_map = {f"{b.block_type}{b.block_number}": b for b in current_blocks}

        # Check 1: New blocks added (potential logic injection)
        for key, block in current_block_map.items():
            if key not in baseline_blocks:
                self.alerts.append(IntegrityAlert(
                    alert_id=f"INT-{self.alert_counter:04d}",
                    timestamp=datetime.now().isoformat(),
                    severity="critical",
                    plc_name=plc_name,
                    plc_ip=plc_ip,
                    alert_type="NEW_BLOCK_DETECTED",
                    description=(
                        f"New program block {key} ({block.name}) found in PLC "
                        f"that does not exist in baseline. Size: {block.size_bytes} bytes."
                    ),
                    baseline_value="Block does not exist in baseline",
                    current_value=f"{key}: {block.size_bytes} bytes, checksum {block.checksum}",
                    mitre_technique="T0839 - Module Firmware / T0833 - Modify Control Logic",
                ))
                self.alert_counter += 1

        # Check 2: Blocks removed
        for key in baseline_blocks:
            if key not in current_block_map:
                self.alerts.append(IntegrityAlert(
                    alert_id=f"INT-{self.alert_counter:04d}",
                    timestamp=datetime.now().isoformat(),
                    severity="high",
                    plc_name=plc_name,
                    plc_ip=plc_ip,
                    alert_type="BLOCK_REMOVED",
                    description=f"Program block {key} removed from PLC",
                    baseline_value=f"{key}: {baseline_blocks[key].size_bytes} bytes",
                    current_value="Block not found",
                    mitre_technique="T0833 - Modify Control Logic",
                ))
                self.alert_counter += 1

        # Check 3: Block content modified (checksum mismatch)
        for key in baseline_blocks:
            if key in current_block_map:
                baseline_block = baseline_blocks[key]
                current_block = current_block_map[key]

                if baseline_block.checksum != current_block.checksum:
                    self.alerts.append(IntegrityAlert(
                        alert_id=f"INT-{self.alert_counter:04d}",
                        timestamp=datetime.now().isoformat(),
                        severity="critical",
                        plc_name=plc_name,
                        plc_ip=plc_ip,
                        alert_type="BLOCK_MODIFIED",
                        description=(
                            f"Program block {key} checksum mismatch. "
                            f"Logic has been modified since baseline was established."
                        ),
                        baseline_value=f"Checksum: {baseline_block.checksum}, Size: {baseline_block.size_bytes}",
                        current_value=f"Checksum: {current_block.checksum}, Size: {current_block.size_bytes}",
                        mitre_technique="T0833 - Modify Control Logic",
                    ))
                    self.alert_counter += 1

        # Check 4: Block count change
        if len(current_blocks) != baseline["total_blocks"]:
            self.alerts.append(IntegrityAlert(
                alert_id=f"INT-{self.alert_counter:04d}",
                timestamp=datetime.now().isoformat(),
                severity="high",
                plc_name=plc_name,
                plc_ip=plc_ip,
                alert_type="BLOCK_COUNT_CHANGE",
                description=f"Total block count changed from {baseline['total_blocks']} to {len(current_blocks)}",
                baseline_value=str(baseline["total_blocks"]),
                current_value=str(len(current_blocks)),
                mitre_technique="T0833 - Modify Control Logic",
            ))
            self.alert_counter += 1

    def generate_report(self):
        """Generate integrity monitoring report."""
        print(f"\n{'='*70}")
        print("PLC LOGIC INTEGRITY MONITORING REPORT")
        print(f"{'='*70}")
        print(f"Baselines loaded: {len(self.baselines)}")
        print(f"Alerts: {len(self.alerts)}")

        for a in self.alerts:
            print(f"\n  [{a.severity.upper()}] {a.alert_type}")
            print(f"    PLC: {a.plc_name} ({a.plc_ip})")
            print(f"    {a.description}")
            print(f"    Baseline: {a.baseline_value}")
            print(f"    Current: {a.current_value}")
            print(f"    MITRE: {a.mitre_technique}")


if __name__ == "__main__":
    monitor = PLCIntegrityMonitor()
    print("PLC Logic Integrity Monitor")
    print("Load baselines and call check_integrity() periodically")
Step 3: Deploy Physics-Based Process Anomaly Detection

Monitor physical process behavior using models that predict expected sensor values based on the laws of physics. Deviations indicate either equipment failure or cyber-physical attack.

python
#!/usr/bin/env python3
"""Physics-Based Cyber-Physical Attack Detector.

Uses simplified physics models to detect process manipulation
attacks where the attacker modifies the physical process while
spoofing sensor readings (the core Stuxnet attack pattern).
"""

import math
from dataclasses import dataclass
from datetime import datetime


@dataclass
class PhysicsAlert:
    timestamp: str
    severity: str
    alert_type: str
    sensor_tag: str
    reported_value: float
    predicted_value: float
    deviation_percent: float
    description: str


class CentrifugePhysicsModel:
    """Physics model for a centrifuge system (Stuxnet target analog).

    Detects manipulation by cross-correlating:
    - Motor frequency (Hz) vs reported RPM
    - RPM vs vibration signature
    - Power consumption vs rotational speed
    """

    def __init__(self, rated_rpm=1200, rated_frequency=50, rated_power_kw=75):
        self.rated_rpm = rated_rpm
        self.rated_frequency = rated_frequency
        self.rated_power_kw = rated_power_kw
        self.alerts = []

    def check_frequency_rpm_correlation(self, frequency_hz, reported_rpm):
        """Verify motor frequency matches reported RPM.

        For an induction motor: RPM = 120 * frequency / poles
        If RPM is being spoofed, it won't match the actual frequency.
        """
        # Assuming 4-pole motor with typical 3% slip
        expected_rpm = (120 * frequency_hz / 4) * 0.97
        deviation = abs(reported_rpm - expected_rpm) / expected_rpm * 100

        if deviation > 5.0:
            self.alerts.append(PhysicsAlert(
                timestamp=datetime.now().isoformat(),
                severity="critical",
                alert_type="FREQUENCY_RPM_MISMATCH",
                sensor_tag="MOTOR.RPM vs VFD.FREQ",
                reported_value=reported_rpm,
                predicted_value=round(expected_rpm, 1),
                deviation_percent=round(deviation, 1),
                description=(
                    f"Motor RPM ({reported_rpm}) does not match VFD frequency "
                    f"({frequency_hz} Hz). Expected ~{expected_rpm:.0f} RPM. "
                    f"Possible RPM sensor spoofing while frequency is manipulated."
                ),
            ))

    def check_power_speed_correlation(self, rpm, power_kw):
        """Verify power consumption matches rotational speed.

        Power scales approximately with RPM^3 for centrifugal loads.
        """
        speed_ratio = rpm / self.rated_rpm
        expected_power = self.rated_power_kw * (speed_ratio ** 3)
        deviation = abs(power_kw - expected_power) / max(expected_power, 0.1) * 100

        if deviation > 15.0:
            self.alerts.append(PhysicsAlert(
                timestamp=datetime.now().isoformat(),
                severity="high",
                alert_type="POWER_SPEED_MISMATCH",
                sensor_tag="MOTOR.POWER vs MOTOR.RPM",
                reported_value=power_kw,
                predicted_value=round(expected_power, 1),
                deviation_percent=round(deviation, 1),
                description=(
                    f"Power consumption ({power_kw} kW) inconsistent with RPM ({rpm}). "
                    f"Expected ~{expected_power:.1f} kW. May indicate hidden speed changes."
                ),
            ))

    def check_vibration_anomaly(self, rpm, vibration_mm_s):
        """Check if vibration signature is consistent with operating speed.

        Abnormal vibration at reported 'normal' speed may indicate actual
        speed is different from what sensors report.
        """
        # Normal vibration increases linearly with speed for balanced rotor
        speed_ratio = rpm / self.rated_rpm
        expected_vibration = 2.0 * speed_ratio  # mm/s baseline
        deviation = abs(vibration_mm_s - expected_vibration) / max(expected_vibration, 0.1) * 100

        if vibration_mm_s > 7.0:  # ISO 10816 alert threshold
            self.alerts.append(PhysicsAlert(
                timestamp=datetime.now().isoformat(),
                severity="critical",
                alert_type="ABNORMAL_VIBRATION",
                sensor_tag="MOTOR.VIBRATION",
                reported_value=vibration_mm_s,
                predicted_value=round(expected_vibration, 1),
                deviation_percent=round(deviation, 1),
                description=(
                    f"Vibration ({vibration_mm_s} mm/s) at ISO alert level while "
                    f"RPM reports normal ({rpm}). Actual speed may differ from reported."
                ),
            ))

    def report(self):
        if self.alerts:
            print(f"\n{'='*60}")
            print("PHYSICS-BASED ANOMALY DETECTION ALERTS")
            print(f"{'='*60}")
            for a in self.alerts:
                print(f"\n  [{a.severity.upper()}] {a.alert_type}")
                print(f"    {a.description}")
                print(f"    Reported: {a.reported_value} | Predicted: {a.predicted_value}")
                print(f"    Deviation: {a.deviation_percent}%")


if __name__ == "__main__":
    model = CentrifugePhysicsModel(rated_rpm=1200, rated_frequency=50, rated_power_kw=75)

    # Normal operation - no alerts expected
    model.check_frequency_rpm_correlation(50.0, 1164)
    model.check_power_speed_correlation(1164, 72.0)

    # Stuxnet-style attack: frequency increased but RPM spoofed as normal
    model.check_frequency_rpm_correlation(84.0, 1164)  # freq up, RPM spoofed
    model.check_power_speed_correlation(1164, 180.0)    # power reveals true speed

    model.report()
Show full SKILL.md (154 more words)Show less

Key Concepts

TermDefinition
Cyber-Physical AttackAttack that manipulates both the cyber system (PLC logic, sensor readings) and the physical process simultaneously
Logic InjectionInserting malicious code blocks into PLC programs to alter physical process behavior
Sensor SpoofingReplaying or fabricating sensor readings to hide process manipulation from operators
Physics-Based DetectionUsing mathematical models of physical processes to detect when reported sensor values are inconsistent with actual physics
PLC Logic BaselineKnown-good copy of PLC program blocks (OB, FC, FB, DB) used for integrity comparison
Air-Gap BridgingTechnique of crossing air-gapped networks via USB drives, as used by Stuxnet's initial access method

Tools & Systems

  • Claroty xDome: Continuous PLC logic monitoring with baseline comparison and change detection
  • SIGA OT Solutions: Physical signal monitoring at the electrical level for detecting process manipulation
  • Nozomi Guardian: OT monitoring with PLC program change detection capabilities
  • Siemens SINEMA Remote Connect: Secure remote access with PLC project version tracking

Output Format

Stuxnet-Style Attack Detection Report
========================================
Monitored PLCs: [N]
Monitoring Period: YYYY-MM-DD to YYYY-MM-DD

PLC INTEGRITY:
  Baselines verified: [N]/[N]
  Logic modifications detected: [N]
  New blocks detected: [N]

PHYSICS ANOMALIES:
  Sensor correlation violations: [N]
  Process model deviations: [N]

ENGINEERING WORKSTATION:
  Unauthorized modifications: [N]
  USB connections: [N]

© 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-stuxnet-style-attacks 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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Questions about Detecting Stuxnet Style Attacks

What does Detecting Stuxnet Style Attacks do?

Detects sophisticated cyber-physical attacks that follow the Stuxnet pattern of modifying PLC logic while spoofing sensor readings to hide the manipulation, using PLC logic integrity monitoring…. Detecting Stuxnet Style Attacks is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Detects sophisticated cyber-physical attacks that follow the Stuxnet pattern of modifying PLC logic while spoofing sensor readings to hide the manipulation, using PLC logic integrity monitoring (Claroty xDome, Nozomi Guardian) and physics-based process anomaly detection.

When should I use Detecting Stuxnet Style Attacks?

Detecting Stuxnet Style Attacks fits situations like: hunting for IT-to-OT lateral movement; discrepancies between PLC program state and physical process behavior in ICS/SCADA environments.

How do I install Detecting Stuxnet Style Attacks in Claude Code?

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

How do I install Detecting Stuxnet Style Attacks in Codex?

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

Can I use Detecting Stuxnet Style Attacks 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-stuxnet-style-attacks -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-stuxnet-style-attacks, .gemini/skills/detecting-stuxnet-style-attacks, .github/skills/detecting-stuxnet-style-attacks and .opencode/skills/detecting-stuxnet-style-attacks in your project.

What does Detecting Stuxnet Style Attacks need to run?

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

Does Detecting Stuxnet Style Attacks 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 Stuxnet Style Attacks 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 Stuxnet Style Attacks use?

Detecting Stuxnet Style Attacks 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 Stuxnet Style Attacks use?

About 4.9k tokens (SKILL.md is roughly 20k 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 556 tokens, read only when the agent opens those files.

What are the alternatives to Detecting Stuxnet Style Attacks?

Skills that share tags, products or a category with Detecting Stuxnet Style Attacks: Threat Detection (alirezarezvani/claude-skills, 28k stars), Hashing Gate (tola-rs/tola-ssg, 178 stars), Anomalib Tiled Ensemble (open-edge-platform/anomalib, 6.2k stars) and Runtime Sentinel (LeoYeAI/openclaw-master-skills, 2.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Detecting Stuxnet Style Attacks?

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.