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Analyzing Apt Group With Mitre Navigator

mukul975/anthropic-cybersecurity-skills

  • 417 installs
  • 27.3k repo stars
  • Updated August 2, 2026
  • mukul975/anthropic-cybersecurity-skills

Analyzing APT Group with MITRE Navigator is an agent skill that maps advanced persistent threat behaviors to MITRE ATT&CK Navigator layers and techniques.

About

Analyzing APT Group with MITRE Navigator is an agent skill for security-minded solo builders who need to turn vague “advanced persistent threat” stories into concrete technique mappings. Instead of ad-hoc bullet lists, the workflow centers MITRE ATT&CK Navigator: you structure layers, tie behaviors to techniques and tactics, and produce a matrix others can scan in seconds. It fits when you are validating a niche B2B product’s threat model, preparing a ship checklist for a SaaS handling sensitive data, or documenting why certain controls matter for compliance conversations. The skill does not replace a SOC or formal red team; it gives you a disciplined, citable way to reason about named threat actors and their tradecraft using the same vocabulary large security teams use. Pair it with your own threat intel sources and environment-specific assumptions.

  • Guides analysis of APT groups using MITRE ATT&CK Navigator layers and techniques
  • Aligns findings to enterprise-standard ATT&CK taxonomy for reports and briefings
  • Supports repeatable threat-intel workflows for solo builders shipping security-sensitive products
  • Output-friendly for sharing matrices with stakeholders or pasting into docs

Analyzing Apt Group With Mitre Navigator by the numbers

  • 417 all-time installs (skills.sh)
  • +28 installs in the week ending Aug 4, 2026 (Skillselion tracking)
  • Ranked #545 of 2,203 Security skills by installs in the Skillselion catalog
  • Security screen: MEDIUM risk (skills.sh audit)
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill analyzing-apt-group-with-mitre-navigator

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Installs417
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Last updatedAugust 2, 2026
Repositorymukul975/anthropic-cybersecurity-skills ↗

What it does

Map adversary TTPs to MITRE ATT&CK layers in Navigator so you can document threat models or incident hypotheses in a standard visual format.

Who is it for?

Best when you're shipping APIs or SaaS and must explain realistic attacker TTPs using MITRE’s standard framework.

Skip if: Skip if you need live SIEM correlation, managed detection, or certified pen-test deliverables without doing your own intel sourcing.

When should I use this skill?

You need to analyze or present an APT group’s behaviors using MITRE ATT&CK Navigator rather than unstructured notes.

What you get

You leave with a Navigator-oriented technique map and narrative hooks tied to ATT&CK, ready to paste into threat models or ship security docs.

  • ATT&CK-aligned technique mapping narrative
  • Navigator layer structure guidance
  • Threat-model talking points for stakeholders

Files

SKILL.mdMarkdownGitHub ↗

Analyzing APT Group with MITRE ATT&CK Navigator

Overview

MITRE ATT&CK Navigator is a web-based tool for annotating and exploring ATT&CK matrices, enabling analysts to visualize threat actor technique coverage, compare multiple APT groups, identify detection gaps, and build threat-informed defense strategies. This skill covers querying ATT&CK data programmatically, mapping APT group TTPs to Navigator layers, creating multi-layer overlays for gap analysis, and generating actionable intelligence reports for detection engineering teams.

When to Use

  • When investigating security incidents that require analyzing apt group with mitre navigator
  • When building detection rules or threat hunting queries for this domain
  • When SOC analysts need structured procedures for this analysis type
  • When validating security monitoring coverage for related attack techniques

Prerequisites

  • Python 3.9+ with attackcti, mitreattack-python, stix2, requests libraries
  • ATT&CK Navigator (https://mitre-attack.github.io/attack-navigator/) or local deployment
  • Understanding of ATT&CK Enterprise matrix: 14 Tactics, 200+ Techniques, Sub-techniques
  • Access to threat intelligence reports or MISP/OpenCTI for threat actor data
  • Familiarity with STIX 2.1 Intrusion Set and Attack Pattern objects

Key Concepts

ATT&CK Navigator Layers

Navigator layers are JSON files that annotate ATT&CK techniques with scores, colors, comments, and metadata. Each layer can represent a single APT group's technique usage, a detection capability map, or a combined overlay. Layer version 4.5 supports enterprise-attack, mobile-attack, and ics-attack domains with filtering by platform (Windows, Linux, macOS, Cloud, Azure AD, Office 365, SaaS).

APT Group Profiles in ATT&CK

ATT&CK catalogs over 140 threat groups with documented technique usage. Each group profile includes aliases, targeted sectors, associated campaigns, software used, and technique mappings with procedure-level detail. Groups are identified by G-codes (e.g., G0016 for APT29, G0007 for APT28, G0032 for Lazarus Group).

Multi-Layer Analysis

The Navigator supports loading multiple layers simultaneously, allowing analysts to overlay threat actor TTPs against detection coverage to identify gaps, compare multiple APT groups to find common techniques worth prioritizing, and track technique coverage changes over time.

Workflow

Step 1: Query ATT&CK Data for APT Group

from attackcti import attack_client
import json

lift = attack_client()

# Get all threat groups
groups = lift.get_groups()
print(f"Total ATT&CK groups: {len(groups)}")

# Find APT29 (Cozy Bear / Midnight Blizzard)
apt29 = next((g for g in groups if g.get('name') == 'APT29'), None)
if apt29:
    print(f"Group: {apt29['name']}")
    print(f"Aliases: {apt29.get('aliases', [])}")
    print(f"Description: {apt29.get('description', '')[:300]}")

# Get techniques used by APT29 (G0016)
techniques = lift.get_techniques_used_by_group("G0016")
print(f"APT29 uses {len(techniques)} techniques")

technique_map = {}
for tech in techniques:
    tech_id = ""
    for ref in tech.get("external_references", []):
        if ref.get("source_name") == "mitre-attack":
            tech_id = ref.get("external_id", "")
            break
    if tech_id:
        tactics = [p.get("phase_name", "") for p in tech.get("kill_chain_phases", [])]
        technique_map[tech_id] = {
            "name": tech.get("name", ""),
            "tactics": tactics,
            "description": tech.get("description", "")[:500],
            "platforms": tech.get("x_mitre_platforms", []),
            "data_sources": tech.get("x_mitre_data_sources", []),
        }

Step 2: Generate Navigator Layer JSON

def create_navigator_layer(group_name, technique_map, color="#ff6666"):
    techniques_list = []
    for tech_id, info in technique_map.items():
        for tactic in info["tactics"]:
            techniques_list.append({
                "techniqueID": tech_id,
                "tactic": tactic,
                "color": color,
                "comment": info["name"],
                "enabled": True,
                "score": 100,
                "metadata": [
                    {"name": "group", "value": group_name},
                    {"name": "platforms", "value": ", ".join(info["platforms"])},
                ],
            })

    layer = {
        "name": f"{group_name} TTP Coverage",
        "versions": {"attack": "16.1", "navigator": "5.1.0", "layer": "4.5"},
        "domain": "enterprise-attack",
        "description": f"Techniques attributed to {group_name}",
        "filters": {
            "platforms": ["Linux", "macOS", "Windows", "Cloud",
                          "Azure AD", "Office 365", "SaaS", "Google Workspace"]
        },
        "sorting": 0,
        "layout": {
            "layout": "side", "aggregateFunction": "average",
            "showID": True, "showName": True,
            "showAggregateScores": False, "countUnscored": False,
        },
        "hideDisabled": False,
        "techniques": techniques_list,
        "gradient": {"colors": ["#ffffff", color], "minValue": 0, "maxValue": 100},
        "legendItems": [
            {"label": f"Used by {group_name}", "color": color},
            {"label": "Not observed", "color": "#ffffff"},
        ],
        "showTacticRowBackground": True,
        "tacticRowBackground": "#dddddd",
        "selectTechniquesAcrossTactics": True,
        "selectSubtechniquesWithParent": False,
        "selectVisibleTechniques": False,
    }
    return layer

layer = create_navigator_layer("APT29", technique_map)
with open("apt29_layer.json", "w") as f:
    json.dump(layer, f, indent=2)
print("[+] Layer saved: apt29_layer.json")

Step 3: Compare Multiple APT Groups

groups_to_compare = {"G0016": "APT29", "G0007": "APT28", "G0032": "Lazarus Group"}
group_techniques = {}

for gid, gname in groups_to_compare.items():
    techs = lift.get_techniques_used_by_group(gid)
    tech_ids = set()
    for t in techs:
        for ref in t.get("external_references", []):
            if ref.get("source_name") == "mitre-attack":
                tech_ids.add(ref.get("external_id", ""))
    group_techniques[gname] = tech_ids

common_to_all = set.intersection(*group_techniques.values())
print(f"Techniques common to all groups: {len(common_to_all)}")
for tid in sorted(common_to_all):
    print(f"  {tid}")

for gname, techs in group_techniques.items():
    others = set.union(*[t for n, t in group_techniques.items() if n != gname])
    unique = techs - others
    print(f"\nUnique to {gname}: {len(unique)} techniques")

Step 4: Detection Gap Analysis with Layer Overlay

# Define your current detection capabilities
detected_techniques = {
    "T1059", "T1059.001", "T1071", "T1071.001", "T1566", "T1566.001",
    "T1547", "T1547.001", "T1053", "T1053.005", "T1078", "T1027",
}

actor_techniques = set(technique_map.keys())
covered = actor_techniques.intersection(detected_techniques)
gaps = actor_techniques - detected_techniques

print(f"=== Detection Gap Analysis for APT29 ===")
print(f"Actor techniques: {len(actor_techniques)}")
print(f"Detected: {len(covered)} ({len(covered)/len(actor_techniques)*100:.0f}%)")
print(f"Gaps: {len(gaps)} ({len(gaps)/len(actor_techniques)*100:.0f}%)")

# Create gap layer (red = undetected, green = detected)
gap_techniques = []
for tech_id in actor_techniques:
    info = technique_map.get(tech_id, {})
    for tactic in info.get("tactics", [""]):
        color = "#66ff66" if tech_id in detected_techniques else "#ff3333"
        gap_techniques.append({
            "techniqueID": tech_id,
            "tactic": tactic,
            "color": color,
            "comment": f"{'DETECTED' if tech_id in detected_techniques else 'GAP'}: {info.get('name', '')}",
            "enabled": True,
            "score": 100 if tech_id in detected_techniques else 0,
        })

gap_layer = {
    "name": "APT29 Detection Gap Analysis",
    "versions": {"attack": "16.1", "navigator": "5.1.0", "layer": "4.5"},
    "domain": "enterprise-attack",
    "description": "Green = detected, Red = gap",
    "techniques": gap_techniques,
    "gradient": {"colors": ["#ff3333", "#66ff66"], "minValue": 0, "maxValue": 100},
    "legendItems": [
        {"label": "Detected", "color": "#66ff66"},
        {"label": "Detection Gap", "color": "#ff3333"},
    ],
}
with open("apt29_gap_layer.json", "w") as f:
    json.dump(gap_layer, f, indent=2)

Step 5: Tactic Breakdown Analysis

from collections import defaultdict

tactic_breakdown = defaultdict(list)
for tech_id, info in technique_map.items():
    for tactic in info["tactics"]:
        tactic_breakdown[tactic].append({"id": tech_id, "name": info["name"]})

tactic_order = [
    "reconnaissance", "resource-development", "initial-access",
    "execution", "persistence", "privilege-escalation",
    "defense-evasion", "credential-access", "discovery",
    "lateral-movement", "collection", "command-and-control",
    "exfiltration", "impact",
]

print("\n=== APT29 Tactic Breakdown ===")
for tactic in tactic_order:
    techs = tactic_breakdown.get(tactic, [])
    if techs:
        print(f"\n{tactic.upper()} ({len(techs)} techniques):")
        for t in techs:
            print(f"  {t['id']}: {t['name']}")

Validation Criteria

  • ATT&CK data queried successfully via TAXII server
  • APT group mapped to all documented techniques with procedure examples
  • Navigator layer JSON validates and renders correctly in ATT&CK Navigator
  • Multi-layer overlay shows threat actor vs. detection coverage
  • Detection gap analysis identifies unmonitored techniques with data source recommendations
  • Cross-group comparison reveals shared and unique TTPs
  • Output is actionable for detection engineering prioritization

References

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How it compares

Use for structured ATT&CK visualization instead of free-form chat summaries of “hackers might do X.”

FAQ

Who is analyzing-apt-group-with-mitre-navigator for?

Developers doing security storytelling, threat modeling, or ship-phase reviews who want MITRE ATT&CK Navigator as the backbone.

When should I use analyzing-apt-group-with-mitre-navigator?

During Ship security work when you document APT-relevant TTPs, before launch diligence, or when validating scope for a security-sensitive feature.

Is analyzing-apt-group-with-mitre-navigator safe to install?

Review the Security Audits panel on this Prism page and the upstream skill repo license and scripts before running anything that touches your environment.

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