|
| 1 | +--- |
| 2 | +id: threat_context |
| 3 | +title: Threat Context |
| 4 | +--- |
| 5 | + |
| 6 | +## Overview |
| 7 | + |
| 8 | +The `threat_context` object provides geographic and industry-level intelligence about exploitation activity for a given CVE or reconnaissance rule. While [scores](./scores) tell you *how urgent* a threat is and [exploitation phases](./exploitation_phases) tell you *where in the lifecycle* it sits, threat context answers a different set of questions: |
| 9 | + |
| 10 | +- **Where are attacks coming from?** (attacker countries) |
| 11 | +- **Who is being targeted?** (defender countries, industry types, risk profiles) |
| 12 | +- **What do attackers want?** (attacker objectives) |
| 13 | + |
| 14 | +All distributions are **percentage-based** and sum to approximately 100. The top entries are listed individually, with an `OTHER` bucket aggregating the remainder. |
| 15 | + |
| 16 | +:::info Null and empty values |
| 17 | +- `threat_context: null` — The CVE or rule has no threat context data at all (insufficient telemetry). |
| 18 | +- Individual sub-fields as `{}` — Data for that dimension is not yet available, even though other dimensions may have data. This is common for very recently tracked CVEs. |
| 19 | +::: |
| 20 | + |
| 21 | +## Attacker Countries |
| 22 | + |
| 23 | +Shows the geographic distribution of attack traffic as observed by the CrowdSec Network. Keys are [ISO 3166-1 alpha-2](https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2) country codes; values are percentages. |
| 24 | + |
| 25 | +```json |
| 26 | +"attacker_countries": { |
| 27 | + "US": 48, |
| 28 | + "IE": 18, |
| 29 | + "DE": 7, |
| 30 | + "FR": 5, |
| 31 | + "NL": 4, |
| 32 | + "SG": 4, |
| 33 | + "GB": 2, |
| 34 | + "AE": 2, |
| 35 | + "VN": 1, |
| 36 | + "OTHER": 10 |
| 37 | +} |
| 38 | +``` |
| 39 | + |
| 40 | +:::info |
| 41 | +These reflect the **IP geolocation** of attacking infrastructure, not necessarily the nationality of the threat actor. Attackers routinely use cloud providers, VPNs, and compromised infrastructure worldwide. A high percentage for a given country means attack *traffic* originates there — not that the attacker is physically located there. |
| 42 | +::: |
| 43 | + |
| 44 | +## Defender Countries |
| 45 | + |
| 46 | +Shows which countries' infrastructure is being targeted, using the same format as attacker countries. |
| 47 | + |
| 48 | +```json |
| 49 | +"defender_countries": { |
| 50 | + "HU": 22, |
| 51 | + "FR": 22, |
| 52 | + "US": 12, |
| 53 | + "DE": 10, |
| 54 | + "AT": 8, |
| 55 | + "SM": 4, |
| 56 | + "SG": 3, |
| 57 | + "BE": 3, |
| 58 | + "NL": 2, |
| 59 | + "OTHER": 15 |
| 60 | +} |
| 61 | +``` |
| 62 | + |
| 63 | +:::tip |
| 64 | +If your organization operates primarily in countries that show high defender percentages, this CVE is disproportionately relevant to you. A CVE where 70% of targets are in your country warrants more attention than one spread evenly across the globe. |
| 65 | +::: |
| 66 | + |
| 67 | +## Industry Types |
| 68 | + |
| 69 | +Shows the distribution of targeted organizations by industry sector. |
| 70 | + |
| 71 | +| Value | Description | |
| 72 | +|---|---| |
| 73 | +| `commerce` | Retail, e-commerce, and commercial businesses | |
| 74 | +| `financial_services` | Banks, insurance, fintech, financial institutions | |
| 75 | +| `government` | Government agencies and public administration | |
| 76 | +| `healthcare` | Healthcare providers, hospitals, medical organizations | |
| 77 | +| `non_profit` | Non-profit organizations, NGOs, charities | |
| 78 | + |
| 79 | +```json |
| 80 | +"industry_types": { |
| 81 | + "financial_services": 1, |
| 82 | + "commerce": 71, |
| 83 | + "government": 4, |
| 84 | + "healthcare": 5, |
| 85 | + "non_profit": 19 |
| 86 | +} |
| 87 | +``` |
| 88 | + |
| 89 | +:::tip |
| 90 | +If your industry shows a high percentage, the CVE is disproportionately relevant to your sector — attackers are specifically hitting organizations like yours. |
| 91 | +::: |
| 92 | + |
| 93 | +## Industry Risk Profiles |
| 94 | + |
| 95 | +Classifies targets by their **technology risk profile** rather than their business sector. This provides a complementary lens to industry types — two organizations in the same industry may have very different exposure depending on their technology stack. |
| 96 | + |
| 97 | +| Value | Description | |
| 98 | +|---|---| |
| 99 | +| `critical_infrastructure` | Energy, water, transportation, telecommunications | |
| 100 | +| `homelab_and_iot` | Home labs, IoT devices, consumer-grade infrastructure | |
| 101 | +| `public_service` | Government services, education, public utilities | |
| 102 | +| `technology_business` | Technology-focused businesses, SaaS, software companies | |
| 103 | +| `traditional_business` | Non-tech commercial enterprises, manufacturing, logistics | |
| 104 | + |
| 105 | +```json |
| 106 | +"industry_risk_profiles": { |
| 107 | + "critical_infrastructure": 6, |
| 108 | + "traditional_business": 6, |
| 109 | + "public_service": 6, |
| 110 | + "technology_business": 65, |
| 111 | + "homelab_and_iot": 17 |
| 112 | +} |
| 113 | +``` |
| 114 | + |
| 115 | +## Attacker Objectives |
| 116 | + |
| 117 | +Shows the inferred goals of the exploitation campaigns. |
| 118 | + |
| 119 | +| Value | Description | |
| 120 | +|---|---| |
| 121 | +| `data_exfiltration` | Stealing sensitive data for sale, espionage, or leverage | |
| 122 | +| `infrastructure_takeover` | Gaining persistent control of target systems (botnets, cryptomining, proxying) | |
| 123 | +| `ransomware` | Encryption-based extortion campaigns | |
| 124 | + |
| 125 | +```json |
| 126 | +"attacker_objectives": { |
| 127 | + "ransomware": 7, |
| 128 | + "data_exfiltration": 11, |
| 129 | + "infrastructure_takeover": 82 |
| 130 | +} |
| 131 | +``` |
| 132 | + |
| 133 | +:::info |
| 134 | +These objectives are inferred from observed attack patterns and post-exploitation behavior across the CrowdSec Network, not from attacker self-reporting. A single campaign may exhibit multiple objectives. |
| 135 | +::: |
| 136 | + |
| 137 | +## Practical Example |
| 138 | + |
| 139 | +:::tip Reading a complete threat context |
| 140 | +Consider CVE-2024-0012 (PanOS Authentication Bypass). Its threat context shows: |
| 141 | + |
| 142 | +- **Attacker Countries**: 48% US, 18% IE — attacks are concentrated from US and Irish cloud infrastructure |
| 143 | +- **Defender Countries**: 22% HU, 22% FR — Hungarian and French organizations are disproportionately targeted |
| 144 | +- **Industry Types**: 71% commerce — commercial organizations are the primary targets |
| 145 | +- **Risk Profiles**: 65% technology_business — tech companies running PanOS infrastructure are the main victims |
| 146 | +- **Objectives**: 82% infrastructure_takeover — attackers want persistent access to PAN-OS management interfaces, not data theft |
| 147 | + |
| 148 | +This tells a SOC analyst: if you operate PAN-OS in a tech company in France or Hungary, this CVE should be at the top of your priority list. The attackers are not after your data — they want control of your firewall management plane. |
| 149 | + |
| 150 | +*Threat context is computed from live telemetry and changes over time. The values shown here may differ from what you see today.* |
| 151 | +::: |
| 152 | + |
| 153 | +## Accessing Threat Context |
| 154 | + |
| 155 | +- **Web Interface**: Available on each CVE and Reconnaissance Rule detail page in the [Live Exploit Tracker](https://tracker.crowdsec.net). |
| 156 | +- **API**: Returned in the `threat_context` field of the [CVE endpoints](./api_cves) (`/v1/cves` and `/v1/cves/{cve_id}`) and [Fingerprint endpoints](./api_fingerprints) (`/v1/fingerprints` and `/v1/fingerprints/{fingerprint}`). |
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