CVE Intelligence

This pillar is engineered for massive scale, continuously ingesting telemetry from cloud environments, container registries, code repositories, and external attack surfaces, and mapping it against real-time global threat intelligence to provide zero-day exposure visibility and automated remediation workflows.

What This Pillar Monitors

CyberFurl's CVE Intelligence pillar provides exhaustive, real-time surveillance across the entire hardware, software, and cloud application stack. The platform continuously monitors:

1. Software Bill of Materials (SBOM) & Transitive Dependencies: CyberFurl ingests CycloneDX and SPDX SBOMs directly from your CI/CD pipelines and runtime environments. It maps not just primary libraries, but deep transitive dependencies (the libraries your libraries use) against emerging vulnerability feeds. This allows for instant identification of deeply embedded flaws like Log4j or Spring4Shell across thousands of microservices in milliseconds.
2. Container Images & Kubernetes Configurations: Continuous monitoring of container registries (Docker Hub, AWS ECR, GCP Artifact Registry) and running Kubernetes pods. The system dynamically extracts layer information, identifies outdated base images, and correlates running kernel versions against known local privilege escalation (LPE) CVEs.
3. Operating Systems & Kernel Modules: Continuous assessment of Windows, Linux, and macOS endpoints via agentless integrations or lightweight eBPF sensors. This includes monitoring installed packages, kernel versions, active daemons, and missing security KB patches.
4. External Attack Surface (EASM): Unauthenticated, continuous enumeration of internet-facing assets. The system performs advanced banner grabbing, service fingerprinting, and protocol analysis to detect vulnerable exposed services (e.g., exposed RDP, outdated VPN gateways, unpatched Microsoft Exchange servers).
5. Global Exploit Chatter & Zero-Day Telemetry: Integration with deep/dark web intelligence feeds, Exploit-DB, GitHub PoC repositories, and Telegram channels. CyberFurl monitors for the release of weaponized exploits and maps this intelligence back to your asset inventory to identify zero-day exposure before the NVD releases an official CVE.
6. EPSS Probability Updates: Daily ingestion and recalculation of EPSS scores. While a CVSS score is static, an EPSS score is highly dynamic. CyberFurl monitors these daily fluctuations to alert security teams when a dormant vulnerability suddenly becomes highly likely to be exploited.
7. CISA KEV Catalog: Real-time synchronization with the Cybersecurity and Infrastructure Security Agency's Known Exploited Vulnerabilities catalog. Any vulnerability verified as actively exploited in the wild triggers immediate, highest-priority alerts.

Security Controls Covered

The CVE Intelligence pillar enforces over 35 continuous security controls aligned with frameworks like CIS-Controls (Control 7: Continuous Vulnerability Management) and NIST SP 800-53 (RA-5 Vulnerability Scanning). Key technical controls include:

1. Continuous Dynamic Attack Surface Scanning

Unlike heavy, authenticated network scans that impact performance, CyberFurl utilizes distributed, asynchronous network probing techniques. Leveraging customized masscan and Nmap scripting engines, the platform continuously profiles open ports, protocols, and service banners across external IP ranges. This control ensures that shadow IT and newly spun-up cloud instances are immediately assessed for known vulnerabilities.

2. eBPF-Powered Runtime Profiling

At the kernel level, CyberFurl deploys eBPF (Extended Berkeley Packet Filter) probes to monitor which vulnerable libraries are actually being loaded into memory by running processes. This control heavily reduces false positives; if a vulnerable library exists on disk but is never executed or loaded by a running application, its risk priority is dramatically downgraded.

3. Shift-Left CI/CD Pipeline Integration

Integrating vulnerability intelligence into the software development lifecycle (SDLC). This control provides plugins for GitHub Actions, GitLab CI, and Jenkins, performing dependency checks, static application security testing (SAST), and container image scanning before code is merged. Builds are automatically halted if they violate EPSS or CISA KEV thresholds.

4. Automated SBOM Ingestion & Property Graph Mapping

All discovered assets and software components are ingested into CyberFurl's highly scalable graph database (Neo4j). This control creates a multi-dimensional relationship map between infrastructure, applications, vulnerabilities, and threat actors. When a zero-day is announced, traversing this graph allows CyberFurl to identify the exact blast radius within milliseconds.

5. API-Driven Virtual Patching (WAF/IPS Orchestration)

For legacy applications or vulnerabilities that cannot be immediately patched, this control integrates with Web Application Firewalls (AWS WAF, Cloudflare) and Intrusion Prevention Systems (IPS). CyberFurl dynamically generates and pushes RegEx-based virtual patching rules to block exploit attempts at the edge while the engineering team tests the official patch.

6. Configuration Drift Detection

Vulnerabilities are not just missing patches; they are also insecure configurations. This control continuously monitors for drift from secure baselines (e.g., CIS Benchmarks), detecting instances where secure protocols have been downgraded, default credentials have been restored, or encryption ciphers have been weakened.

Risks Detected

CyberFurl’s CVE Intelligence engine is highly tuned to detect a wide array of cyber risks, emphasizing those that lead to systemic compromise. The platform identifies and categorizes risks into several critical technical vectors:

• Pre-Authentication Remote Code Execution (RCE): The most critical risk category. Flaws that allow unauthenticated attackers to execute arbitrary code over the network (e.g., CVE-2023-46805 in Ivanti Connect Secure). CyberFurl immediately flags assets exposing these services.
• Server-Side Request Forgery (SSRF): Vulnerabilities that allow attackers to manipulate a server into making HTTP requests to internal, protected resources. In cloud environments (AWS, GCP), SSRF is frequently weaponized to query the Instance Metadata Service (IMDS) and steal highly privileged IAM roles.
• Local Privilege Escalation (LPE): Vulnerabilities within the OS kernel or setuid binaries (like the infamous Polkit pkexec vulnerability, CVE-2021-4034) that allow a low-privileged user or compromised service account to gain root access.
• Insecure Deserialization: Flaws occurring when untrusted data is used to instantiate an object, often leading to RCE. CyberFurl deeply analyzes Java, Python, and .NET applications to identify outdated libraries vulnerable to deserialization attacks.
• Path Traversal & Local File Inclusion (LFI): Vulnerabilities allowing attackers to read arbitrary files on the server running an application, potentially exposing configuration files, source code, or password hashes.
• Supply Chain Poisoning & Typosquatting: Beyond traditional CVEs, CyberFurl detects the presence of malicious packages within the dependency tree—instances where attackers have published compromised packages to npm, PyPI, or RubyGems that mimic legitimate libraries.
• Zero-Day Exposure Windows: The critical gap between the discovery of a vulnerability by threat actors and the deployment of a patch. CyberFurl calculates the exposure window risk metric, predicting the likelihood of an asset being compromised based on its exposure duration and the current threat landscape.

Threat Examples

To understand the immense value of continuous CVE intelligence combined with EPSS and real-time threat telemetry, consider the following technical threat scenarios and how CyberFurl’s architecture mitigates them.

Scenario 1: The Zero-Day Supply Chain Compromise (The "Next Log4j" Scenario)

Imagine a critical, unauthenticated RCE vulnerability is discovered in a ubiquitous open-source logging library. The vulnerability is publicly disclosed on Twitter and GitHub via a Proof-of-Concept (PoC) script, but the National Vulnerability Database (NVD) has not yet analyzed it, and no CVE ID has been officially published.

The CyberFurl Response:

1. Threat Ingestion: CyberFurl’s CTI engine detects the sudden spike in GitHub repositories containing the term "RCE" and the library name, correlating it with dark web chatter discussing active exploitation.
2. SBOM Graph Traversal: Within seconds, CyberFurl queries the centralized graph database containing the SBOMs of all your deployed container images and applications.
3. Blast Radius Identification: The platform instantly identifies 14 microservices and 3 external-facing APIs that utilize the vulnerable version of the logging library deeply nested as a transitive dependency.
4. Zero-Day Alerting: An emergency alert is generated via webhook to the SOC, classifying the risk as a "Predicted Critical Zero-Day" despite the lack of a formal CVE.
5. Automated Mitigation: CyberFurl orchestrates a call to the Cloudflare WAF API, deploying a custom managed rule that inspects incoming HTTP headers for the specific JNDI lookup string associated with the exploit, virtually patching the perimeter instantly.

Scenario 2: EPSS Driven Prioritization of an "Ignored" Vulnerability

A vulnerability in an obscure file parsing daemon (CVE-2022-XXXXX) was published a year ago. At the time, it was given a CVSS base score of 6.5 (Medium) because exploitation required high complexity and non-standard configurations. Most organizations placed it in the "accept risk" or "backlog" category.

The CyberFurl Response:

1. EPSS Recalculation: A well-known Ransomware-as-a-Service (RaaS) cartel discovers a novel way to chain this vulnerability, bypassing the previous complexity requirements. They incorporate the exploit into their automated scanning tools.
2. Probability Spike: The FIRST EPSS model detects this massive increase in real-world exploitation activity. The EPSS score for CVE-2022-XXXXX spikes overnight from 0.02 (2% probability) to 0.89 (89% probability).
3. Dynamic Reprioritization: CyberFurl ingests the new EPSS feed. The vulnerability management engine recalculates the risk matrix for all assets.
4. Targeted Alerting: The platform identifies 50 internal servers running the vulnerable daemon. Because the EPSS score breached the predefined critical threshold (e.g., > 0.50), CyberFurl automatically escalates the ticket in Jira to P1 (Critical), bypassing the standard SLA workflow and alerting the incident response team to patch immediately.

Continuous Monitoring Workflow

The technical architecture of CyberFurl’s continuous monitoring workflow is designed for high-throughput, low-latency data processing, ensuring that security intelligence is actionable the moment it is generated.

graph TD
    A[External Threat Intel] -->|Ingest CVE, KEV, EPSS| C(CyberFurl Data Lake)
    B[Dark Web / GitHub PoC] -->|NLP & Heuristics| C
    D[Cloud Assets AWS/GCP] -->|API Polling| E(Asset Discovery Engine)
    F[CI/CD Pipelines] -->|SBOM Push| E
    G[eBPF Runtime Sensors] -->|Loaded Modules| E
    E --> C
    C --> H{Correlation & Risk Engine}
    H -->|Calculate Contextual Risk| I[(Neo4j Vulnerability Graph)]
    I --> J[Alerting & Routing]
    J --> K[Jira/ServiceNow Tickets]
    J --> L[SOAR / WAF Virtual Patch]

1. Telemetry Ingestion & Normalization

The workflow begins with continuous ingestion of both internal telemetry and external intelligence. CyberFurl utilizes Apache Kafka clusters to handle high-velocity event streams from cloud APIs, network scanners, and agent sensors. External feeds (NVD JSON feeds, EPSS CSVs, CISA KEV API) are polled continuously. All data is normalized into a unified schema, mapping software titles to standardized Common Platform Enumeration (CPE) formatting.

2. Contextual Risk Calculation (The Scoring Engine)

CyberFurl does not rely on CVSS alone. The platform utilizes a proprietary multi-dimensional risk algorithm that calculates a Contextual Risk Score (CRS) from 0 to 100. The algorithm weighs the following factors:

• Vulnerability Severity: CVSS Base, Temporal, and Environmental scores.
• Exploitability Probability: Real-time EPSS score (0.01 - 1.00).
• Active Exploitation: Boolean flag from the CISA KEV catalog.
• Asset Criticality: Defined by the user (e.g., Domain Controller = High, Dev Server = Low).
• Network Exposure: Is the asset reachable from the internet? (Derived from EASM and Cloud Security Group analysis).
• Runtime Execution: Is the vulnerable library actually loaded in memory? (Derived from eBPF sensors).

Mathematically, the calculation approximates: CRS = Base_CVSS * (1 + EPSS_Weight) * Asset_Criticality_Factor * Exposure_Multiplier

3. Graph Correlation & Blast Radius Analysis

All normalized data is pushed into a highly available Neo4j graph database. This allows CyberFurl to perform complex traversals that relational databases cannot handle at scale. When a new vulnerability enters the system, the correlation engine executes Cypher queries to find all paths from the internet to the vulnerable asset, determining the true blast radius and potential attack paths.

4. Automated Orchestration

If a vulnerability exceeds predefined risk thresholds, the workflow triggers automated orchestration. This involves generating highly detailed JSON payloads that are pushed to SIEMs (Splunk, Sentinel), ticketing systems (Jira, ServiceNow), or SOAR platforms for automated containment.

Alerts Generated

To prevent alert fatigue—the nemesis of modern SOCs—CyberFurl employs stringent thresholding and deduplication logic. Alerts are not generated every time a vulnerability is found; they are generated when a vulnerability matters.

Machine Learning Alert Deduplication & Grouping

Before an alert is fired, it passes through CyberFurl's ML-driven deduplication engine. In a Kubernetes cluster, a single vulnerable base image might be instantiated across 5,000 running pods. Traditional scanners would generate 5,000 separate alerts, overwhelming the ticketing system. CyberFurl uses unsupervised clustering algorithms to group these instances into a single, high-fidelity meta-alert based on the root cause (e.g., "Base Image node:14-alpine contains CVE-202X-XXXX").

The engine also deduplicates temporally. If a vulnerability fluctuates around an EPSS threshold (e.g., dropping from 0.51 to 0.49 and back), CyberFurl applies hysteresis smoothing to prevent alert flapping.

Types of Alerts

1. Critical Risk Exposure (CRE): Triggered when a new or existing vulnerability on an internet-facing asset exceeds an EPSS probability of 0.60 or is added to the CISA KEV catalog. Requires immediate, out-of-band response.
2. Zero-Day Intelligence Warning: Triggered when high-confidence exploit chatter is detected for software present in the environment, prior to a CVE assignment.
3. SLA Breach Notification: Triggered when a vulnerability remains unpatched beyond the organizationally defined Service Level Agreement (e.g., Critical patch > 14 days).
4. Pipeline Guardrail Violation: Triggered within the CI/CD pipeline when a developer attempts to merge code introducing a highly exploitable dependency, resulting in a failed build.

Webhook Alert Payload Example

CyberFurl delivers rich, contextual alerts via standard webhooks. The JSON payload provides everything an analyst or automated playbook needs to begin remediation instantly.

{
  "alert_id": "cve-int-2026-99384",
  "timestamp": "2026-06-04T08:15:32Z",
  "alert_type": "Critical Risk Exposure",
  "severity": "CRITICAL",
  "contextual_risk_score": 98.5,
  "vulnerability": {
    "cve_id": "CVE-2024-55555",
    "title": "Unauthenticated RCE in Example Application",
    "cvss_v3_score": 9.8,
    "epss_probability": 0.85,
    "cisa_kev": true,
    "description": "A buffer overflow in the web interface of Example Application allows an unauthenticated remote attacker to execute arbitrary code.",
    "remediation": "Apply vendor patch 2.4.1 immediately. Alternatively, block traffic containing the 'X-Exploit-Header' using the provided WAF rule."
  },
  "asset_details": {
    "asset_id": "ast-8837-vpc-aws",
    "hostname": "gateway-prod-us-east.example.com",
    "ip_address": "198.51.100.42",
    "exposure": "Internet Facing",
    "criticality": "Tier 1 - Mission Critical",
    "owner": "platform-engineering-team"
  },
  "evidence": {
    "port_protocol": "443/tcp",
    "banner_grab": "Example-Server/2.3.9",
    "ebpf_status": "Loaded in memory (PID 4492)"
  },
  "recommended_actions": [
    "Deploy patch via Ansible playbook 'update-example-app.yml'",
    "Apply WAF Virtual Patch ID 'waf-vp-774'"
  ],
  "dashboard_url": "https://console.cyberfurl.com/intelligence/cve-2024-55555/ast-8837"
}

Remediation Guidance

Detection is only the first step; effective remediation is the ultimate goal of the CVE Intelligence pillar. CyberFurl provides actionable, deterministic remediation guidance mapped to the specific infrastructure type.

1. Automated Patching Workflows

For standardized environments, CyberFurl generates configuration-as-code snippets (e.g., Ansible playbooks, Chef recipes, Terraform plans) that can be executed directly or via a Pull Request to update the vulnerable software. For containerized environments, CyberFurl provides the exact Dockerfile modification required (e.g., FROM ubuntu:20.04 -> FROM ubuntu:22.04).

2. Cloud-Native & Kubernetes Remediation

In highly ephemeral cloud-native environments, patching a running container via SSH is an anti-pattern. CyberFurl integrates tightly with Kubernetes admission controllers (e.g., OPA Gatekeeper, Kyverno) to enforce immutable infrastructure principles.

When a critical vulnerability is detected in a running pod:

• Node Cordoning & Draining: CyberFurl orchestrates a workflow to safely cordon the underlying Kubernetes node if the vulnerability resides at the Kubelet or OS level, draining the workloads to healthy nodes before terminating the vulnerable instance.
• Dynamic Admission Control: The platform automatically updates OPA Gatekeeper policies to reject the instantiation of any pod utilizing an image hash known to contain a vulnerability with an EPSS > 0.80.
• GitOps Orchestration: CyberFurl automatically creates a Pull Request in your infrastructure-as-code (IaC) repository (e.g., modifying the deployment.yaml or Helm chart to reference the patched image version). Once the PR is merged, your GitOps continuous delivery tool (like ArgoCD or Flux) synchronizes the state, spinning up secure pods and terminating the vulnerable ones organically without downtime.

3. Virtual Patching and Mitigating Controls

When immediate patching is impossible due to operational constraints (e.g., legacy medical devices, mission-critical ICS systems), CyberFurl generates mitigating controls. This includes:

• WAF/IPS Signatures: Custom regular expressions to drop malicious packets before they reach the vulnerable service.
• Network Segmentation: Recommendations for zero-trust firewall rules to isolate the vulnerable asset from the rest of the network, strictly limiting inbound and outbound communication to authorized IPs only.
• Service Configuration: Guidance on disabling the specific vulnerable feature or module if the full application cannot be patched.

4. Service Level Agreement (SLA) Tracking

Remediation must be measurable. CyberFurl allows organizations to define strict SLAs based on the Contextual Risk Score (CRS). The platform continuously monitors the environment and escalates tickets automatically.

• CRS 90 - 100 (Critical/Exploited): Remediate within 24 Hours.
• CRS 70 - 89 (High Risk/High EPSS): Remediate within 7 Days.
• CRS 40 - 69 (Medium Risk): Remediate within 30 Days.
• CRS 0 - 39 (Low Risk/No Exposure): Remediate within 90 Days.

CyberFurl’s dashboards provide real-time metrics on SLA compliance, Mean Time to Detect (MTTD), and Mean Time to Remediate (MTTR), allowing CISOs to accurately report on the organization's risk posture to the board.

API Integration

CyberFurl is API-first, allowing extensive integration into existing SOC tooling, SOAR platforms, and custom developer workflows. The CVE Intelligence API is a RESTful service adhering to OpenAPI 3.0 standards, secured via bearer tokens.

Base URL

https://api.cyberfurl.com/v2/intelligence/cve

Key Endpoints

1. Query Vulnerabilities by EPSS Threshold

Retrieve a list of vulnerabilities affecting your infrastructure that have an EPSS score greater than a specified threshold.

Request:

curl -X GET "https://api.cyberfurl.com/v2/intelligence/cve/active?min_epss=0.50&include_cisa_kev=true" \
     -H "Authorization: Bearer $API_TOKEN" \
     -H "Content-Type: application/json"

Response (200 OK):

{
  "count": 2,
  "vulnerabilities": [
    {
      "cve_id": "CVE-2023-XXXX1",
      "epss_score": 0.92,
      "cisa_kev": true,
      "affected_assets_count": 14,
      "severity": "CRITICAL"
    },
    {
      "cve_id": "CVE-2023-XXXX2",
      "epss_score": 0.65,
      "cisa_kev": false,
      "affected_assets_count": 3,
      "severity": "HIGH"
    }
  ]
}

2. CI/CD Pipeline Dependency Check (Shift-Left)

Used within a CI/CD pipeline to submit an SBOM and receive a synchronous pass/fail decision based on organizational risk policies.

Request:

curl -X POST "https://api.cyberfurl.com/v2/intelligence/pipeline/analyze" \
     -H "Authorization: Bearer $API_TOKEN" \
     -H "Content-Type: application/json" \
     -d '{
           "project_name": "payment-gateway-service",
           "sbom_format": "CycloneDX",
           "policy_id": "pol-strict-no-criticals",
           "sbom_payload": "<BASE64_ENCODED_SBOM_XML_OR_JSON>"
         }'

Response (200 OK):

{
  "status": "FAILED",
  "policy_violations": 1,
  "details": [
    {
      "component": "org.yaml:snakeyaml:1.30",
      "cve_id": "CVE-2022-1471",
      "epss_score": 0.45,
      "reason": "Component contains a vulnerability exceeding the allowed EPSS threshold of 0.20."
    }
  ],
  "recommendation": "Upgrade org.yaml:snakeyaml to version 2.0 or higher."
}

3. GraphQL queries for Asset-Vulnerability Mapping

While the REST API provides robust atomic endpoints, complex attack surface queries are best executed via CyberFurl's GraphQL interface. This endpoint allows security teams to dynamically define the shape of the response, reducing over-fetching when extracting vast quantities of asset dependency trees.

Request:

curl -X POST "https://api.cyberfurl.com/v2/intelligence/graphql" \
     -H "Authorization: Bearer $API_TOKEN" \
     -H "Content-Type: application/json" \
     -d '{
       "query": "query {
         assets(environment: \"PRODUCTION\", exposure: INTERNET_FACING) {
           id
           hostname
           ipAddress
           vulnerabilities(minEpss: 0.6) {
             cveId
             cvssV3
             epssProbability
             cisaKev
             remediation {
               patchAvailable
               wafRuleId
             }
           }
         }
       }"
     }'

Response (200 OK):

{
  "data": {
    "assets": [
      {
        "id": "ast-4421-k8s-node",
        "hostname": "prod-k8s-worker-01.aws.example.com",
        "ipAddress": "203.0.113.15",
        "vulnerabilities": [
          {
            "cveId": "CVE-2023-44487",
            "cvssV3": 7.5,
            "epssProbability": 0.88,
            "cisaKev": true,
            "remediation": {
              "patchAvailable": true,
              "wafRuleId": "waf-vp-http2-rapid-reset"
            }
          }
        ]
      }
    ]
  }
}

4. Request Graph Blast Radius

Query the Neo4j backend to determine the attack paths to a specific vulnerable asset.

Request:

curl -X GET "https://api.cyberfurl.com/v2/intelligence/graph/blast-radius?asset_id=ast-992-db" \
     -H "Authorization: Bearer $API_TOKEN"

The response returns a complex JSON structure representing the graph nodes and edges, easily parseable by SOAR platforms to understand network dependencies and logical isolation points.

Advanced Threat Intelligence Correlation

Beyond basic CVE identification, CyberFurl's intelligence engine deeply correlates identified vulnerabilities with known Threat Actor (TA) groups and Advanced Persistent Threats (APTs). By utilizing the MITRE ATT&CK framework mapping, the platform translates raw CVE data into tactical threat intelligence.

For example, if an unpatched Fortinet vulnerability (e.g., CVE-2022-42475) is detected on an edge firewall, CyberFurl not only alerts on the vulnerability but also correlates it with known exploitation by state-sponsored actors. The alert will include specific TTPs (Tactics, Techniques, and Procedures) associated with the threat actor, allowing the SOC to preemptively hunt for Indicators of Compromise (IoCs) within the internal network.

This correlation engine ingest data from:

• Mandiant Threat Intelligence: For high-fidelity attribution.
• CrowdStrike Falcon Intelligence: For endpoint-level indicator mapping.
• Open Source Intelligence (OSINT): Twitter, security blogs, and academic research papers parsed via natural language processing (NLP).

Performance Metrics & Scalability

The continuous monitoring architecture is built to process petabytes of telemetry without introducing latency into production environments.

• Ingestion Rate: Capable of processing over 100,000 SBOMs and 5 million vulnerability events per minute.
• Query Latency: GraphQL and REST API queries maintain a p99 latency of under 50ms, even when performing complex graph traversals.
• Storage Efficiency: Time-series telemetry and vulnerability state are compressed using advanced columnar storage formats (Parquet) in cloud object storage, reducing retention costs while enabling multi-year historical auditing.