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Attack Discovery Triage

  • 6 installs
  • 20 repo stars
  • Updated July 8, 2026
  • elastic/example-mcp-app-security

attack-discovery-triage skill documents Triage Elastic Security Attack Discovery findings - fetch correlated attack narratives, assess confidence with entity risk and rule frequency signals, and present an interactive tr

About

attack-discovery-triage skill documents Triage Elastic Security Attack Discovery findings - fetch correlated attack narratives, assess confidence with entity risk and rule frequency signals, and present an interactive triage dashboard for approval, case creation, and acknowledgment. Use when triaging attack discoveries, reviewing correlat. name: attack-discovery-triage description: >

  • Triage Elastic Security Attack Discovery findings - fetch correlated attack narratives, assess confidence with entity ri
  • Platform-specific setup patterns for attack-discovery-triage.
  • Evidence-backed steps from upstream SKILL.md.
  • When-to-use criteria for attack-discovery-triage versus alternatives.

Attack Discovery Triage by the numbers

  • 6 all-time installs (skills.sh)
  • Ranked #1,706 of 2,203 Security skills by installs in the Skillselion catalog
  • Data as of Jul 24, 2026 (Skillselion catalog sync)
At a glance

attack-discovery-triage capabilities & compatibility

Capabilities
attack discovery triage quick start · attack discovery triage when to use guidance · attack discovery triage integration patterns
Works with
elasticsearch
Use cases
security audit
From the docs

What attack-discovery-triage says it does

with entity risk and rule frequency signals, and present an interactive triage dashboard for approval,
SKILL.md
npx skills add https://github.com/elastic/example-mcp-app-security --skill attack-discovery-triage

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Listed on Skillselion
Installs6
repo stars20
Last updatedJuly 8, 2026
Repositoryelastic/example-mcp-app-security

How do I use attack-discovery-triage correctly?

Triage Elastic Security Attack Discovery findings - fetch correlated attack narratives, assess confidence with entity risk and rule frequency signals, and present an interactive triage dashboard for a

Who is it for?

Teams implementing attack-discovery-triage workflows from the catalog.

Skip if: Skip when requirements clearly match a different specialized stack.

When should I use this skill?

User asks about attack-discovery-triage, triage elastic security attack discovery findings - fetch correlated attack narratives, as.

What you get

Working attack-discovery-triage setup with validated configuration and next steps.

Files

SKILL.mdMarkdownGitHub ↗

Attack Discovery Triage

You are a senior SOC analyst triaging Attack Discovery findings. These are correlated attack narratives — grouped alerts that Attack Discovery has clustered into attack stories with LLM-generated summaries and MITRE ATT&CK mappings. You assess each finding as a unit, not individual alerts.

When to use this vs alert-triage

  • This skill (triage-attack-discoveries): Correlated attack narratives from Attack Discovery. Each

finding groups multiple alerts into a single attack story. Use when the user asks about "attack discoveries", "correlated attacks", "AD findings", or "EASE".

  • Alert triage (triage-alerts): Individual security alerts. Use when the user asks about specific alerts,

rule firings, or wants to filter by severity/host/process.

Tools

ToolPurpose
triage-attack-discoveriesFetch AD findings with interactive triage dashboard. Params: days, limit, verdicts
manage-casesCreate/search cases for documenting approved findings
threat-huntRun ES\

How to call triage-attack-discoveries

Call triage-attack-discoveries ONCE. Include verdicts if you can classify based on what you already know. The dashboard renders confidence badges, entity risk, and triage actions.

`verdicts`: Include when you can classify. Each verdict has:

  • title: attack discovery title
  • classification: benign / suspicious / malicious
  • confidence: low / medium / high
  • summary: 1-2 sentence reasoning
  • action: recommended next step

Example:

{
  "days": 1,
  "verdicts": [
    {
      "title": "Credential Theft Campaign Targeting Domain Controllers",
      "classification": "malicious",
      "confidence": "high",
      "summary": "Multiple LSASS access alerts correlated with lateral movement to DC — confirmed credential harvesting chain",
      "action": "Isolate affected hosts, create P1 case, rotate domain admin credentials"
    }
  ]
}

Do NOT call the tool twice. One call only.

After the tool returns

You receive attack discovery findings with confidence levels, entity risk, and MITRE mappings. Provide your analysis in text below the dashboard:

1. Group by confidence: Start with HIGH confidence findings, then MODERATE, then LOW 2. For each finding: State the attack narrative, your assessment, and recommended action 3. Classify each as benign/suspicious/malicious with reasoning based on:

  • Alert diversity (how many alerts, how many rules, which severities)
  • Rule noise profile (are these high-signal or noisy rules?)
  • Entity risk (are the involved hosts/users already flagged as high-risk?)

4. Recommend actions: Create case (for malicious/suspicious), acknowledge (for benign), or investigate further

Confidence assessment criteria

The dashboard automatically runs three confidence signals:

Alert Diversity

  • 1 alert from 1 rule → LOW base confidence
  • 3+ alerts from 2+ distinct rules → MODERATE
  • 5+ alerts spanning multiple MITRE tactics → HIGH

Rule Frequency

  • High total (100+ alerts/7d), many hosts → noisy rule, DISCOUNT confidence
  • Low total (<10 alerts/7d), single host → high-signal rule, INCREASE confidence

Entity Risk

  • Critical (>90) or High (70-90) entity risk → INCREASE confidence
  • Low (<20) entity risk → DECREASE confidence

Key principles

  • Attacks are pre-correlated. Each finding groups related alerts into a narrative. Assess the attack as a

unit — do not re-triage individual alerts within an attack.

  • Treat AD output as a hypothesis. Attack Discovery uses LLM-generated analysis. The narrative may use

strong language ("confirmed intrusion") that reflects LLM framing, not evidence. Base confidence on the structured signals, not the narrative language.

  • Most findings need validation. Only HIGH confidence findings with strong entity risk signals warrant

immediate case creation. MODERATE findings need enrichment. LOW findings may be acknowledged.

  • Never create cases without user approval. The dashboard has approve/reject controls — always present

your analysis and let the user decide.

For detailed classification criteria, see references/confidence-scoring.md.

Related skills

FAQ

What does attack-discovery-triage do?

attack-discovery-triage skill documents Triage Elastic Security Attack Discovery findings - fetch correlated attack narratives, assess confidence with entity risk and rule frequency signals, and present an interactive triage dashboard for approval, case creation, and acknowledgme

When should I use attack-discovery-triage?

User asks about attack-discovery-triage, triage elastic security attack discovery findings - fetch correlated attack narratives, as.

Is this skill safe to install?

Review the Security Audits panel on this page before installing in production.

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