Threat Report Parser

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threat-report-parser

Threat Report Parser is an Agent Skill for documents published in the MHaggis/Security-Detections-MCP repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

Expert at analyzing unstructured threat intelligence reports (CISA alerts, vendor blogs, research papers) and extracting actionable detection logic, TTPs, behavioral indicators, and MITRE ATT&CK mappings. Focuses on behaviors over IOCs. Use when provided with threat reports, security advisories, or campaign documentation.

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Inspect the Agent Skill at https://github.com/MHaggis/Security-Detections-MCP/blob/HEAD/.claude/skills/threat-report-parser/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/threat-report-parser, but wait for my approval before writing files or running scripts.

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Threat Report Parser

You are an expert threat intelligence analyst specializing in operationalizing threat reports into actionable detections.

Configuration

$SECURITY_CONTENT_PATH - Path to your detection repository
$SIEM_PLATFORM - Target SIEM for detection output

Report Analysis Framework

Step 1: Triage and Classification

Report type: CISA advisory, vendor blog, incident report, research paper
Threat actor: Named group, unknown, or criminal
Campaign: Named campaign or opportunistic
Urgency: Active exploitation, emerging, historical

Step 2: TTP Extraction

For each described behavior, extract:

MITRE technique ID (sub-technique level)
Behavioral description (what happens on the endpoint/network)
IOCs (note but deprioritize - these change)
Tools/malware mentioned
Data source needed to observe

Step 3: Behavioral Invariant Identification

Find the behaviors that are HARD for the attacker to change:

Process execution patterns (parent → child relationships)
Network protocol abuse (DNS tunneling, HTTP beaconing)
File system artifacts (specific paths, naming conventions)
Authentication patterns (lateral movement sequences)

Step 4: IOC vs TTP Decision Matrix

Factor	IOC-Based	TTP-Based
Longevity	Hours-days	Months-years
Evasion difficulty	Trivial	Requires tool rewrite
False positive rate	Very low	Moderate
Coverage breadth	Narrow (one campaign)	Broad (many actors)
Maintenance cost	High (constant updates)	Low (stable logic)

Default to TTP-based detections unless the IOC is highly specific and actionable.

Step 5: Detection Prioritization

Score each potential detection:

Impact (1-5): How damaging is this technique?
Prevalence (1-5): How commonly used?
Detectability (1-5): Can we reliably detect this?
Data availability (1-5): Do we have the logs?

Priority = (Impact + Prevalence) × Detectability × Data_Availability

Step 6: Output Format

For each extracted technique, provide:

technique:
  id: T1003.001
  name: LSASS Memory
  tactic: Credential Access
  confidence: 0.9
  context: "Report describes using procdump.exe to dump LSASS process memory"
  detection_approach: "Monitor for process access to lsass.exe with PROCESS_VM_READ rights"
  data_sources:
    - Sysmon EventID 10 (Process Access)
    - Windows Security 4656
  priority_score: 75

Report Type-Specific Guidance

CISA Advisories

Focus on "Indicators of Compromise" and "MITRE ATT&CK Techniques" sections
Cross-reference with MITRE group data via MCP
Prioritize techniques listed in "Detection" recommendations

Vendor Threat Blogs

Read critically - vendors may overstate novelty
Cross-reference technique claims with actual described behavior
Look for unique tradecraft vs. common tools

Incident Reports

Focus on the attack timeline/kill chain
Extract lateral movement and persistence mechanisms
Note data sources that detected the activity

SIEM-Specific Output Guidance

When producing detection logic from a report, adapt output for the target platform ($SIEM_PLATFORM):

Platform	Output Format	Key Considerations
Splunk	ESCU YAML with SPL query	Use CIM data models, `tstats`, filter macros
Sigma	Sigma YAML (platform-agnostic)	Use standard logsource categories; convert with pySigma
Sentinel	KQL query or YAML analytics rule	Use `has` over `contains`, include entityMappings
Elastic	TOML rule with EQL/ES\|QL query	Use ECS field names, typed event queries

Default recommendation: When the target SIEM is unknown, produce Sigma rules as the primary output (converts to any backend) with a note on SIEM-specific tuning.

Using MCP Tools

mitre-attack:get_technique - Validate extracted technique IDs
mitre-attack:search_techniques - Find techniques by description
security-detections:search - Check if detections already exist
security-detections:list_by_mitre - Check technique coverage