Analyzing Threat Landscape With MispSAFE

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The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: analyzing-threat-landscape-with-misp
description: Query a MISP (Malware Information Sharing Platform) instance via PyMISP
  to compute event statistics, IOC type breakdowns, threat actor galaxy clusters,
  and tag trends, and generate threat landscape reports with temporal trends. Use
  when asked to analyze threat intelligence data, summarize top threat actors or
  malware families, or produce a CTI landscape report from MISP events.
domain: cybersecurity
subdomain: threat-intelligence
tags:
- threat-intelligence
- misp
- threat-landscape
- ioc-analysis
- cti
- threat-sharing
version: '1.0'
author: mahipal
license: Apache-2.0
d3fend_techniques:
- File Metadata Consistency Validation
- Application Protocol Command Analysis
- Identifier Analysis
- Content Format Conversion
- Message Analysis
nist_csf:
- ID.RA-01
- ID.RA-05
- DE.CM-01
- DE.AE-02
mitre_attack:
- T1566
- T1071.001
- T1568
- T1583.001
- T1102
---


# Analyzing Threat Landscape with MISP


## When to Use

- When investigating security incidents that require analyzing threat landscape with misp
- 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

- Familiarity with threat intelligence concepts and tools
- Access to a test or lab environment for safe execution
- Python 3.8+ with required dependencies installed
- Appropriate authorization for any testing activities

## Instructions

1. Install dependencies: `pip install pymisp`
2. Configure MISP URL and API key.
3. Run the agent to generate threat landscape analysis:
   - Pull event statistics by threat level and date range
   - Analyze attribute type distributions (IP, domain, hash, URL)
   - Identify top MITRE ATT&CK techniques from event tags
   - Track threat actor activity via galaxy clusters
   - Generate temporal trend analysis of IOC submissions

```bash
python scripts/agent.py --misp-url https://misp.local --api-key YOUR_KEY --days 90 --output landscape_report.json
```

## Examples

### Threat Landscape Summary
```
Period: Last 90 days
Events analyzed: 1,247
Top threat level: High (43%)
Top attribute type: ip-dst (31%), domain (22%), sha256 (18%)
Top MITRE technique: T1566 Phishing (89 events)
Top threat actor: APT28 (34 events)
```

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

What the source does

Filesystem

none-observed

Network

none-observed

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.