threat-detection

Imported from borghei/claude-skills (engineering/threat-detection/SKILL.md). Install upstream with npx skills add borghei/claude-skills --skill threat-detection. Copyright stays with the author (MIT + Commons Clause).

Threat Detection

Category: Engineering Domain: Security Operations

Overview

The Threat Detection skill provides automated analysis of log files for suspicious patterns including brute force attempts, injection attempts, path traversal, admin path probing and request flooding. It helps security teams triage log data and identify threats before they escalate.

Clarify First

Before analyzing logs, settle the inputs below. When one is missing or vague and would change the result, ask about it; when it wouldn't, use a sensible default and say which one you used.

• Log file & source type: auth / access / application logs to analyze (--file; the subject of detection).
• Threat category: injection / auth (brute force) / traversal / probe / rate (--category; focuses the scan).
• Minimum severity: the reporting/alert threshold (--min-severity; changes which signals surface and any SIEM/CI gate).

Ask about the two or three inputs that matter most for this request, not the whole list. If the user wants a result straight away, produce it and state your assumptions at the top.

Quick Start

# Analyze a log file for threat signals
python scripts/threat_signal_analyzer.py --file /var/log/auth.log

# Analyze with specific threat category
python scripts/threat_signal_analyzer.py --file access.log --category injection

# JSON output for SIEM integration
python scripts/threat_signal_analyzer.py --file auth.log --format json

# Set minimum severity
python scripts/threat_signal_analyzer.py --file access.log --min-severity high

Tools Overview

threat_signal_analyzer.py

Analyzes log files for suspicious activity patterns across multiple threat categories.

Workflows

Log Analysis Workflow

1. Collect - Gather logs from auth, access, application sources
2. Analyze - Run threat_signal_analyzer.py across log files
3. Triage - Review critical and high severity findings first
4. Correlate - Cross-reference findings across log sources
5. Respond - Block IPs, reset credentials, escalate as needed

Incident Investigation Workflow

1. Scope - Identify time window and affected systems
2. Scan - Run analyzer on all relevant log files
3. Timeline - Build timeline from threat signals
4. Impact - Assess what was accessed or modified
5. Contain - Block threat actors and patch vulnerabilities

Continuous Monitoring

# Cron job: analyze auth logs every hour
python scripts/threat_signal_analyzer.py --file /var/log/auth.log --format json --min-severity high > /tmp/threat_report.json

# CI/CD: scan application logs on deployment
python scripts/threat_signal_analyzer.py --file app.log --category injection --format json

Scope & Limitations

In Scope:

• Rule-based scanning of a single log file with threat_signal_analyzer.py for SQL injection, XSS, command injection, path traversal, failed authentication and brute force, admin path probing, and request flooding.
• Filtering by --category (injection, auth, traversal, probe, rate) and --min-severity, with text or JSON output.
• Attack categories, severity levels, common log formats, scanner indicators and an incident response playbook in references/threat-indicators.md.

Out of Scope:

• Real-time monitoring, alert routing or SIEM integration. The script reads one file per run.
• Running an incident response (see engineering/incident-commander/) and building a security operations program (see engineering/senior-secops/).
• Vault audit log analysis (see engineering/secrets-vault-manager/).
• Blocking IPs or changing any system. The script reports signals only.

Important Caveats:

• Detection uses regular expressions on log lines and simple counts. Encoded or new attack strings can be missed, and legitimate traffic can match.
• Brute force and flooding thresholds count events per IP across the whole file, not within a time window.
• The script does not detect privilege escalation, check IP reputation or flag unusual access times.
• A quiet report does not mean no attack took place. Findings need correlation with other sources before any response.

Reference Documentation

• Threat Indicators - Common attack patterns, indicators of compromise, response playbooks

Common Patterns Quick Reference

Threat Categories

Severity Levels

• CRITICAL - Active exploitation attempt (injection, RCE)
• HIGH - Likely attack in progress (brute force, XSS, path traversal, flooding)
• MEDIUM - Suspicious activity requiring investigation
• LOW - Informational, possible false positive

Response Actions