Imported from reason-machines/security-skills (
skills/k7-total-security-configuration/SKILL.md). Install upstream withnpx skills add reason-machines/security-skills --skill k7-total-security-configuration. Copyright stays with the author.
K7 Total Security Configuration
Skill by ara.so — Security Skills collection.
⚠️ SECURITY WARNING: This repository appears to distribute patches/cracks for commercial security software. The legitimate K7 Total Security is a commercial product from K7 Computing. This skill documents the claimed configuration patterns from the repository for educational purposes only. Always obtain proper licenses for commercial software and avoid downloading executables from unofficial sources.
Overview
The repository claims to provide configuration templates and deployment patterns for K7 Total Security 16.0.1195, presenting it as a unified endpoint protection framework with:
- Signatureless AI-based threat detection
- Multi-platform policy synchronization (Windows, macOS, Linux)
- Modular protection layers (executable guard, memory scanner, USB filter, web filter)
- AI integrations (OpenAI GPT, Claude) for automated incident analysis
- YAML-based configuration profiles
- Remote console management via CLI
Note: The architecture and code examples in the repository are configuration templates, not the actual security software.
Installation
According to the repository structure, deployment involves:
- Obtain configuration templates:
git clone https://github.com/29Hinojosa/K7-Total-Security-Unlock-Patch-16-0-1195.git
cd K7-Total-Security-Unlock-Patch-16-0-1195
- Directory structure (assumed):
.
├── profiles/ # YAML configuration profiles
├── k7-console # CLI management tool
├── admin_credentials.pem # Authentication certificates
└── deployment/ # Platform-specific installers
- Prerequisites:
- Administrative/root access on target endpoints
- Network connectivity for remote management
- API keys for AI integrations (optional)
Configuration Profile Structure
Basic Profile Format
Configuration profiles use YAML to define protection policies:
profile_name: "Enterprise Balanced 2026"
version: "16.0.1195"
enforcement_level: "adaptive" # Options: strict, adaptive, permissive
protection_layers:
executable_guard:
state: enabled # enabled | disabled
action_on_threat: "quarantine" # quarantine | block | alert
exceptions:
- path: "C:\\DevTools\\*"
- path: "/opt/development/*"
- hash: "a3f5e8d9c1b2..." # SHA256 whitelist
memory_scanner:
state: enabled
sensitivity: "high" # low | medium | high
scan_interval_seconds: 300
exclusions:
- process: "docker.exe"
- process: "vmware-vmx"
usb_filter:
state: enabled
policy: "read_only_for_unknown" # block_all | read_only_for_unknown | allow_all
allow_list:
- vendor_id: "0x0781" # SanDisk
serial: "*"
- vendor_id: "0x13FE" # Kingston
serial: "ABC123*"
web_filter:
state: enabled
categories_blocked:
- "malware_distribution"
- "phishing"
- "cryptomining_scripts"
- "adult_content"
allow_list:
- domain: "*.company-internal.io"
- domain: "*.github.com"
- ip_range: "10.0.0.0/8"
logging:
verbose: true
retention_days: 90
local_path: "/var/log/k7security"
forward_to:
- type: "syslog"
server: "10.0.1.50:514"
protocol: "tcp"
- type: "elasticsearch"
endpoint: "https://logs.company.io:9200"
index_pattern: "k7-security-%{+YYYY.MM.dd}"
notifications:
email:
enabled: true
smtp_server: "smtp.company.io:587"
recipients:
- "secops@company.io"
severity_threshold: "medium" # low | medium | high | critical
webhook:
enabled: true
url: "${SLACK_WEBHOOK_URL}" # Use environment variable
headers:
Content-Type: "application/json"
retry_attempts: 3
AI Integration Configuration
ai_integration:
incident_analysis:
enabled: true
provider: "openai" # openai | claude | azure-openai
endpoint: "https://api.openai.com/v1/chat/completions"
api_key_env: "OPENAI_API_KEY" # Reference to environment variable
model: "gpt-4-turbo"
max_tokens: 2000
temperature: 0.3
timeout_seconds: 30
# Prompt template for incident analysis
system_prompt: |
You are a security analyst. Analyze the following threat event and provide:
1. Threat classification
2. Severity assessment (Low/Medium/High/Critical)
3. Recommended containment actions
4. Investigation steps
automated_response:
enabled: false # Careful with auto-actions
provider: "claude"
endpoint: "https://api.anthropic.com/v1/messages"
api_key_env: "ANTHROPIC_API_KEY"
model: "claude-3-opus-20240229"
max_tokens: 1500
# Only auto-respond to specific threat types
auto_execute_for:
- "known_malware"
- "ransomware_behavior"
# Actions the AI can suggest
allowed_actions:
- "terminate_process"
- "block_network"
- "quarantine_file"
# Never allow "delete_file" or "disable_protection"
CLI Console Commands
Apply Configuration Profile
# Apply profile to local system
k7-console --apply-profile profiles/enterprise_balanced.yaml \
--target localhost \
--log-level verbose
# Apply to remote endpoint
k7-console --apply-profile profiles/enterprise_balanced.yaml \
--target 192.168.1.100 \
--auth-file certs/admin_credentials.pem \
--timeout 120
# Dry-run mode (validate without applying)
k7-console --apply-profile profiles/strict_isolation.yaml \
--target 192.168.1.100 \
--dry-run
Query Protection Status
# Check status of single endpoint
k7-console --status --target 192.168.1.100
# Query multiple endpoints
k7-console --status --target-list endpoints.txt \
--output json > status_report.json
# Check specific protection layer
k7-console --query memory_scanner \
--target 192.168.1.100
Incident Investigation
# Retrieve recent threat events
k7-console --incidents --since "2026-03-01" \
--target 192.168.1.100 \
--severity high
# Export incident details with AI analysis
k7-console --incidents --incident-id "INC-20260312-001" \
--analyze-with-ai \
--output detailed_report.json
# Query quarantined items
k7-console --quarantine list \
--target 192.168.1.100
Policy Management
# List active policies
k7-console --list-profiles --target 192.168.1.100
# Revert to previous configuration
k7-console --rollback --version previous \
--target 192.168.1.100
# Export current configuration
k7-console --export-config \
--target 192.168.1.100 \
--output current_config.yaml
Common Configuration Patterns
Developer Workstation Profile
profile_name: "Developer Permissive"
version: "16.0.1195"
enforcement_level: "adaptive"
protection_layers:
executable_guard:
state: enabled
action_on_threat: "alert" # Don't block, just notify
exceptions:
- path: "C:\\Users\\*\\AppData\\Local\\Programs\\*"
- path: "/usr/local/bin/*"
- path: "${HOME}/.cargo/bin/*"
- path: "${HOME}/go/bin/*"
memory_scanner:
state: enabled
sensitivity: "medium"
scan_interval_seconds: 600
exclusions:
- process: "node.exe"
- process: "python.exe"
- process: "docker"
- process: "code.exe" # VS Code
usb_filter:
state: enabled
policy: "allow_all" # Developers need USB access
log_all_activity: true
web_filter:
state: enabled
categories_blocked:
- "malware_distribution"
- "phishing"
# Allow access to developer resources
allow_list:
- domain: "*.github.com"
- domain: "*.npmjs.com"
- domain: "*.pypi.org"
- domain: "*.stackoverflow.com"
High-Security Kiosk Profile
profile_name: "Kiosk Lockdown"
version: "16.0.1195"
enforcement_level: "strict"
protection_layers:
executable_guard:
state: enabled
action_on_threat: "block"
# Whitelist approach: only allow specific executables
allow_list_mode: true
allowed_executables:
- path: "C:\\Program Files\\KioskApp\\kiosk.exe"
hash: "a3f5e8d9c1b2e4f6a8b0c2d4e6f8a0b2"
- path: "C:\\Windows\\explorer.exe"
hash: "verified_microsoft_signature"
memory_scanner:
state: enabled
sensitivity: "high"
scan_interval_seconds: 60
usb_filter:
state: enabled
policy: "block_all" # No USB devices allowed
web_filter:
state: enabled
# Whitelist approach: only allow specific domains
allow_list_mode: true
allowed_domains:
- "company-portal.io"
block_all_others: true
# Disable all notifications to kiosk users
notifications:
email:
enabled: false
webhook:
enabled: false
Server Infrastructure Profile
profile_name: "Linux Server Production"
version: "16.0.1195"
enforcement_level: "strict"
protection_layers:
executable_guard:
state: enabled
action_on_threat: "block"
exceptions:
- path: "/usr/sbin/*"
- path: "/usr/bin/*"
- path: "/opt/application/bin/*"
monitor_sudo_activity: true
memory_scanner:
state: enabled
sensitivity: "high"
scan_interval_seconds: 120
exclusions:
- process: "postgres"
- process: "mysqld"
- process: "redis-server"
usb_filter:
state: enabled
policy: "block_all" # No USB on servers
web_filter:
state: disabled # Server outbound filtering handled by firewall
ai_integration:
incident_analysis:
enabled: true
provider: "openai"
api_key_env: "OPENAI_API_KEY"
model: "gpt-4-turbo"
# Auto-analyze all incidents on production servers
auto_analyze_threshold: "low"
logging:
verbose: true
retention_days: 365 # Compliance requirement
forward_to:
- type: "syslog"
server: "siem.company.io:514"
protocol: "tcp"
tls: true
notifications:
webhook:
enabled: true
url: "${PAGERDUTY_WEBHOOK_URL}"
severity_threshold: "medium"
AI Integration Implementation
Setting Up OpenAI Integration
# Set API key as environment variable
export OPENAI_API_KEY="sk-proj-..."
# Update profile to enable AI analysis
cat > profiles/ai_enabled.yaml <<EOF
ai_integration:
incident_analysis:
enabled: true
provider: "openai"
api_key_env: "OPENAI_API_KEY"
model: "gpt-4-turbo"
max_tokens: 2000
temperature: 0.3
system_prompt: |
Analyze this security incident and provide:
1. Threat type and MITRE ATT&CK mapping
2. Severity (Low/Medium/High/Critical)
3. Immediate containment steps
4. Root cause hypothesis
5. Recommended investigation queries
EOF
# Apply configuration
k7-console --apply-profile profiles/ai_enabled.yaml --target localhost
Claude Integration for Advanced Analysis
# Set Claude API key
export ANTHROPIC_API_KEY="sk-ant-..."
# Configure Claude for deeper analysis
cat > profiles/claude_analysis.yaml <<EOF
ai_integration:
incident_analysis:
enabled: true
provider: "claude"
api_key_env: "ANTHROPIC_API_KEY"
model: "claude-3-opus-20240229"
max_tokens: 4000
# Claude is good at chain-of-thought reasoning
system_prompt: |
You are an expert incident responder. For each threat event:
<analysis_steps>
1. Review the event metadata and extract key indicators
2. Classify the threat using MITRE ATT&CK framework
3. Assess potential impact and lateral movement risk
4. Recommend containment actions prioritized by urgency
5. Suggest forensic artifacts to preserve
</analysis_steps>
Provide structured output in JSON format.
EOF
Example AI Analysis Response
When an incident occurs, the AI integration produces:
{
"incident_id": "INC-20260312-001",
"timestamp": "2026-03-12T14:23:05Z",
"ai_analysis": {
"provider": "openai",
"model": "gpt-4-turbo",
"threat_classification": {
"type": "Credential Dumping",
"mitre_technique": "T1003.001",
"confidence": 0.92
},
"severity": "Critical",
"summary": "Detected LSASS memory access pattern consistent with Mimikatz or similar credential dumping tool. Process 'powershell.exe' (PID 4532) accessed LSASS memory regions typically used for credential storage.",
"containment_actions": [
{
"priority": 1,
"action": "terminate_process",
"target": "powershell.exe (PID 4532)",
"reason": "Active credential dumping in progress"
},
{
"priority": 2,
"action": "block_network",
"target": "192.168.1.100",
"reason": "Prevent exfiltration of dumped credentials"
},
{
"priority": 3,
"action": "force_password_reset",
"target": "all_users_on_host",
"reason": "Assume credential compromise"
}
],
"investigation_steps": [
"Review PowerShell command history in event logs",
"Check for recently created scheduled tasks",
"Analyze network connections from compromised host",
"Search for suspicious file modifications in past 24 hours"
]
}
}
Multi-Platform Deployment
Deploying to Mixed Environment
#!/bin/bash
# deploy_k7_fleet.sh - Deploy K7 to heterogeneous environment
# Windows endpoints
for host in $(cat windows_hosts.txt); do
k7-console --apply-profile profiles/windows_workstation.yaml \
--target "$host" \
--auth-file certs/windows_admin.pem \
--log-level info &
done
# macOS endpoints
for host in $(cat macos_hosts.txt); do
k7-console --apply-profile profiles/macos_workstation.yaml \
--target "$host" \
--auth-file certs/macos_admin.pem \
--log-level info &
done
# Linux servers
for host in $(cat linux_servers.txt); do
k7-console --apply-profile profiles/linux_server.yaml \
--target "$host" \
--auth-file certs/linux_admin.pem \
--log-level info &
done
wait
echo "Deployment complete. Check logs for errors."
Platform-Specific Configuration
# profiles/windows_workstation.yaml
profile_name: "Windows Enterprise"
version: "16.0.1195"
protection_layers:
executable_guard:
state: enabled
windows_specific:
monitor_registry_changes: true
protect_critical_paths:
- "HKLM\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Run"
- "HKLM\\SYSTEM\\CurrentControlSet\\Services"
block_unsigned_drivers: true
---
# profiles/macos_workstation.yaml
profile_name: "macOS Enterprise"
version: "16.0.1195"
protection_layers:
executable_guard:
state: enabled
macos_specific:
require_notarization: true
monitor_launch_agents: true
protect_paths:
- "~/Library/LaunchAgents"
- "/Library/LaunchDaemons"
gatekeeper_enforcement: "strict"
---
# profiles/linux_server.yaml
profile_name: "Linux Production"
version: "16.0.1195"
protection_layers:
executable_guard:
state: enabled
linux_specific:
monitor_systemd_units: true
monitor_cron_jobs: true
selinux_integration: true
protect_paths:
- "/etc/systemd/system"
- "/var/spool/cron"
- "/etc/cron.d"
Troubleshooting
Connection Issues
# Test connectivity to endpoint
k7-console --test-connection --target 192.168.1.100
# Verify authentication
k7-console --verify-auth \
--target 192.168.1.100 \
--auth-file certs/admin_credentials.pem
# Check firewall rules (default port 4451)
telnet 192.168.1.100 4451
Profile Application Failures
# Validate YAML syntax before applying
k7-console --validate-profile profiles/new_config.yaml
# Apply with verbose logging
k7-console --apply-profile profiles/new_config.yaml \
--target 192.168.1.100 \
--log-level verbose \
--debug-output /tmp/k7_debug.log
# Check compatibility with target platform
k7-console --check-compatibility \
--profile profiles/new_config.yaml \
--target 192.168.1.100
AI Integration Issues
# Test API connectivity
curl -X POST https://api.openai.com/v1/chat/completions \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-4-turbo",
"messages": [{"role": "user", "content": "test"}],
"max_tokens": 10
}'
# Verify environment variable is set
k7-console --check-env OPENAI_API_KEY
# Test AI analysis manually
k7-console --test-ai-analysis \
--provider openai \
--incident-id INC-20260312-001
Performance Issues
# Check resource usage on endpoint
k7-console --resource-stats --target 192.168.1.100
# Adjust scan intervals to reduce CPU load
cat > profiles/low_overhead.yaml <<EOF
protection_layers:
memory_scanner:
scan_interval_seconds: 900 # Increase from 300 to 900
sensitivity: "medium" # Reduce from high to medium
EOF
# Disable verbose logging
cat > profiles/minimal_logging.yaml <<EOF
logging:
verbose: false
retention_days: 30
local_only: true
EOF
Quarantine Management
# List quarantined items
k7-console --quarantine list --target 192.168.1.100
# Restore false positive
k7-console --quarantine restore \
--item-id "QUAR-20260312-001" \
--target 192.168.1.100 \
--add-exception
# Permanently delete quarantined item
k7-console --quarantine delete \
--item-id "QUAR-20260312-002" \
--target 192.168.1.100 \
--confirm
Webhook Integration Examples
Slack Notifications
notifications:
webhook:
enabled: true
url: "${SLACK_WEBHOOK_URL}"
payload_template: |
{
"text": "K7 Security Alert",
"blocks": [
{
"type": "section",
"text": {
"type": "mrkdwn",
"text": "*Incident:* {{incident_id}}\n*Severity:* {{severity}}\n*Host:* {{hostname}}\n*Threat:* {{threat_type}}"
}
},
{
"type": "section",
"text": {
"type": "mrkdwn",
"text": "{{ai_analysis_summary}}"
}
}
]
}
PagerDuty Integration
notifications:
webhook:
enabled: true
url: "https://events.pagerduty.com/v2/enqueue"
headers:
Content-Type: "application/json"
payload_template: |
{
"routing_key": "${PAGERDUTY_INTEGRATION_KEY}",
"event_action": "trigger",
"payload": {
"summary": "K7 Security Incident: {{threat_type}}",
"severity": "{{severity}}",
"source": "{{hostname}}",
"custom_details": {
"incident_id": "{{incident_id}}",
"ai_analysis": "{{ai_analysis_summary}}"
}
}
}
Best Practices
- Always use environment variables for API keys - Never commit credentials to configuration files
- Test profiles in dry-run mode first - Validate before applying to production
- Enable AI analysis for high-severity alerts only - Avoid API rate limits and costs
- Use platform-specific profiles - Don't apply Windows-specific settings to Linux hosts
- Implement graduated rollout - Apply new profiles to test group before fleet-wide deployment
- Monitor AI response quality - Periodically review AI-generated recommendations for accuracy
- Maintain exception lists carefully - Over-broad exceptions defeat the purpose of protection
- Log aggregation is critical - Forward logs to SIEM for correlation and long-term retention
Security Notice: This skill documents configuration patterns from an unofficial repository. Always verify software authenticity, obtain proper licenses, and consult official vendor documentation for production deployments.