HackGPT Enterprise: AI Pentesting Platform

HackGPT Enterprise is a cloud-native AI-powered penetration testing platform built for security teams that need AI-assisted assessments, SOC analysis, SIEM integration, automated reporting, and scalable deployment.

The project combines Python, AI model providers, machine learning, Docker, Kubernetes, PostgreSQL, Redis, Celery, and security frameworks into one platform. The current GitHub release is v2026.09.19, which adds dynamic model discovery, additional AI routing options, and several stability improvements.

⚠️ Important: HackGPT is intended for authorized security testing. Only use it against systems you own or systems for which you have explicit permission to test.

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🤖 AI Engine and Multi-Provider Support​

One of HackGPT Enterprise's main features is its multi-provider AI engine.
The project supports multiple AI providers, including:

• OpenAI
• Anthropic
• Google Gemini
• DeepSeek
• GLM/Zhipu
• Ollama for local LLMs
• OpenRouter

The platform is designed to let users switch models at runtime instead of locking an assessment to a single AI provider.
The latest release also adds dynamic model discovery through provider APIs, allowing available models to be queried and registered at runtime.

AI-assisted capabilities include:

• Pattern recognition
• Anomaly detection
• Behavioral analysis
• Vulnerability correlation
• Risk assessment
• Automated reporting
• Security findings analysis

This makes the AI layer useful not only for generating text, but also for organizing security assessment data and helping analysts interpret findings.

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🛡️ SOC Analysis and SIEM Integration​

HackGPT also includes an SOC analysis layer for processing and correlating security events.
The platform provides capabilities for:

• Log parsing and normalization
• IOC extraction
• MITRE ATT&CK technique mapping
• Event grouping
• Duplicate event detection
• Attack timeline reconstruction
• Statistical anomaly detection
• Incident response guidance

SIEM integrations include connectors for platforms such as Splunk, IBM QRadar, Elasticsearch, and customizable webhooks.
This can be useful when a security team wants to connect penetration testing results with broader monitoring and incident-response workflows.

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🏢 Enterprise Security Features​

HackGPT includes several features aimed at enterprise environments.

Authentication and Access Control​

The platform provides:

• Role-based access control
• LDAP/Active Directory integration
• JWT-based authentication
• Session management
• Permission management
• API rate limiting
• Input validation
• Audit logging

Supported roles can be separated between administrators, security leads, senior testers, pentesters, and analysts.

🔐 Data Protection​

The project documentation also lists:

• AES-256-GCM encryption
• TLS 1.3 for data in transit
• JWT tokens
• Secure sessions
• Key rotation
• Audit trails

These features are intended to help teams protect assessment data and maintain visibility into platform activity.

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☁️ Cloud-Native Architecture​

HackGPT is designed around a microservices architecture.
The repository includes components for the application, workers, database, Redis, monitoring, logging, service discovery, and reverse proxy infrastructure.

The architecture can use:

• Docker
• Kubernetes
• Nginx
• Consul
• PostgreSQL
• Redis
• Celery
• Prometheus
• Grafana
• Elasticsearch
• Kibana

The project also documents deployment options for AWS, Azure, and Google Cloud.

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⚡ Performance and Scalability​

For larger assessments, the platform uses distributed processing and caching.
Key components include:

• Celery for background task processing
• Redis for caching and queues
• PostgreSQL for persistent data
• Connection pooling
• Worker pools
• WebSocket-based real-time updates
• Prometheus and Grafana for monitoring

This architecture allows resource-intensive tasks to be distributed across multiple workers rather than running everything inside a single process.

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📊 Reporting and Analytics​

Security testing is only useful when the results can be understood and acted on.
HackGPT provides reporting and analytics capabilities for different audiences.
Supported output formats include:

• HTML
• PDF
• JSON
• XML
• CSV

Reports can include technical findings, risk information, business impact, compliance mappings, and executive summaries.
The monitoring stack can also provide dashboards through Prometheus and Grafana, while Elasticsearch and Kibana can be used for log analysis.

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🚀 How to Install HackGPT Enterprise​

Prerequisites​

The project documentation lists support for:

• Linux distributions such as Ubuntu, Debian, RHEL, and CentOS
• macOS
• Windows through WSL2
• Python 3.8+
• pip and virtual environments
• Docker for containerized deployment
• At least 4 GB RAM
• At least 20 GB disk space

Clone and Install​

Clone the official repository and run the installer:

git clone https://github.com/yashab-cyber/HackGPT.git
cd HackGPT

chmod +x install.sh
./install.sh

cp .env.example .env
nano .env

python3 test_installation.py

The repository contains the installer, enterprise requirements, configuration files, Docker configuration, and installation test script.

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🖥️ Deployment Options​

HackGPT can be started in several different modes.

1. Standalone Mode​

For running the application directly:

source venv/bin/activate
python3 advance_hackgpt.py

2. REST API Mode​

To start the API server:

python3 advance_hackgpt.py --api

The documented local API endpoint is:
http://localhost:8000
The health endpoint is:
http://localhost:8000/api/health

3. Web Dashboard​

To launch the web interface:

python3 advance_hackgpt.py --web

The documented dashboard address is:
http://localhost:8080

4. Full Docker Stack​

For a containerized deployment:

docker-compose up -d

A typical stack can expose services for the API, dashboard, Prometheus, Grafana, and Kibana.

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🔎 AI Model Management​

The current release adds command-line options for working with AI models and providers.
For example, the release documentation shows commands for fetching models, listing available models, and selecting a provider and model.

python advance_hackgpt.py --fetch-models
python advance_hackgpt.py --list-models

A specific model and provider can also be selected:

python advance_hackgpt.py --model gpt-astra --provider openai

The exact models available will depend on the current project version and the providers configured in your environment.

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🧪 Penetration Testing Workflow​

HackGPT organizes its security assessment workflow into six major phases.

Phase 1 - Reconnaissance​

The first phase focuses on gathering information about authorized targets.
Possible activities include:

• OSINT collection
• Asset discovery
• Cloud asset identification
• Threat intelligence correlation
• Multi-source information gathering

Phase 2 - Scanning and Enumeration​

The next phase focuses on identifying exposed services and assets.
The project documentation references tools such as:

• Nmap
• Masscan
• Nuclei
• HTTPx
• Naabu

Use these tools only against authorized targets.

Phase 3 - Vulnerability Assessment​

Findings can then be organized and assessed based on factors such as:

• Severity
• CVSS information
• Business impact
• Exploit availability
• Compliance requirements

The goal is to help security teams prioritize remediation instead of simply producing a long list of vulnerabilities.

Phase 4 - Exploitation and Post-Exploitation​

This phase is intended for controlled security testing.
The documented workflow includes concepts such as:

• Safe-mode exploitation
• Privilege escalation enumeration
• Lateral movement analysis
• Controlled data-exfiltration simulation

These capabilities should only be used inside an explicitly authorized testing scope.

Phase 5 - Reporting​

Assessment results can be turned into technical and executive reports.
This phase can include:

• Vulnerability details
• Risk prioritization
• Business impact
• Compliance gaps
• Executive summaries

Phase 6 - Verification and Retesting​

After remediation, security teams can repeat relevant tests to verify whether vulnerabilities have actually been fixed.
This can include:

• Remediation verification
• Regression testing
• Scheduled retesting
• Delta reporting
• Security trend analysis

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🧩 REST API and Web Interfaces​

HackGPT provides several ways to interact with the platform.

CLI​

The command-line interface can be used for configuration, assessments, reports, user management, compliance, and AI configuration.

REST API​

The documented API includes endpoints for operations such as:

GET /api/health
POST /api/pentest/start
GET /api/sessions
GET /api/reports/{id}
POST /api/users
GET /api/compliance

Web Dashboard​

The web dashboard is designed for tasks such as:

• Monitoring assessments
• Managing vulnerabilities
• Viewing executive summaries
• Managing users and roles
• Reviewing compliance information
• Configuring the platform

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⚙️ Configuration​

HackGPT uses configuration files and environment variables to control its services.
A typical configuration can contain application, database, AI, security, LDAP, compliance, and cloud settings.

For example:

[app]
debug = false
environment = production
max_sessions = 100

[database]
url = postgresql://hackgpt:CHANGE_ME@localhost:5432/hackgpt
pool_size = 20
backup_enabled = true

[ai]
default_model = gpt-5
default_provider = openai
openai_api_key = your_key_here
anthropic_api_key = your_key_here
google_api_key = your_key_here
enable_local_fallback = true
confidence_threshold = 0.8

[security]
secret_key = CHANGE_ME
jwt_algorithm = HS256
rate_limit_enabled = true

⚠️ Security note: Never publish real API keys, passwords, JWT secrets, database credentials, or cloud credentials in configuration files, screenshots, Git repositories, or support requests.

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🧰 Environment Variables​

The platform also supports environment variables for external services.
A simplified example is:

DATABASE_URL=postgresql://hackgpt:CHANGE_ME@localhost:5432/hackgpt
REDIS_URL=redis://localhost:6379/0

HACKGPT_MODEL=gpt-5
OPENAI_API_KEY=your_openai_api_key
ANTHROPIC_API_KEY=your_anthropic_api_key
GOOGLE_API_KEY=your_google_api_key
DEEPSEEK_API_KEY=your_deepseek_api_key
GLM_API_KEY=your_glm_api_key
OPENROUTER_API_KEY=your_openrouter_api_key

LOCAL_LLM_ENDPOINT=http://localhost:11434

SECRET_KEY=CHANGE_ME
JWT_SECRET_KEY=CHANGE_ME
LDAP_SERVER=ldaps://your-ldap.com:636

Replace placeholder values with credentials appropriate for your environment.

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☸️ Kubernetes Deployment​

For Kubernetes environments, the repository includes Kubernetes deployment files.
A basic deployment can be applied with:

kubectl apply -f k8s/

Worker capacity can then be adjusted according to the workload:

kubectl scale deployment hackgpt-worker --replicas=10

Before scaling workers in production, make sure the database, Redis instance, CPU, memory, and other dependencies can handle the additional workload.

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📈 Monitoring with Prometheus and Grafana​

HackGPT can integrate with Prometheus and Grafana for operational monitoring.
Useful metrics include:

• CPU usage
• Memory usage
• Disk usage
• Network activity
• Request rates
• Response times
• Application errors
• Vulnerability counts
• Risk scores
• Remediation rates

This gives security and infrastructure teams a way to monitor both the platform and assessment workload.

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🔧 Troubleshooting​

PostgreSQL Connection Problems​

Check whether PostgreSQL is running:

systemctl status postgresql
docker logs hackgpt-database

You can also test the application database connection:

python3 -c "from database import get_db_manager; print(get_db_manager().test_connection())"

Redis Problems​

Check Redis connectivity:

redis-cli ping
docker logs hackgpt-redis

If you need to clear a development Redis instance, make sure you understand the impact before using FLUSHALL, because it removes all keys from the selected Redis database.

AI Provider Problems​

To inspect the available models:

python3 -c "from ai_engine import list_all_models; [print(m.display_name, m.provider.value) for m in list_all_models()]"

For provider availability:

python3 -c "from ai_engine.providers import ProviderFactory; print(ProviderFactory.get_available_providers())"

For a local Ollama installation:

ollama list

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🧪 Testing the Installation​

HackGPT includes separate test directories for different testing levels.
Run unit tests with:

pytest tests/unit/

Integration tests:

pytest tests/integration/

End-to-end tests:

pytest tests/e2e/

The latest release also documents installation verification with:

python test_installation.py

The repository includes dedicated test and development files, including conftest.py, test_installation.py, and development requirements.

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📦 Docker Swarm and Cloud Deployment​

For Docker Swarm:

docker swarm init
docker stack deploy -c docker-compose.yml hackgpt

For Kubernetes:

kubectl create namespace hackgpt
kubectl apply -f k8s/

The project also documents cloud deployment examples for AWS, Azure, and Google Cloud.

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🛡️ Security and Responsible Use​

HackGPT is a security testing platform, so authorization is critical.
Before starting an assessment:

1. Confirm that you own the target or have written permission.
2. Define the allowed IP addresses, domains, applications, and APIs.
3. Establish the testing window.
4. Define prohibited actions.
5. Keep assessment logs and evidence.
6. Report discovered vulnerabilities through the agreed process.
7. Remove or protect sensitive testing data after the engagement.

Never run penetration-testing workflows against random public systems simply because they are reachable.

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📄 License and Project Status​

HackGPT is presented as an open-source cybersecurity project, and the repository includes an MIT license file. The project also contains additional enterprise-related terms and documentation, so review the current repository license and project terms before using it commercially.
The repository currently includes dedicated directories and files for AI, cloud deployment, databases, exploitation, logging, monitoring, performance, reporting, security, tests, Docker, and enterprise requirements.

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❓ Frequently Asked Questions​

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What is HackGPT Enterprise?​

HackGPT Enterprise is an AI-powered penetration testing and security assessment platform that combines AI models, automation, security analysis, reporting, and cloud-native infrastructure.

Does HackGPT support multiple AI providers?​

Yes. The project supports providers including OpenAI, Anthropic, Google, DeepSeek, GLM, Ollama, and OpenRouter. The current release also includes dynamic model discovery capabilities.

Can HackGPT run locally?​

Yes. The project supports local deployment and can use Ollama for local LLM workloads.

Does HackGPT support Docker and Kubernetes?​

Yes. Docker is supported for containerized deployments, while Kubernetes manifests are included for cluster-based deployments.

Can HackGPT integrate with SIEM platforms?​

Yes. The project documents integrations for Splunk, IBM QRadar, Elasticsearch, and customizable webhooks.

Is HackGPT suitable for unauthorized penetration testing?​

No. The project explicitly states that it is intended for authorized security testing. Testing systems without permission can violate laws, contracts, and security policies.

Where can I find the project?​

The official source repository is available on GitHub: HackGPT on GitHub

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🎯 Final Takeaway​

HackGPT Enterprise brings AI-assisted security testing, SOC analysis, SIEM integration, automated reporting, and cloud-native deployment into a single cybersecurity platform.

Its architecture is particularly focused on teams that need more than a simple command-line pentesting tool: the project combines AI model management, distributed workers, security analytics, monitoring, compliance reporting, and multiple deployment options.

For anyone evaluating HackGPT, the most important step is to check the current release documentation and repository before deployment because model catalogs, integrations, commands, and project capabilities can change between releases.