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Introduction:
The fusion of large language models with offensive security tooling has given birth to autonomous AI shells capable of adapting their reasoning on the fly. SecorizonAI 1.0, a terminal-native agent released by Secorizon, introduces context/model switching, integrated MCP client, web search, and a “think++” mode – all designed to accelerate professional pentesting workflows without sacrificing precision.
Learning Objectives:
- Install and configure SecorizonAI on Linux and Windows environments for AI‑augmented pentesting.
- Execute on‑the‑fly model switching and think++ mode to automate reconnaissance and exploitation.
- Integrate web search, MCP client, and custom methodologies to test API security and cloud hardening.
You Should Know:
1. Installing and Configuring SecorizonAI on Linux/Windows
SecorizonAI runs as a terminal‑native shell, requiring Python 3.10+ and Git. Below are verified steps for both platforms.
Linux (Debian/Ubuntu):
sudo apt update && sudo apt install -y python3 python3-pip git git clone https://github.com/secorizon/SecorizonAI.git cd SecorizonAI python3 -m venv venv source venv/bin/activate pip install -r requirements.txt python secorizonai.py --setup
Windows (PowerShell as Admin):
winget install -e --id Python.Python.3.10 git clone https://github.com/secorizon/SecorizonAI.git cd SecorizonAI python -m venv venv .\venv\Scripts\Activate.ps1 pip install -r requirements.txt python secorizonai.py --setup
The setup wizard prompts for API keys (OpenAI, Anthropic, or local LLM endpoints) and default methodology (e.g., OWASP, PTES). Test the installation:
secorizon "ping -c 4 8.8.8.8" --dry-run
2. Mastering On‑the‑Fly Model Switching for Adaptive Reconnaissance
SecorizonAI’s core innovation is switching between models per task – use a lightweight model for port scanning, then a reasoning‑heavy model for exploit chaining.
Step‑by‑step model switching:
Set default model
secorizon --set-model gpt-4o-mini
Switch temporarily for a single command
secorizon "nmap -sV -p- 192.168.1.0/24" --model llama-3-70b
Interactive mode with runtime switching
secorizon --interactive
<blockquote>
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use model claude-3-opus
run custom_scan.py --target 10.0.0.5
use model gpt-4o
explain last_output
To view available models and context state:
secorizon --list-models secorizon --context-stats
This allows a pentester to allocate expensive GPU models only for complex reasoning (e.g., SQLi blind inference) while routine enumeration runs on cheap, fast models.
- Leveraging MCP Client and Web Search for OSINT
SecorizonAI includes a built‑in Model Context Protocol (MCP) client, enabling it to query external tools, APIs, and databases. Combined with web search, it automates passive reconnaissance.
Enable MCP and web search:
secorizon --config mcp_servers.yaml
Example `mcp_servers.yaml`:
servers:
shodan:
type: api
endpoint: https://api.shodan.io/shodan/host/{ip}
key: env:SHODAN_API_KEY
cve:
type: mcp
command: cve_search -json
OSINT automation workflow:
secorizon "search 'target.com subdomains' via web" secorizon "mcp shodan 8.8.8.8" --save results/shodan.json secorizon "mcp cve --filter Apache"
The agent correlates web results with MCP data and suggests attack vectors. For Windows users, ensure `curl` and `jq` are installed for parsing.
4. Running Automated Vulnerability Exploitation with Think++ Mode
Think++ mode forces the agent to recursively reason about a finding, generating exploit code, verifying blind spots, and self‑correcting. This is akin to an autonomous red teamer.
Start think++ on a discovered vulnerability:
secorizon "vuln found: CVE-2024-6387 in sshd" --think++
The shell outputs a chain‑of‑thought, then proposes a proof‑of‑concept (PoC). Example output snippet:
[bash] This is a signal handler race condition in OpenSSH. Remote code execution is possible if glibc > 2.34. [bash] Generate Python exploit using async I/O to race the signal handler. [Self‑check] Does the target have SELinux? Run `getenforce` first.
Execute generated exploit:
secorizon --run-exploit generated_exploit.py --target 192.168.1.100 --port 22
For Windows targets, use PowerShell equivalents:
secorizon "CVE-2024-26234" --think++ --platform windows
Think++ logs every reasoning step to `~/.secorizon/logs` for later auditing.
5. Integrating SecorizonAI with Cloud Hardening Workflows (AWS/Azure)
SecorizonAI can assess cloud misconfigurations by loading cloud‑specific methodologies (e.g., CIS Benchmarks, AWS Well‑Architected). It integrates with AWS CLI and Azure CLI.
Setup cloud provider authentication:
aws configure set access keys secorizon --cloud-provider aws
Enumerate IAM misconfigurations:
secorizon "list all IAM users with unused access keys >90d" --methodology cis_1.4
Automated remediation suggestions:
secorizon "audit s3 bucket policies for public read" --fix-dry-run
Example output:
[bash] Bucket 'logs-target' allows GetObject from '' [bash] Add bucket policy denying non‑VPC requests [bash] aws s3api put-bucket-policy --bucket logs-target --policy file://policy.json
For Azure:
az login secorizon --cloud-provider azure secorizon "find NSGs allowing 0.0.0.0/0 on RDP"
6. API Security Testing Using Custom Methodologies
SecorizonAI supports custom YAML‑based methodologies for API testing (GraphQL, REST, gRPC). It can parse OpenAPI specs and generate attack sequences.
Load an API methodology:
secorizon --load-methodology api_heavy.yaml
Example `api_heavy.yaml` snippet:
name: OWASP API Top 10
steps:
- id: BOLA
tool: custom_bola.py
params: {auth: JWT, id_param: user_id}
- id: Excessive Data Exposure
tool: graphql_introspection
filter: return size > 10000
Automated GraphQL injection:
secorizon "test https://api.target.com/graphql --methodology graphql_attacks"
The agent runs each step, captures responses, and correlates with model context. For manual verification, it prints curl commands:
secorizon "show last curl --redact-headers"
7. Mitigation and Defensive Tactics Against AI‑Powered Attacks
Understanding SecorizonAI’s capabilities helps defenders harden environments. Implement these countermeasures:
Detect AI‑generated scanning patterns:
Linux: rate‑limit SSHD and monitor for model‑specific User‑Agents sudo iptables -A INPUT -p tcp --dport 22 -m recent --update --seconds 60 --hitcount 4 -j DROP sudo tail -f /var/log/auth.log | grep "secorizon"
Windows: Enable PowerShell logging and block unrecognized AI shell signatures
Set-ItemProperty -Path "HKLM:\SOFTWARE\Policies\Microsoft\Windows\PowerShell\ScriptBlockLogging" -Name EnableScriptBlockLogging -Value 1 New-NetFirewallRule -DisplayName "Block Secorizon AI" -Direction Inbound -Protocol TCP -RemotePort 5000-6000 -Action Block
Deploy AI‑aware WAF rules (ModSecurity):
SecRule REQUEST_HEADERS:User-Agent "secorizon|llama|gpt" "id:1001,deny,status:403,msg:'AI Pentest Tool Detected'"
Harden MCP endpoints: Require mutual TLS and rotate API keys every 24h. Use `fail2ban` for repeated methodology fingerprints.
What Undercode Say:
- Key Takeaway 1: SecorizonAI democratizes advanced pentesting by lowering the cognitive load of context switching, but it requires experienced oversight – think++ mode can produce dangerous exploits if left unmonitored.
- Key Takeaway 2: The integration of MCP and web search turns the AI shell into an OSINT powerhouse; defenders must assume attackers will use similar autonomous agents for reconnaissance at machine speed.
Analysis: The release of SecorizonAI 1.0 signals a shift from static pentesting scripts to adaptive, reasoning‑driven agents. Unlike traditional frameworks (Metasploit, Burp), this tool dynamically selects models based on task complexity – a lightweight model for enumeration, a frontier model for zero‑day pattern matching. The “think++” recursive reasoning mimics human iterative testing, reducing false positives and generating context‑aware PoCs. For blue teams, this means attack surface expands beyond signature‑based detections; anomaly detection must now model behavioral chains, not just individual commands. The inclusion of customizable methodologies makes it suitable for continuous red teaming, while the MCP client allows hooking into private threat intel feeds. However, the reliance on cloud API keys introduces supply chain risks – a compromised key could leak sensitive target data. Organizations should sandbox SecorizonAI in isolated VMs and rotate credentials per engagement.
Prediction:
Within 12–18 months, autonomous AI shells like SecorizonAI will become standard in both offensive and defensive toolchains. We will see “AI red team as a service” platforms that combine multiple specialized models (recon, exploit, privilege escalation) orchestrated by a meta‑planner. Regulators may require disclosure of AI‑assisted pentesting in compliance audits (PCI DSS, SOC 2). Defenders will respond with adversarial ML – poisoning methodology YAML files or feeding fake vulnerabilities to waste attacker compute. The next evolution will be agent‑versus‑agent duels in continuous purple team exercises, with each side adapting models in real time. For cybersecurity professionals, mastering prompt engineering for penetration tests will become as valuable as knowing Metasploit modules.
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Reported By: Laurentgaffie Secorizonai – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
