offsec-ai 2.4.0

Offensive-security toolkit: port scanning, L7/WAF detection, mTLS, certificate analysis, OWASP Top 10, AI/LLM OWASP Top 10 black-box probing, MCP endpoint security scanning, and OpenClaw gateway security assessment

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  Offensive-Security Toolkit · AI/LLM · MCP · Red-Team

Offensive-security toolkit for authorized red-team engagements.

offsec-ai is a Python library and CLI that combines classic network reconnaissance with modern AI/LLM security testing. It probes live AI/LLM endpoints for the OWASP LLM Top 10, scans and actively attacks Model Context Protocol (MCP) servers for known CVEs, and performs full-stack infrastructure security assessments.

Legal Notice: Active attack features (mcp-attack, openclaw-attack, k8s-attack, deep mode) require the --i-have-authorization flag. Only use against systems you own or have explicit written permission to test.

---

Features

New in v2.3.0 — Kubernetes Cluster Security

Feature	Description
☸️ Kubernetes Scanner	Five-phase black-box scan of exposed K8s components: kube-apiserver (6443/8080), kubelet (10250/10255), etcd (2379), scheduler, controller-manager, cAdvisor, dashboard
📋 OWASP K8s Top 10 (2025)	Findings mapped to K01–K10; 10+ advisories (K8S-ADV-###) + real CVEs (CVE-2018-1002105, CVE-2019-11253, CVE-2020-8558, CVE-2021-25741, CVE-2022-3294)
🤖 Optional LLM Judge	LLMJudge triages ambiguous findings and generates remediation advice; supports OpenAI, Anthropic, and Google Gemini; rule-based fallback when no API key is set
⚔️ Kubernetes Attacker	Authorized red-team probes: anonymous API reads, kubelet /exec command execution, Secret extraction, SelfSubjectAccessReview privilege audit, etcd key dump, cloud metadata SSRF (K08)

New in v2.1.0 — OpenClaw Gateway Security

Feature	Description
🦞 OpenClaw Scanner	Six-phase passive assessment of OpenClaw AI-gateway deployments: fingerprint (including HTML-based detection for OpenClaw 2026.x), endpoint enumeration, auth posture, config review, CVE/misconfiguration matching, optional LLM triage
🔟 10 Advisory Checks	OCL-ADV-001 through OCL-ADV-010 — from unauthenticated REST/WebSocket access to insecure sandbox modes, DM policy exposure, and API-key leakage via config endpoint
⚔️ OpenClaw Attacker	Authorized active exploitation: prompt injection, SSRF via webhook, session history dump, WebSocket message injection; optional --llm-judge for attack-path narrative

New in v2.0.0 — AI / LLM Security

Feature	Description
🤖 AI OWASP Top 10 Scanner	Black-box probing of live LLM/chat API endpoints for all 10 OWASP LLM categories
🔬 Rule-based + LLM Judge	Pattern-based detection + optional LLM judge (OpenAI / Anthropic / Gemini) via [ai] extra
🔌 MCP Security Scanner	Enumerate tools/resources/prompts, detect CVEs, check auth posture (HTTP, SSE, stdio)
⚔️ MCP Attacker	Authorized active testing: auth bypass, path traversal, tool injection, command injection; optional --llm-judge for attack-path narrative
🛡️ Authorization Gating	MCPAttacker(authorized=False) raises AuthorizationRequired; --i-have-authorization flag required at CLI

Infrastructure Security

Feature	Description
🔍 Port Scanning	Async concurrent scanning of well-known and custom ports
🌐 L7 Protection Detection	Identify WAF/CDN services (Cloudflare, AWS WAF, Azure, F5, Akamai, etc.)
🔐 mTLS Checker	Test mutual TLS support, client certificate requirements, handshake validation
🔒 Certificate Analysis	Full chain analysis, trust path, issuer identification, expiry, missing intermediates
🏛️ Hybrid Identity Detection	Azure AD / ADFS federation endpoint discovery (same method as Azure Portal)
🕵️ OWASP Top 10 Web Scanner	Web OWASP Top 10 2021 & 2025 with safe/deep modes, PDF/JSON/CSV reports
🛡️ Security Headers	Grade HTTP headers (HSTS, CSP, X-Frame-Options, Referrer-Policy, etc.)
📄 Multi-format Reporting	Export to PDF, JSON, CSV with tech-specific remediation (Nginx, Apache, IIS, Cloudflare)

---

Installation

# Core toolkit
pip install offsec-ai

# With optional LLM judge (OpenAI / Anthropic / Gemini)
pip install "offsec-ai[ai]"

From Source

git clone https://github.com/htunn/offsec-ai.git
cd offsec-ai
pip install -e ".[dev]"

Docker

docker run --rm htunnthuthu/offsec-ai:latest --help

---

Quick Start

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  Offensive-Security Toolkit · AI/LLM · MCP · Red-Team

CLI

# AI / LLM security
offsec-ai ai-owasp-scan https://api.example.com/v1/chat/completions
offsec-ai mcp-scan https://mcp.example.com/mcp
offsec-ai mcp-attack https://mcp.example.com/mcp --i-have-authorization

# OpenClaw gateway security
offsec-ai openclaw-scan 192.168.1.10
offsec-ai openclaw-scan gateway.example.com --port 18789 --tls
offsec-ai openclaw-scan 192.168.1.10 --llm-judge
offsec-ai openclaw-attack 192.168.1.10 --i-have-authorization --mode deep
offsec-ai openclaw-attack 192.168.1.10 --i-have-authorization --mode deep --llm-judge

# Kubernetes cluster security
offsec-ai k8s-scan 192.168.1.100
offsec-ai k8s-scan k8s.example.com --port 6443 --port 10250 --llm-judge
# kubectl proxy makes the API server reachable on plain HTTP locally:
offsec-ai k8s-scan 127.0.0.1 --port 8001 --llm-judge
offsec-ai k8s-attack 192.168.1.100 --i-have-authorization --mode deep
offsec-ai k8s-attack 127.0.0.1 --port 8001 --i-have-authorization --llm-judge

# Infrastructure
offsec-ai scan example.com
offsec-ai l7-check example.com
offsec-ai cert-check example.com
offsec-ai owasp-scan example.com
offsec-ai hybrid-identity example.com
offsec-ai mtls-check example.com

Python API

import asyncio
from offsec_ai import LLMOwaspScanner, MCPScanner, MCPAttacker, AuthorizationRequired

async def main():
    # AI OWASP scan
    scanner = LLMOwaspScanner("https://api.example.com/v1/chat/completions")
    result = await scanner.scan()
    print(f"Grade: {result.overall_grade}  Score: {result.total_score}")
    for cat_id, cat in result.categories.items():
        if cat.findings:
            print(f"  {cat_id}: {len(cat.findings)} finding(s) — grade {cat.grade}")

    # MCP scan
    mcp = MCPScanner("https://mcp.example.com/mcp")
    mcp_result = await mcp.scan()
    print(f"MCP vulnerabilities: {len(mcp_result.vulnerabilities)}")

    # MCP attack (requires explicit authorization)
    try:
        attacker = MCPAttacker(authorized=True)   # must be True
        report = await attacker.attack(
            target="https://mcp.example.com/mcp",
            transport="http",
            mode="safe",
        )
        print(f"Attacks run: {report.attacks_run}, triggered: {len(report.triggered_results)}")
    except AuthorizationRequired:
        print("Provide authorized=True to unlock attack mode")

asyncio.run(main())

---

AI OWASP Top 10 Scanner

Probes a live LLM/chat endpoint for the OWASP LLM Top 10. Designed for black-box testing — no model access required.

Categories Covered

ID	Category	Safe Mode	Deep Mode
LLM01	Prompt Injection	✅	✅
LLM02	Sensitive Information Disclosure	✅	✅
LLM03	Supply Chain	🚫	🚫
LLM04	Data & Model Poisoning	🚫	🚫
LLM05	Improper Output Handling (XSS/SQLi)	✅	✅
LLM06	Excessive Agency	✅	✅
LLM07	System Prompt Leakage	✅	✅
LLM08	Vector & Embedding Weaknesses	🚫	🚫
LLM09	Misinformation	✅	✅
LLM10	Unbounded Consumption	✅	✅

🚫 = Not externally testable via black-box probing

CLI Usage

# Basic scan (safe mode, OpenAI-compatible endpoint)
offsec-ai ai-owasp-scan https://api.example.com/v1/chat/completions

# Deep mode with all probes
offsec-ai ai-owasp-scan https://api.example.com/v1/chat/completions --mode deep

# Specific categories only
offsec-ai ai-owasp-scan https://api.example.com/v1/chat/completions \
  --categories LLM01,LLM02,LLM07

# Generic/custom API format (non-OpenAI)
offsec-ai ai-owasp-scan https://chat.example.com/api/chat --api-format generic

# With authentication header
offsec-ai ai-owasp-scan https://api.example.com/v1/chat/completions \
  --header "Authorization: Bearer sk-..."

# JSON output
offsec-ai ai-owasp-scan https://api.example.com/v1/chat/completions --output results.json

# Enable LLM judge (requires OPENAI_API_KEY, ANTHROPIC_API_KEY, or GEMINI_API_KEY env var)
offsec-ai ai-owasp-scan https://api.example.com/v1/chat/completions --llm-judge

Python API

import asyncio
from offsec_ai import LLMOwaspScanner, LLMScanMode, LLMJudge

async def main():
    # Optional: enable LLM judge for smarter detection
    judge = LLMJudge.from_env()  # reads OPENAI_API_KEY / ANTHROPIC_API_KEY

    scanner = LLMOwaspScanner(
        endpoint="https://api.example.com/v1/chat/completions",
        mode=LLMScanMode.DEEP,
        categories=["LLM01", "LLM02", "LLM07"],
        api_format="openai",
        headers={"Authorization": "Bearer sk-..."},
        judge=judge,                # None = rule-based only
    )

    result = await scanner.scan()
    print(f"Grade: {result.overall_grade}  ({result.total_score} pts)")

    for cat_id, cat in result.categories.items():
        if cat.findings:
            print(f"\n{cat_id}: {cat.category_name}")
            for finding in cat.findings:
                print(f"  [{finding.severity.value}] {finding.title}")
                print(f"  Evidence: {finding.evidence[:80]}...")

asyncio.run(main())

Severity & Grading

Severity	Points
CRITICAL	15
HIGH	10
MEDIUM	5
LOW	1

Grade: A (0–10), B (11–25), C (26–50), D (51–100), F (>100 or any CRITICAL finding).

LLM Judge (Optional)

Install the [ai] extra and set an API key to enable smarter semantic detection:

pip install "offsec-ai[ai]"
export GEMINI_API_KEY="AIza..."       # Google Gemini  (1st priority)
export ANTHROPIC_API_KEY="sk-ant-..." # or Anthropic   (2nd priority)
export OPENAI_API_KEY="sk-..."        # or OpenAI      (3rd priority)

If multiple keys are set, Gemini is used first, then Anthropic, then OpenAI. Without the extra, detection falls back to rule-based pattern matching.

---

MCP Security Scanner

Scans Model Context Protocol servers for security vulnerabilities. Supports HTTP/SSE transports (remote URL) and stdio transport (local subprocess).

CVEs / Checks Performed

Check	Description
Unauthenticated Exposure	Server accessible without credentials
Tool Poisoning	Malicious instructions hidden in tool descriptions
Path Traversal in Resources	../ patterns in resource URIs
Command Injection	Shell metacharacters in tool params
Secrets in Descriptions	API keys, passwords leaked in tool/resource descriptions
Excessive Agency	Unrestricted file system or network tools
Prompt Injection via Tool Response	LLM instruction injection through tool output
Rug-pull / Tool Shadowing	Tool behavior changed post-trust-establishment

CLI Usage

# Scan HTTP/SSE MCP endpoint
offsec-ai mcp-scan https://mcp.example.com/mcp

# Scan local stdio server
offsec-ai mcp-scan --transport stdio --cmd "npx @example/mcp-server"

# With authentication
offsec-ai mcp-scan https://mcp.example.com/mcp \
  --header "Authorization: Bearer token"

# JSON output
offsec-ai mcp-scan https://mcp.example.com/mcp --output mcp-scan.json

# With LLM judge for enriched triage
offsec-ai mcp-scan https://mcp.example.com/mcp --llm-judge
offsec-ai mcp-attack https://mcp.example.com/mcp --i-have-authorization --llm-judge

Python API

import asyncio
from offsec_ai import MCPScanner, MCPTransport

async def main():
    # HTTP transport
    scanner = MCPScanner(
        target="https://mcp.example.com/mcp",
        transport=MCPTransport.HTTP,
        headers={"Authorization": "Bearer token"},
        judge=LLMJudge.from_env(),  # optional: enriches MEDIUM/LOW findings
    )
    result = await scanner.scan()

    print(f"Server: {result.server_info.name} v{result.server_info.version}")
    print(f"Tools: {len(result.tools)}, Resources: {len(result.resources)}")
    print(f"Vulnerabilities: {len(result.vulnerabilities)}")

    for vuln in result.vulnerabilities:
        print(f"  [{vuln.severity.value}] {vuln.title}: {vuln.description}")

    # Stdio transport
    scanner = MCPScanner(
        target="stdio://local",
        transport=MCPTransport.STDIO,
        cmd=["npx", "@example/mcp-server"],
    )
    result = await scanner.scan()

asyncio.run(main())

---

MCP Attacker

Performs active security testing against MCP servers. Requires explicit authorization.

Attack Suite

Attack	Safe Mode	Deep Mode	Description
Auth Bypass	✅	✅	Null token, empty bearer, X-Forwarded-For injection
Path Traversal	❌	✅	/etc/passwd, .env, shadow file read attempts
Tool Injection	❌	✅	Malicious payload in tool call arguments
Command Injection	❌	✅	Shell metacharacter injection in tool params

CLI Usage

# Safe mode (auth bypass only) — must provide --i-have-authorization
offsec-ai mcp-attack https://mcp.example.com/mcp --i-have-authorization

# Deep mode (all attacks)
offsec-ai mcp-attack https://mcp.example.com/mcp \
  --i-have-authorization --mode deep

# JSON output
offsec-ai mcp-attack https://mcp.example.com/mcp \
  --i-have-authorization --output attack-report.json

Python API

import asyncio
from offsec_ai import MCPAttacker, MCPScanner, AuthorizationRequired

async def main():
    # Authorization is enforced at instantiation
    try:
        bad = MCPAttacker()               # raises AuthorizationRequired
    except AuthorizationRequired:
        pass

    attacker = MCPAttacker(authorized=True)

    # Optional: use scan result to guide attacks
    scanner = MCPScanner("https://mcp.example.com/mcp")
    scan_result = await scanner.scan()

    report = await attacker.attack(
        target="https://mcp.example.com/mcp",
        transport="http",
        mode="deep",
        scan_result=scan_result,
    )

    print(f"Attacks run: {report.attacks_run}")
    print(f"Triggered: {len(report.triggered_results)}")
    for r in report.triggered_results:
        print(f"  [{r.severity.value}] {r.title}")

asyncio.run(main())

---

OpenClaw Gateway Security

OpenClaw is a self-hosted AI-assistant gateway that bridges messaging platforms (Telegram, Discord, Slack, etc.) to LLM backends. Because OpenClaw instances are often internet-exposed, misconfigurations lead to unauthenticated LLM access, conversation history disclosure, SSRF, and prompt injection surfaces.

Scanner (openclaw-scan)

Five-phase passive assessment — no exploitation:

Phase	What it does
1 — Fingerprint	Probe /health, /status, /api/v1/status; match headers/body against OpenClaw signatures; extract version and gateway ID
2 — Endpoint Enumeration	Probe all known API paths (/api/v1/*, /ws/*, /webhooks); flag endpoints leaking API keys or tokens in response bodies
3 — Authentication Posture	Detect unauthenticated REST API access; probe for unauthenticated WebSocket upgrade on /ws and /api/v1/ws
4 — Configuration Assessment	Parse /api/v1/config for DM policy and sandbox mode settings
5 — CVE / Misconfiguration	Cross-reference findings against advisory database; produce severity-ranked vulnerability list

Advisory Database

ID	Severity	Finding
OCL-ADV-001	Critical	Unauthenticated REST API access
OCL-ADV-002	High	Open DM policy — all channels accepted
OCL-ADV-003	High	Sandbox mode disabled
OCL-ADV-004	High	Unauthenticated WebSocket connection
OCL-ADV-005	Medium	Health/status endpoint information disclosure
OCL-ADV-006	Medium	Webhook automation SSRF risk
OCL-ADV-007	Medium	Session history and message log exposure
OCL-ADV-008	Medium	Model API key leakage via config endpoint
OCL-ADV-009	Low	Gateway version fingerprinting
OCL-ADV-010	Info	OpenClaw instance fingerprint

CLI Usage

# Passive scan — fingerprint and report misconfigurations
offsec-ai openclaw-scan 192.168.1.10

# Custom port / TLS
offsec-ai openclaw-scan gateway.example.com --port 18789 --tls

# With bearer token (authenticated scan)
offsec-ai openclaw-scan gateway.example.com \
    --header "Authorization: Bearer <token>"

# Export JSON report
offsec-ai openclaw-scan 192.168.1.10 --format json --output report.json

# Active attack (requires explicit authorization flag)
offsec-ai openclaw-attack 192.168.1.10 --i-have-authorization

# Deep mode — message injection + WebSocket + SSRF probes
offsec-ai openclaw-attack 192.168.1.10 --i-have-authorization --mode deep

# Export attack report
offsec-ai openclaw-attack 192.168.1.10 --i-have-authorization \
    --mode deep --format json --output attack.json

Python API

import asyncio
from offsec_ai.core.openclaw_scanner import OpenClawScanner
from offsec_ai.core.openclaw_attacker import OpenClawAttacker
from offsec_ai.exceptions import AuthorizationRequired

async def main():
    # Passive scan
    scanner = OpenClawScanner(
        target="192.168.1.10",
        port=18789,
        use_tls=False,
    )
    result = await scanner.scan()

    print(f"OpenClaw detected : {result.openclaw_detected}")
    print(f"Version           : {result.version}")
    print(f"Unauthenticated   : {result.unauthenticated_access}")
    print(f"Vulnerabilities   : {len(result.vulnerabilities)}")
    for v in result.vulnerabilities:
        print(f"  [{v.severity}] {v.advisory_id}: {v.title}")

    # Authorized active attack
    try:
        attacker = OpenClawAttacker(authorized=True)
        report = await attacker.attack(
            target="192.168.1.10",
            port=18789,
            mode="safe",   # "safe" | "deep"
        )
        print(f"Attacks triggered : {len(report.triggered_results)}")
        for r in report.triggered_results:
            print(f"  [{r.severity}] {r.title}")
    except AuthorizationRequired as exc:
        print(exc)

asyncio.run(main())

See docs/openclaw.md for the full guide including remediation advice.

---

Kubernetes Cluster Security

Black-box scanning and authorized red-team testing of exposed Kubernetes cluster components, aligned with the OWASP Kubernetes Top 10 (2025). No kubernetes SDK or kubeconfig required — all probes are over the network via httpx.

Component Surface

Component	Default Ports	Key Probes
kube-apiserver	6443, 443, 8080	/version, /healthz, /api, anon /api/v1/secrets//pods, SelfSubjectAccessReview
kubelet	10250 (rw), 10255 (ro)	/pods, /runningpods, /stats/summary, /spec; /exec /run (attack)
etcd	2379, 2380	/version, /health, v2/v3 keys
scheduler / controller-mgr	10259 / 10257	/healthz, /metrics
kube-proxy / cAdvisor	10249 / 4194	/healthz, metrics
Dashboard	8001, 30000–32767	UI accessibility, auth posture

OWASP K8s Top 10 Coverage

ID	Category	Black-box coverage
K01	Insecure Workload Configurations	⚠️ via kubelet /pods spec (privileged, hostPath, hostNetwork)
K02	Overly Permissive Authorization	⚠️ via anonymous SelfSubjectAccessReview (deep mode)
K03	Secrets Management Failures	⚠️ via anon apiserver /api/v1/secrets + kubelet env exposure
K04	Lack of Cluster Policy Enforcement	🔎 informational (admission webhook hints)
K05	Missing Network Segmentation	🔎 informational (exposed NodePort / internal services)
K06	Overly Exposed Components	✅ PRIMARY — all component ports probed for accessibility
K07	Misconfigured / Vulnerable Components	✅ /version → CVE match; insecure port 8080 detection
K08	Cluster → Cloud Lateral Movement	⚠️ cloud IMDS SSRF probes (deep mode)
K09	Broken Authentication Mechanisms	✅ anonymous-auth detection on apiserver + kubelet
K10	Inadequate Logging and Monitoring	🔎 informational only

✅ Full coverage · ⚠️ Partial (deep mode or limited by anon access) · 🔎 Informational

Advisory Database

ID	CVE	Severity	Finding
K8S-ADV-001	—	Critical	kube-apiserver exposed without authentication
K8S-ADV-002	—	Critical	Kubelet read-write port (10250) exposed without auth
K8S-ADV-003	—	High	Kubelet read-only port (10255) accessible
K8S-ADV-004	—	Critical	etcd accessible without authentication
K8S-ADV-005	—	Medium	Kubernetes Dashboard exposed without auth
K8S-ADV-006	—	High	Scheduler / controller-manager metrics port exposed
CVE-2018-1002105	CVE-2018-1002105	Critical	API server privilege escalation via API aggregation
CVE-2019-11253	CVE-2019-11253	High	API server DoS via malformed YAML/JSON
CVE-2020-8558	CVE-2020-8558	High	NodePort services reachable via loopback interface
CVE-2021-25741	CVE-2021-25741	High	Symlink + hardlink in volume path traversal
CVE-2022-3294	CVE-2022-3294	High	Node address bypass for node restriction admission plugin

CLI Usage

# Passive scan — probe all default K8s component ports
offsec-ai k8s-scan 192.168.1.100

# Target specific ports
offsec-ai k8s-scan k8s.example.com --port 6443 --port 10250

# With authentication header (semi-auth scan)
offsec-ai k8s-scan k8s.example.com \
    --header "Authorization: Bearer <token>"

# Enable LLM judge for finding triage and remediation advice
offsec-ai k8s-scan 192.168.1.100 --llm-judge

# Export JSON report
offsec-ai k8s-scan 192.168.1.100 --format json --output k8s-scan.json

# Authorized active attack (safe mode — anon reads + RBAC review)
offsec-ai k8s-attack 192.168.1.100 --i-have-authorization

# Deep mode — kubelet /exec, secret extraction, etcd dump, cloud IMDS SSRF
offsec-ai k8s-attack 192.168.1.100 --i-have-authorization --mode deep

# Export attack report
offsec-ai k8s-attack 192.168.1.100 --i-have-authorization \
    --mode deep --format json --output k8s-attack.json

Python API

import asyncio
from offsec_ai.core.k8s_scanner import K8sScanner
from offsec_ai.core.k8s_attacker import K8sAttacker
from offsec_ai.core.llm_judge import LLMJudge
from offsec_ai.exceptions import AuthorizationRequired

async def main():
    # Optional LLM judge — auto-detects OPENAI/ANTHROPIC/GEMINI key from env
    judge = LLMJudge()   # rule-based fallback when no key is set

    # Passive scan
    scanner = K8sScanner(
        target="192.168.1.100",
        ports=[6443, 10250, 2379],
        judge=judge,
    )
    result = await scanner.scan()

    print(f"Kubernetes detected : {result.is_kubernetes}")
    print(f"Version             : {result.server_info.git_version}")
    print(f"Exposed components  : {[c.component.value for c in result.exposed_components]}")
    print(f"OWASP coverage      : {result.owasp_coverage}")
    print(f"Vulnerabilities     : {len(result.vulnerabilities)}")
    for v in result.vulnerabilities:
        print(f"  [{v.severity.value}] {v.owasp_id} {v.vuln_id}: {v.title}")
        if v.llm_reasoning:
            print(f"    LLM: {v.llm_reasoning}")

    # Authorized active attack
    try:
        attacker = K8sAttacker(authorized=True, judge=judge)
        report = await attacker.attack(
            target="192.168.1.100",
            mode="safe",           # "safe" | "deep"
            scan_result=result,   # guides attack selection
        )
        print(f"Attacks run       : {len(report.attack_results)}")
        print(f"Succeeded         : {len(report.successful_attacks)}")
        for r in report.successful_attacks:
            print(f"  [{r.severity.value}] {r.owasp_id} {r.attack_id}: {r.description}")
    except AuthorizationRequired as exc:
        print(exc)

asyncio.run(main())

See docs/k8s.md for the full guide including OWASP K8s Top 10 mapping, CVE database, attack sequences, and remediation advice.

---

Infrastructure Scanning

Port Scanner

offsec-ai scan example.com
offsec-ai scan example.com --ports 80,443,8080,8443
offsec-ai scan example.com google.com --output results.json

from offsec_ai import PortChecker
import asyncio

async def main():
    checker = PortChecker()
    result = await checker.scan_host("example.com", ports=[80, 443, 8080])
    open_ports = [p for p in result.ports if p.is_open]
    print(f"Open: {[p.port for p in open_ports]}")

asyncio.run(main())

L7 Protection Detection

offsec-ai l7-check example.com
offsec-ai l7-check example.com --trace-dns
offsec-ai full-scan example.com

SSL/TLS Certificate Analysis

offsec-ai cert-check example.com
offsec-ai cert-chain github.com
offsec-ai cert-info google.com

from offsec_ai import CertificateAnalyzer
import asyncio

async def main():
    analyzer = CertificateAnalyzer()
    chain = await analyzer.analyze_certificate_chain("example.com", 443)
    print(f"Subject: {chain.server_cert.subject}")
    print(f"Issuer: {chain.server_cert.issuer}")
    print(f"Chain complete: {chain.chain_complete}")
    print(f"Days until expiry: {chain.server_cert.days_until_expiry}")

asyncio.run(main())

mTLS Checker

offsec-ai mtls-check example.com
offsec-ai mtls-check example.com --client-cert client.crt --client-key client.key
offsec-ai mtls-gen-cert test-client.example.com
offsec-ai mtls-validate-cert client.crt client.key

OWASP Top 10 Web Scanner (2021 & 2025)

offsec-ai owasp-scan example.com
offsec-ai owasp-scan example.com --deep
offsec-ai owasp-scan example.com -c A02,A05,A07 -t nginx --verbose
offsec-ai owasp-scan example.com -f pdf -o report.pdf

Hybrid Identity Detection

offsec-ai hybrid-identity example.com
offsec-ai hybrid-identity example.com --verbose --output results.json

---

All CLI Commands

offsec-ai --help

Commands:
  ai-owasp-scan       Probe a live LLM/AI endpoint for AI OWASP Top 10
  mcp-scan            Scan an MCP endpoint for security vulnerabilities
  mcp-attack          Perform authorized active testing against an MCP server
  openclaw-scan       Five-phase passive security scan of an OpenClaw AI gateway
  openclaw-attack     Authorized active attack against an OpenClaw gateway
  k8s-scan            Black-box Kubernetes cluster security scan (OWASP K8s Top 10)
  k8s-attack          Authorized active red-team attack against Kubernetes components
  scan                Scan target hosts for open ports
  l7-check            Check for L7 protection services (WAF, CDN, etc.)
  full-scan           Port scan + L7 protection detection
  cert-check          Analyze SSL/TLS certificate chain
  cert-chain          Analyze complete certificate chain and trust path
  cert-info           Show detailed certificate information
  dns-trace           Trace DNS records and analyze L7 protection
  owasp-scan          OWASP Top 10 2021/2025 vulnerability scanner
  hybrid-identity     Check for Azure AD/ADFS hybrid identity setup
  mtls-check          Check for mTLS authentication support
  mtls-gen-cert       Generate a self-signed certificate for mTLS testing
  mtls-validate-cert  Validate client certificate and private key files
  service-detect      Detect service version and information

---

Docker

docker run --rm htunnthuthu/offsec-ai:latest ai-owasp-scan https://api.example.com/v1/chat/completions
docker run --rm htunnthuthu/offsec-ai:latest mcp-scan https://mcp.example.com/mcp
docker run --rm htunnthuthu/offsec-ai:latest scan example.com
docker run --rm htunnthuthu/offsec-ai:latest owasp-scan example.com

# Save output to host
docker run --rm -v $(pwd):/app/output htunnthuthu/offsec-ai:latest \
  ai-owasp-scan https://api.example.com/v1/chat/completions \
  --output /app/output/llm-report.json

# LLM Judge — openai, anthropic, or gemini key auto-detected; no extra install needed
docker run --rm \
  -e OPENAI_API_KEY=sk-... \
  htunnthuthu/offsec-ai:latest \
  ai-owasp-scan https://api.example.com/v1/chat/completions --llm-judge

# Custom OpenAI-compatible backend (Ollama, LM Studio, Azure OpenAI…)
docker run --rm \
  -e OFFSEC_LLM_BASE_URL=http://host.docker.internal:11434/v1 \
  -e OFFSEC_LLM_MODEL=llama3 \
  htunnthuthu/offsec-ai:latest \
  ai-owasp-scan https://api.example.com/v1/chat/completions --llm-judge

See docs/DOCKER.md for the full Docker reference including CI/CD integration, Kubernetes jobs, Makefile publish targets, and troubleshooting.

---

Configuration

Environment Variables

Variable	Description
OPENAI_API_KEY	Enable OpenAI-based LLM judge
ANTHROPIC_API_KEY	Enable Anthropic-based LLM judge
OFFSEC_LLM_BASE_URL	Custom OpenAI-compatible base URL for LLM judge

Optional Extras

pip install "offsec-ai[ai]"   # Adds openai + anthropic for LLM judge

---

Security & Ethics

This tool is designed for authorized security assessments only.

• Active attack features display an authorization banner and require --i-have-authorization
• MCPAttacker(authorized=False) raises AuthorizationRequired at instantiation — cannot be bypassed
• Default scan modes are passive (safe mode) and will not modify target systems
• Do not use against systems you do not own or lack explicit written permission to test

Please review SECURITY.md and CONTRIBUTING.md before contributing.

---

Requirements

• Python 3.12+
• See requirements.txt for full dependency list

---

License

MIT — see LICENSE