Author: PwnedBytes0x1
Version: 1.0.0
License: MIT
Status: Production-Ready
PwnedAgent is a comprehensive, AI-driven ethical hacking and cybersecurity research framework designed for bug bounty hunters, penetration testers, red team operators, and security researchers. It represents a complete cognitive architecture for autonomous vulnerability discovery, exploitation, and responsible disclosure.
This repository is not merely a collection of scripts — it is a living, reasoning system that encapsulates decades of offensive security knowledge into a structured, extensible, and intelligent agent architecture.
| Feature | Traditional Tools | PwnedAgent |
|---|---|---|
| Reasoning | Static signatures | Context-aware tactical reasoning |
| Adaptation | Manual configuration | Self-improving through feedback loops |
| Scope Safety | Human-dependent | Kernel-enforced hard boundaries |
| Attack Chains | Isolated tests | Automated multi-step chain construction |
| Evasion | Basic rotation | Adversarial countermeasure intelligence |
| Reporting | Manual compilation | Automated PoC + impact visualization |
PwnedAgent/
│
├── kernel/ # Safety, orchestration, and resource governance
│ ├── scope-guardian.md # Hard scope enforcement and boundary validation
│ ├── rate-limit-governor.md # Intelligent throttling to avoid disruption
│ ├── state-manager.md # Session state and context persistence
│ ├── audit-logger.md # Complete audit trail for all actions
│ ├── abort-conditions.md # Emergency stop and safety triggers
│ └── resource-scheduler.md # Computational resource optimization
│
├── memory/ # Multi-layer cognitive memory
│ ├── episodic/ # Session-specific context and findings
│ ├── semantic/ # Long-term security knowledge (CWEs, exploits, patterns)
│ └── procedural/ # Learned muscle memory and technique optimization
│
├── cognition/ # Real-time tactical reasoning engine
│ ├── core-loop/ # OODA loop implementation for hacking
│ ├── tactical-reasoning/ # Skill selection, pivot generation, chain optimization
│ └── attack-surface-pruning/ # Intelligent surface reduction
│
├── methodology/ # Complete testing methodologies
│ ├── phase-based/ # Structured by engagement phase
│ ├── target-type-specific/ # Tailored by application architecture
│ ├── time-boxed/ # Optimized for time constraints
│ ├── vulnerability-class/ # Deep-dive systematic hunting guides
│ ├── tactical-decision-trees/ # Conditional decision frameworks
│ └── platform-specific/ # Bug bounty platform optimization
│
├── skills/ # Granular exploitation capabilities
│ ├── reconnaissance/ # Information gathering techniques
│ ├── authentication-session/ # Auth bypass and session attacks
│ ├── input-validation-injection/ # All injection-class vulnerabilities
│ ├── unicode-parser-abuse/ # Unicode-based WAF bypass and parser attacks
│ ├── client-side-browser/ # XSS, CSRF, CORS, browser abuse
│ ├── api-service-layer/ # API testing, GraphQL, WebSocket
│ ├── business-logic-state/ # Logic flaws and workflow abuse
│ ├── cms-exploitation/ # CMS-specific exploitation guides
│ ├── modern-framework-abuse/ # Next.js, .NET, framework-specific attacks
│ ├── impact-escalation/ # Privilege escalation and impact maximization
│ ├── automation-intelligence/ # Fuzzing, CVE hunting, anomaly detection
│ └── ai-native-targets/ # LLM/AI application security testing
│
├── chains/ # Pre-constructed attack chains
│ ├── discovery/ # Surface mapping chains
│ ├── auth-chains/ # Authentication bypass chains
│ ├── injection-chains/ # Injection to RCE escalation
│ ├── client-side-chains/ # XSS-to-account-takeover chains
│ ├── logic-chains/ # Business logic abuse chains
│ ├── cross-domain-chains/ # Multi-domain impact chains
│ ├── cms-specific-chains/ # CMS exploit chains
│ └── xbow-grade-chains/ # High-impact crossbow-grade chains
│
├── tools/ # Tool abstractions and integrations
├── findings/ # Finding documentation and reporting
├── tradecraft/ # Operational security and evasion
├── countermeasures/ # WAF evasion and bot detection bypass
├── intelligence/ # Threat intelligence and target research
├── platform-ops/ # Bug bounty platform integrations
├── feedback/ # Triage feedback and learning loops
├── evolution/ # Self-improvement and skill evolution
├── swarm/ # Multi-agent coordination
└── meta/ # Self-monitoring and performance analytics
git clone https://github.com/PwnedBytes0x1/PwnedAgent.git
cd PwnedAgentcat BOOT_SEQUENCE.mdThe boot sequence initializes the agent's cognitive state and validates all safety constraints before any testing begins.
Based on your target type and time constraints, select a methodology from methodology/:
| Scenario | Recommended Methodology |
|---|---|
| Quick recon | methodology/time-boxed/2-hour-recon-sprint.md |
| Focused hunt | methodology/time-boxed/4-hour-focused-hunt.md |
| Deep assessment | methodology/phase-based/ |
| API target | methodology/target-type-specific/api-first-application.md |
| AI/LLM target | methodology/target-type-specific/ai-llm-application.md |
Skills are modular — load only what you need:
# Example: Load SQL injection hunting skills
cat skills/input-validation-injection/query-language/sqli.md
# Example: Load authentication bypass skills
cat skills/authentication-session/auth-mechanisms/auth-testing.mdChains are pre-built multi-step attacks:
# Example: SQLi to RCE chain
cat chains/injection-chains/sqli-to-rce.md
# Example: IDOR to full compromise
cat chains/auth-chains/idor-to-full-compromise.mdPwnedAgent is designed exclusively for authorized security testing. Every component enforces:
- Explicit scope validation before any action
- Rate limiting to prevent service disruption
- Complete audit logging for accountability
- Automatic abort on scope violation
The framework prioritizes:
- Contextual reasoning over brute force
- Precision targeting over spray-and-pray
- Adaptive evasion over static techniques
- Chain construction over isolated tests
PwnedAgent learns from:
- Triage feedback and duplicate analysis
- Historical exploit patterns
- Adversarial simulation outcomes
- Technique fitness scoring
┌─────────────────────────────────────────┐
│ SAFETY KERNEL LAYER │
├─────────────────────────────────────────┤
│ Scope Guardian ←→ Rate Limit Governor │
│ ↓ ↓ │
│ Audit Logger ←→ Abort Conditions │
│ ↓ │
│ Resource Scheduler │
└─────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────┐
│ COGNITIVE ENGINE │
├─────────────────────────────────────────┤
│ Planner → Executor → Observer → │
│ Reflector → Skill Selector → │
│ Confidence Calibrator │
└─────────────────────────────────────────┘
Every action flows through the Safety Kernel before execution. No exceptions.
| OWASP Category | Coverage Status | Primary Skills |
|---|---|---|
| A01: Broken Access Control | Complete | skills/authentication-session/authorization/ |
| A02: Cryptographic Failures | Complete | skills/authentication-session/auth-mechanisms/ |
| A03: Injection | Complete | skills/input-validation-injection/ |
| A04: Insecure Design | Complete | skills/business-logic-state/ |
| A05: Security Misconfiguration | Complete | skills/authentication-session/protective-controls/ |
| A06: Vulnerable Components | Complete | skills/automation-intelligence/static-intel/ |
| A07: Auth Failures | Complete | skills/authentication-session/auth-mechanisms/ |
| A08: Software/Data Integrity | Complete | skills/input-validation-injection/object-stream/ |
| A09: Logging Failures | Partial | skills/authentication-session/protective-controls/ |
| A10: SSRF | Complete | skills/input-validation-injection/server-side-request/ |
- Injection: SQLi, NoSQLi, SSTI, CSTI, Command Injection, LDAP/XPath Injection
- Authentication: JWT attacks, OAuth flow abuse, SAML bypass, MFA bypass, session hijacking
- Authorization: IDOR, BAC, privilege escalation, forced browsing, path traversal
- Client-Side: XSS (all types), CSRF, CORS misconfiguration, clickjacking, prototype pollution
- Server-Side: SSRF, XXE, LFI/RFI, file upload abuse, deserialization
- Logic: Business logic flaws, race conditions, payment manipulation, workflow bypass
- API: GraphQL abuse, REST method abuse, WebSocket/Socket.IO testing, mass assignment
- Modern: Unicode parser abuse, cache poisoning, HTTP request smuggling, host header injection
- AI/LLM: Prompt injection, training data extraction, model exfiltration, RAG poisoning
| Level | Description | Example |
|---|---|---|
| Bronze | Single-step vulnerability | Reflected XSS |
| Silver | Two-step chain | IDOR → PII exposure |
| Gold | Multi-step with escalation | SSRF → Metadata → Cloud takeover |
| Platinum | Cross-domain with impact | Subdomain takeover → Session theft → Admin access |
| Diamond (XBow) | Full compromise chains | Unicode WAF bypass → SSTI → RCE → Container escape |
PwnedAgent integrates multiple intelligence sources:
- Historical DNS cache for infrastructure tracking
- Wayback pattern mining for endpoint discovery
- CVE-to-skill translation for rapid exploit development
- Triage behavior modeling for report optimization
- Hunter activity monitoring for competitive intelligence
We welcome contributions from the security community. Please read:
CONTRIBUTING.md— Contribution guidelinesCODE_OF_CONDUCT.md— Community standards- Security issues: Report privately to pwnedbytes@gmail.com
- New attack chain constructions
- Target-type-specific methodologies
- Evasion technique improvements
- AI-native target testing capabilities
PwnedAgent is intended exclusively for authorized security testing and research. Unauthorized access to computer systems is illegal. This tool is provided for educational and professional security assessment purposes only. Always obtain explicit written permission before testing any system you do not own.
The authors assume no liability for misuse or damage caused by this tool. By using PwnedAgent, you agree to use it responsibly and ethically.
- The global bug bounty community for sharing knowledge
- OWASP for their comprehensive security resources
- CVE and NVD for vulnerability intelligence
- All security researchers who publish their findings responsibly
Built with precision. Honed through thousands of engagements. Ready for the next target.
— PwnedBytes0x1