RAPTOR: The AI Security Agent That Actually Hunts Bugs While You Sleep
Forget everything you know about security automation. What if I told you there's an AI-powered framework that doesn't just scan your code—it thinks like a hacker, finds vulnerabilities, writes exploits, and even patches them autonomously?
Meet RAPTOR (Recursive Autonomous Penetration Testing and Observation Robot), and yeah, they really wanted that name. This isn't your grandfather's security scanner. This is what happens when you combine Claude Code with the most powerful offensive security tools on the planet.
I've spent weeks testing this beast, and trust me—this changes the game.
🚨 What Makes RAPTOR Absolutely Insane:
- Autonomous vulnerability discovery - Finds bugs you didn't know existed
- AI-powered exploit generation - Creates working proof-of-concepts automatically
- Intelligent patching - Suggests fixes that actually work
- Multi-tool orchestration - Semgrep, CodeQL, AFL++ fuzzing all in one
- Open source & extensible - Built for the community, by legends
[MISSION BRIEFING]
What Is RAPTOR? (And Why Should You Care)
Picture this: It's 3 AM. Your production server just got compromised. You're scrambling to figure out how they got in, what they accessed, and how to fix it before the board meeting at 9 AM.
Now imagine if you had an AI security researcher that had already found that vulnerability weeks ago, generated a proof-of-concept exploit to prove it was real, and created a patch—all autonomously. That's RAPTOR.
💡 The Big Idea: RAPTOR transforms Claude Code into an autonomous offensive/defensive security agent. It doesn't just scan—it thinks, analyzes, exploits, and patches.
Here's what makes it different from traditional security tools:
| Traditional Tools | RAPTOR |
|---|---|
| Finds potential issues | Proves exploitability |
| Generates reports | Generates working exploits |
| You fix the code | AI suggests patches |
| Single tool focus | Multi-tool orchestration |
| Manual workflow | Autonomous operation |
How RAPTOR Actually Works (The Technical Deep Dive)
RAPTOR is built on a multi-layered architecture that combines the reasoning power of Large Language Models with battle-tested security tools. Here's the magic:
🧠 The AI Brain: Claude Code Integration
At its core, RAPTOR uses Claude Code as its reasoning engine. But it's not just prompting an AI—it's a carefully orchestrated system with:
- Bootstrap Layer - Always-loaded foundational instructions
- Tier 1 Capabilities - Adversarial thinking, analysis guidance, error recovery
- Tier 2 Expert Personas - 9 specialized security experts (Mark Dowd, Charlie Miller, etc.)
- Alpha Skills - User-created custom capabilities
🛠️ The Arsenal: Integrated Security Tools
┌─[RAPTOR@Security-Lab]─[~]
└──╼ $ raptor --show-arsenal
[+] Static Analysis Tools:
├── Semgrep (pattern matching)
└── CodeQL (semantic analysis with dataflow)
[+] Dynamic Testing:
├── AFL++ (fuzzing engine)
└── rr debugger (deterministic debugging)
[+] Web Security:
└── Playwright (browser automation)
[+] Build & Debug:
├── gcc, clang, make, cmake
└── gdb, binutils
[+] AI Reasoning:
└── Claude Code (autonomous decision-making)
⚡ The Workflow: Autonomous Operation
Here's what happens when you run RAPTOR:
- Scan Phase - Semgrep and CodeQL analyze your codebase for vulnerabilities
- AI Analysis - Claude evaluates findings using adversarial thinking (Impact × Exploitability)
- Exploitation - Generates proof-of-concept exploits for confirmed issues
- Validation - Tests exploits to prove they work
- Patching - Creates secure code fixes
- Reporting - Comprehensive structured output with remediation steps
The Offensive Capabilities That Make RAPTOR Dangerous (In a Good Way)
Let's talk about what RAPTOR can actually do. These aren't theoretical features—this is real, working functionality:
/scan - Static Code Analysis
Runs Semgrep and CodeQL simultaneously to find vulnerabilities through pattern matching and semantic analysis. Catches SQL injection, XSS, buffer overflows, and more.
/fuzz - Binary Fuzzing
Unleashes AFL++ to bombard your binaries with malformed inputs, uncovering crashes and memory corruption bugs that static analysis misses.
/agentic - Full Autonomous Workflow
The complete package: analysis → exploit generation → patch creation. This is where RAPTOR truly shines as an autonomous agent.
/exploit - PoC Generation
Generates compilable, working exploit code to prove vulnerabilities are real. No more "might be exploitable" - you get proof.
/patch - Security Patching
AI-generated patches that fix vulnerabilities properly. Includes FFmpeg-specific patching for recent disclosures.
⚠️ Real Talk: The exploit generation works with frontier models (Claude, GPT-4, Gemini). Local models like Ollama can handle analysis but often produce broken exploit code. You need the big guns for this.
Real-World Examples: RAPTOR in Action
Theory is boring. Let's see what RAPTOR actually finds:
[CASE_STUDY_01] Buffer Overflow in C Application
// Vulnerable code
void process_input(char *user_data) {
char buffer[256];
strcpy(buffer, user_data); // ← RAPTOR flags this
// ... rest of code
}
[RAPTOR ANALYSIS]
- Semgrep detects unsafe strcpy usage
- CodeQL traces dataflow from user input
- AI confirms exploitability (Impact: HIGH)
- Generates working buffer overflow exploit
- Suggests patch using strncpy with bounds checking
[CASE_STUDY_02] SQL Injection via ORM Bypass
// Vulnerable code
query = f"SELECT * FROM users WHERE id = {user_id}"
cursor.execute(query) // ← Bypasses ORM safety
[RAPTOR ANALYSIS]
- Semgrep catches f-string SQL construction
- AI recognizes ORM bypass pattern
- Generates SQL injection payload
- Provides parameterized query patch
Getting Started: Your First RAPTOR Hunt
Ready to unleash RAPTOR? Here's how to get started in under 5 minutes:
Option 1: Quick Install (For the Brave)
# 1. Install Claude Code
# Download from: https://claude.ai/download
# 2. Clone RAPTOR
git clone https://github.com/gadievron/raptor.git
cd raptor
claude
# 3. Let Claude install dependencies
"Install dependencies from requirements.txt"
"Install semgrep"
"Set my ANTHROPIC_API_KEY to [your-key]"
# 4. Start hunting
"hi" # RAPTOR introduces itself
/scan /path/to/your/code
Option 2: DevContainer (For the Cautious)
# All tools pre-installed in a container
docker build -f .devcontainer/Dockerfile -t raptor:latest .
# Or open in VS Code with:
# Dev Container: Open Folder in Container
⚡ Pro Tip: The devcontainer is massive (~6GB) but comes with everything: Semgrep, CodeQL, AFL++, rr debugger, Playwright, and all build tools. It's the fastest way to get started.
Your First Commands
# Quick scan
/scan /path/to/code
# Full autonomous analysis with exploits
/agentic --repo /path/to/code
# Fuzz a binary
/fuzz --binary /path/to/program --duration 3600
# Analyze a specific vulnerability
/analyze vulnerability_report.json
The Dream Team Behind RAPTOR
RAPTOR wasn't built by some random startup. This is the work of security legends:
- Gadi Evron - Security researcher and incident responder
- Daniel Cuthbert - Ethical hacker and security evangelist
- Thomas Dullien (Halvar Flake) - Legendary reverse engineer, creator of BinDiff
- Michael Bargury - Cloud security expert and researcher
These aren't theory guys. These are people who've found zero-days, responded to nation-state attacks, and built tools used by security teams worldwide. When they build something, you pay attention.
🤝 Open Source Community: RAPTOR is MIT licensed and actively welcoming contributions. Join the #raptor channel on Prompt||GTFO Slack to connect with the team.
The Future: Where RAPTOR Is Heading
Right now, RAPTOR is in alpha. The team admits it's "held together by vibe coding and duct tape." But here's the thing—it already works incredibly well.
The roadmap is community-driven. Want to add something? Submit a PR. Some ideas floating around:
- Better web exploitation - Enhanced Playwright integration
- YARA signature generation - Automated malware detection rules
- IDE integrations - Cursor, Windsurf, Copilot support
- Advanced fuzzing strategies - Custom mutation engines
- Cloud security modules - AWS, Azure, GCP scanning
💭 My Prediction:
Within a year, every serious security team will be running something like RAPTOR. The combination of AI reasoning and traditional security tools is too powerful to ignore. The question isn't "if" but "when" this becomes standard practice.