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║             Autonomous Offensive/Defensive Research Framework             ║
║             Based on Claude Code (v3.0.0)                                 ║
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║             Gadi Evron, Daniel Cuthbert, Thomas Dullien (Halvar Flake)    ║
║             Michael Bargury, John Cartwright                              ║
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⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠻⠿⣿⣧⠀ Get them bugs.....⠀⠀⠀⠀⠀

Authors: Gadi Evron, Daniel Cuthbert, Thomas Dullien (Halvar Flake), Michael Bargury, John Cartwright (@gadievron, @danielcuthbert, @thomasdullien, @mbrg, @grokjc)

Licence: MIT, see LICENSE. Note that CodeQL has its own licence and does not permit commercial use.

Repository: https://github.com/gadievron/raptor

---

What is RAPTOR?

RAPTOR is an autonomous security research framework built on top of Claude Code (but not tied to it -- you can plug in your own analysis layer too). It chains together static analysis, binary analysis, LLM-powered vulnerability validation, exploit generation, and patch writing into a single workflow you can run against a codebase or binary.

It is not polished software. It was built in free time, held together with enthusiasm and duct tape, and it works well enough that we can't stop using it. If you want to make it better, open a PR.

RAPTOR stands for Recursive Autonomous Penetration Testing and Observation Robot. We really wanted to call it RAPTOR.

---

Quick Start

Option 1: Install manually

# Clone the repo
git clone https://github.com/gadievron/raptor.git
cd raptor

# Install Python dependencies
pip install -r requirements.txt

# Install Claude Code
npm install @anthropic-ai/claude-code

# or globally if you want the `claude` command on PATH everywhere. YMMV
npm install -g @anthropic-ai/claude-code

# Install Semgrep (required for scanning)
pip install semgrep

# Open RAPTOR
claude

Option 2: Devcontainer (recommended)

Everything pre-installed. Open in VS Code with Dev Containers: Open Folder in Container, or pull the prebuilt image:

docker pull danielcuthbert/raptor:latest
docker run --privileged -it -v "$(pwd):/workspaces/raptor" danielcuthbert/raptor:latest

Or build it yourself instead of pulling:

docker build -f .devcontainer/Dockerfile -t raptor:latest .
docker run --privileged -it -v "$(pwd):/workspaces/raptor" raptor:latest

The --privileged flag is required for the rr deterministic debugger. The image is large (around 6 GB). It starts from the Microsoft Python 3.12 devcontainer and adds static analysis, fuzzing, and browser automation tooling.

Once inside, just say "hi" to get started, or jump straight to a command.

---

What RAPTOR can do

Command	What it does	Status
/agentic	Full autonomous workflow: scan, validate, exploit, patch	Stable
/scan	Static analysis with Semgrep and CodeQL	Stable
/understand	Map attack surface, trace data flows, hunt vulnerability variants	Stable
/binary	Black-box binary investigation, runtime evidence, graph queries and handoff	Beta
/validate	Multi-stage exploitability validation pipeline (Stages 0-F)	Stable
/codeql	CodeQL-only deep analysis with SMT dataflow pre-screening	Stable
/sca	Software composition analysis: dependencies, advisories, supply-chain signals, SBOMs, and fixes	Beta
/exploit	Generate proof-of-concept exploit code	Beta
/patch	Generate secure patches for confirmed vulnerabilities	Beta
/fuzz	Binary fuzzing with AFL++ and crash analysis	Stable
/crash-analysis	Autonomous root-cause analysis for C/C++ crashes	Stable
/oss-forensics	Evidence-backed forensic investigation for GitHub repositories	Stable
/project	Named workspaces to organise runs and track findings over time	Stable
project threat-model	Project-owned threat model for focus areas, trust boundaries, and proof expectations	Beta
/web	Web application scanning	Alpha/stub

---

How the pipeline works

Start by creating a project so all your runs land in one place:

/project create myapp --target /path/to/code   # create a project first
/project use myapp                             # set it as active
/understand --map                              # map the attack surface
/agentic --threat-model --validate             # map, model, scan, validate
/project findings                              # review everything in one place

For a compiled artefact, the equivalent starting point is:

/binary investigate /path/to/binary            # build the evidence-backed binary map
/binary graph <run-dir> --edges --json         # query the persisted graph
/binary trace-parser <run-dir>                 # collect runtime parser evidence
/binary harness <run-dir>                      # draft a harness only when the boundary is explicit

/understand builds a context map of entry points, trust boundaries, and sinks before a line of scanning happens. /agentic then runs Semgrep and CodeQL, deduplicates findings, and dispatches each one for validation using the exploitation-validator methodology:

With --threat-model, RAPTOR runs the map first, creates threat-model.json and THREAT_MODEL.md if the project does not already have them, then feeds a compact version into /understand, autonomous analysis, and /validate. Existing project threat models are preserved unless you pass --threat-model-refresh; stale fallback maps are refused unless you explicitly pass --threat-model-use-stale. It also turns mapped unchecked flows into candidate SARIF so scanner misses do not kill the run. It is operator-owned context, not magic proof: findings still need code evidence or oracle-backed confirmation. See docs/threat-model.md.

• Stage A: is the pattern actually a vulnerability, or is the tool pattern-matching noise?
• Stage B: what does an attacker need to reach it, and what gets in the way?
• Stage C: does the code path actually exist? can it be reached from outside?
• Stage D: final call -- is this test code, does it need unrealistic preconditions, is the model hedging?

Findings that clear validation get exploit PoCs and patches generated. A cross-finding analysis runs at the end to find shared root causes and attack chains.

/validate runs this same pipeline as a standalone step if you already have findings from a previous scan.

For a compiled artefact, /binary <path> now runs an evidence-first investigation rather than dumping a pile of raw reverse-engineering artefacts on the operator. Underneath it still builds the SHA-256-bound manifest, evidence ledger, context map, checklist and SQLite graph from file metadata, imports and radare2 xrefs. Mach-O apps also get slice inventory, bundle metadata and Objective-C / Swift class selectors; high-value pseudocode is persisted rather than disappearing inside the run. PE DLL exports, Windows driver dispatchers and Linux kernel-module ioctl handlers are handled as their own ingress candidates too, with PE architecture read from the COFF header rather than guessed. The investigation layer then queries that graph, ranks external ingress before generic sink leads, discovers declared helper/sibling binaries, and writes a compact report split into facts, structural inferences and unproven hypotheses. Frida observations, fuzz crash witnesses, explicit Z3 checks and binary diffs can then add stronger evidence later. RAPTOR also keeps the internal call graph needed to recover bounded ingress-to-parser candidates, so an app callback can be narrowed to the internal function that actually calls XML_Parse, d2i_X509, jpeg_read_header or another real parser surface without pretending that is taint proof. /binary trace-parser <run-dir> is the explicit dynamic follow-on: it runs the narrow Frida parser trace, then refreshes the same context map, handoff, graph and investigation report in place. /binary investigate --active maps first and only launches a real fuzz campaign when a concrete harness boundary exists; app, DLL and driver targets get a harness or snapshot step instead. /binary harness writes an evidence-backed harness spec for the chosen ingress and only emits candidate source when the ABI or IOCTL contract is explicit. It does not blag its way from “memcpy exists” to “this is exploitable”: imports, selectors and call edges stay candidates until something mechanical proves more. See docs/binary-understanding.md.

---

Software Composition Analysis

/sca analyses the dependency and supply-chain side of a project. It is not just a requirements-file CVE lookup: RAPTOR discovers manifests, lockfiles, inline install commands, workflow dependencies, and container/base-image package sources, then normalises them into a single dependency view.

The scan enriches dependencies with OSV advisories, CISA KEV, EPSS, CISA Vulnrichment/SSVC, reachability, exploit-evidence signals, hygiene checks, supply-chain heuristics, licence policy findings, and optional LLM review/triage. It emits RAPTOR-native findings plus SBOM and CI-friendly output:

• findings.json - canonical RAPTOR findings
• report.md - human-readable summary
• sbom.cdx.json - CycloneDX SBOM with VEX data
• findings.sarif - GitHub/GitLab code-scanning output

Common commands:

python3 raptor.py sca --repo /path/to/project
python3 raptor.py sca --repo /path/to/project --no-llm
python3 raptor.py sca --repo /path/to/project --fail-on-severity high --fail-on-kev
python3 raptor.py sca --repo /path/to/project fix
python3 raptor.py sca check PyPI django 4.2.10

Useful subcommands include fix, check, upgrade, diff, verify, health, render, suppress, and clean-cache. See docs/sca.md for the full reference.

---

Z3 SMT integration

RAPTOR has a two-layer Z3 integration (pip install z3-solver). It is optional. Everything works without it, but the results are better with it.

Dataflow pre-screening (CodeQL)

When CodeQL produces a path result, the path constraints are checked for satisfiability before any LLM call is made. Paths that are provably unreachable get dropped immediately. For paths that are reachable, Z3 produces concrete candidate inputs that go into the analysis prompt, so the LLM has something specific to reason about rather than abstract patterns.

One-gadget constraint analysis (binary feasibility)

During binary exploit feasibility assessment, Z3 checks whether a one-gadget's register and memory constraints are satisfiable against the concrete crash state. Gadgets are ranked by actual reachability rather than heuristics, so you spend time on gadgets that can actually work.

Z3 is pre-installed in the devcontainer. For manual installs: pip install z3-solver.

---

Running offline and in air-gapped pipelines

Semgrep scanning works fully offline. All registry packs that would normally be fetched from semgrep.dev at scan time are shipped in the repo under engine/semgrep/rules/registry-cache/. The scanner resolves pack IDs to local files before invoking semgrep, so no network call happens.

Cached packs: p/security-audit, p/owasp-top-ten, p/secrets, p/command-injection, p/jwt, p/default, p/xss.

Custom rules under engine/semgrep/rules/ were never network-dependent and run as normal.

CodeQL needs network access only during initial setup to download the CLI and query packs. Once installed it runs offline.

---

Using a different LLM

RAPTOR has two separate model layers, and it is worth knowing how both work before you change anything.

The orchestration layer is always Claude Code. The CLAUDE.md, skills, and commands all run as Claude Code instructions. To change which Claude model orchestrates RAPTOR, use Claude Code's --model flag or the /model command inside a session.

The analysis dispatch layer is the LLM that analyses individual vulnerability findings. This is separate from the orchestration layer and can be any supported provider. Configure it in ~/.config/raptor/models.json:

{
  "models": [
    {
      "provider": "anthropic",
      "model": "claude-opus-4-6",
      "api_key": "sk-ant-...",
      "role": "analysis"
    },
    {
      "provider": "openai",
      "model": "gpt-5.4",
      "api_key": "sk-...",
      "role": "analysis"
    },
    {
      "provider": "anthropic",
      "model": "claude-sonnet-4-6",
      "api_key": "sk-ant-...",
      "role": "aggregate"
    }
  ]
}

Or skip the config file and set environment variables. RAPTOR will detect them automatically:

export ANTHROPIC_API_KEY=sk-ant-...    # Anthropic Claude
export OPENAI_API_KEY=sk-...           # OpenAI
export GEMINI_API_KEY=...              # Google Gemini
export MISTRAL_API_KEY=...             # Mistral
export OLLAMA_HOST=http://localhost:11434  # Local Ollama

Model roles let you assign different models to different tasks:

Role	What it does
analysis	Validates and analyses each finding (Stages A-D)
code	Writes exploit PoCs and patch code
consensus	Second-opinion vote on true positives
aggregate	Optional. LLM-written narrative synthesis on top of the deterministic multi-model correlation, written to aggregation.json and the final agentic-report.md
fallback	Used if the primary model fails or hits rate limits

If no roles are set, the first model in the list handles everything. For multi-model source-code analysis, configure two or more analysis models — you'll get the deterministic correlation by default. The aggregate role is optional and adds an LLM-written summary on top:

python3 raptor.py agentic --repo /code \
  --model claude-opus-4-6 \
  --model gpt-5.4 \
  --aggregate claude-sonnet-4-6

Budget control:

export RAPTOR_MAX_COST=5.00   # cap analysis spend at $5 per run

Ollama works for analysis but produces unreliable exploit and patch code. For code generation tasks, use a frontier model.

Fast-tier short-circuit + the model scorecard

When your analysis-tier model has a same-provider cheaper sibling (Anthropic Opus → Haiku, OpenAI 5.x → 4o-mini, Gemini Pro → Flash-Lite, Mistral Large → Small), RAPTOR will use it as a prefilter on consumers that wire into the substrate (codeql today; SCA and others as follow-ups land). The cheap model only ever short-circuits on confident false positives; ambiguous cases and confident-TPs always run the full analysis. Trust accumulates per (model, decision_class) cell — RAPTOR records cheap-vs-full agreement and only short-circuits once the Wilson 95% upper-bound on the cell's miss-rate falls at or below 5%.

To inspect what your models are good at, use /scorecard (or directly: libexec/raptor-llm-scorecard list). The scorecard is global (lessons carry across projects) and persists at out/llm_scorecard.json.

---

Projects

Without a project, each run gets its own timestamped directory under out/. With a project, everything goes into one place and you get merged findings, coverage tracking, and diffs between runs.

/project create myapp --target /path/to/code -d "Short description"
/project use myapp

/scan
/understand --map
/validate

/project status                # all runs, pass/fail, timestamps
/project findings              # merged findings across all runs
/project findings --detailed   # per-finding detail
/project coverage --detailed   # which files were reviewed
/project diff myapp run1 run2  # compare two runs
/project report                # full merged report
/project clean --keep 3        # remove old runs, keep the last 3
/project export myapp /tmp/myapp.zip
/project none                  # clear active project

---

Architecture

RAPTOR is two layers.

The Python execution layer (raptor.py, packages/, core/, engine/) handles the heavy lifting: running Semgrep and CodeQL, managing subprocesses, parsing SARIF, deduplicating findings, dispatching LLM API calls, tracking costs, writing output files. It does not make decisions. It executes.

The Claude Code decision layer (.claude/, tiers/, CLAUDE.md) makes the calls: which findings to prioritise, how to interpret results, what the attack scenario is, whether the exploit is realistic. Implemented as Claude Code skills, commands, and agents that load progressively.

CLAUDE.md              always loaded -- bootstrap, routing, security rules
.claude/commands/      slash commands (/agentic, /scan, /validate, etc.)
.claude/skills/        methodology detail, loaded on demand
tiers/                 adversarial thinking, recovery, expert personas
.claude/agents/        specialist sub-agents (offsec, crash analysis, forensics)

The split means you can run the Python layer from a CI pipeline (python3 raptor.py scan --repo ...) and get structured SARIF output without Claude Code, or run it interactively with the full agentic workflow.

---

OSS forensics

/oss-forensics investigates public GitHub repositories using evidence from multiple sources: the GitHub API, GH Archive (immutable event history via BigQuery), the Wayback Machine, and local git history. It runs a structured pipeline from evidence collection through hypothesis formation to a final forensic report.

Requires GOOGLE_APPLICATION_CREDENTIALS for BigQuery access. See .claude/commands/oss-forensics.md for details.

---

Expert personas

Nine expert personas are available on demand. Load one when you want a different perspective on a finding or a specific technique:

Mark Dowd                       Binary exploitation and vulnerability research
Charlie Miller / Halvar Flake   Low-level exploitation and reverse engineering
Security Researcher             General adversarial code review
Patch Engineer                  Secure fix generation
Penetration Tester              Realistic attack scenario assessment
Fuzzing Strategist              Corpus design and triage
Binary Exploitation Specialist  ROP, heap, and memory corruption
CodeQL Dataflow Analyst         Query writing and path analysis
CodeQL Finding Analyst          Triage and false positive identification

Tell Claude which one to use, e.g. "Use the Binary Exploitation Specialist".

---

Documentation

File	Contents
docs/CLAUDE_CODE_USAGE.md	Complete usage guide for interactive sessions
docs/PYTHON_CLI.md	Python CLI reference for scripting and CI
docs/sca.md	Software composition analysis reference
docs/FUZZING_QUICKSTART.md	Binary fuzzing guide
docs/ARCHITECTURE.md	Technical architecture detail
docs/EXTENDING_LAUNCHER.md	How to add new capabilities
docs/DEPENDENCIES.md	External tools, versions, and licences
.claude/commands/oss-forensics.md	OSS forensics investigation guide
tiers/personas/README.md	Persona reference

---

Contributing

RAPTOR is open source. Good places to start if you want to contribute:

• A proper web exploitation module (the current one is a stub)
• SSRF detection rules (no registry pack exists and the local rules directory is empty)
• YARA signature generation
• Ports to other AI coding tools (Cursor, Windsurf, Copilot, Cline)
• Better firmware analysis coverage
• Anything you think is missing

Releases are tagged as vX.Y.Z and built automatically by CI. Commit prefixes determine what goes in the changelog: feat: for new features, fix: for bug fixes, security: for security changes, docs: for documentation. Anything without a prefix lands in "Other changes". No strict convention required, but it helps.

Submit pull requests. Chat with us on the #raptor channel in the Prompt||GTFO Slack: https://join.slack.com/t/promptgtfo/shared_invite/zt-3v2b4sll3-SfyzFRw2lykx_XQX7F3uNQ

---

Licence

MIT -- Copyright (c) 2025-2026 Gadi Evron, Daniel Cuthbert, Thomas Dullien (Halvar Flake), Michael Bargury, John Cartwright.

See LICENSE for the full text. Review the licences for all dependencies before commercial use -- CodeQL in particular does not permit it.

Issues: https://github.com/gadievron/raptor/issues