Listen to this Post
Introduction:
The convergence of large language models (LLMs) and traditional security tooling has given rise to autonomous frameworks capable of performing end‑to‑end vulnerability research. RAPTOR, built on Claude Code, integrates static analysis, binary reverse engineering, LLM‑driven validation, exploit generation, patch creation, and fuzzing into a single pipeline – enabling security teams to automate both offensive and defensive workflows against source code and binaries.
Learning Objectives:
- Understand how to deploy and configure RAPTOR’s multi‑model analysis pipeline (Semgrep, CodeQL, Z3, AFL++, Ollama, Claude, GPT, Gemini).
- Execute autonomous static and binary analysis, followed by LLM‑powered vulnerability validation and exploit generation.
- Apply fuzzing workflows and OSS forensics techniques to discover and remediate vulnerabilities, with practical Linux/Windows commands.
You Should Know:
- Setting Up RAPTOR’s Core Analysis Engine (Static + Binary)
RAPTOR orchestrates multiple analyzers. Start by installing dependencies on Linux (Ubuntu 22.04+):
Install Semgrep, CodeQL, AFL++, and Z3 pip install semgrep wget https://github.com/github/codeql-cli/releases/latest/download/codeql-linux64.zip unzip codeql-linux64.zip && sudo mv codeql /usr/local/bin/ sudo apt install afl++ z3 libz3-dev -y Pull Ollama (local LLM) and set up Claude/GPT APIs curl -fsSL https://ollama.com/install.sh | sh ollama pull llama2 or any local model export CLAUDE_API_KEY="your_key_here" export OPENAI_API_KEY="your_key_here"
For Windows (WSL2 recommended), use similar commands within Ubuntu WSL. RAPTOR’s binary analysis requires Ghidra or Radare2:
sudo apt install radare2 wget https://ghidra-sre.org/ghidra_11.0_PUBLIC_20231222.zip && unzip ghidra
Step‑by‑step – RAPTOR expects a configuration file (raptor.yaml) listing targets (source repo or binary), enabled tools, and LLM models. Run:
git clone https://github.com/example/raptor-framework hypothetical cd raptor python raptor.py --target ./vuln_app --mode full_auto
The framework sequentially executes Semgrep (SAST), CodeQL (code query), then feeds findings to Claude/GPT for false‑positive validation and exploit feasibility analysis.
2. LLM‑Powered Vulnerability Validation & Exploit Generation
After static analysis, RAPTOR uses a multi‑model pipeline to validate each finding and generate proof‑of‑concept exploits. For a buffer overflow detected in binary:
RAPTOR's internal command (example) raptor validate --finding "buffer_overflow in parse_input()" --model claude-3
The LLM receives the code snippet, assembly context (from Radare2), and a prompt like:
“Is this truly exploitable? If yes, generate a Python exploit script using pwntools.”
Step‑by‑step guide – RAPTOR extracts vulnerable function signatures, runs Z3 SMT solver to check constraint satisfiability, and then:
1. Calls GPT‑4 to craft an exploit payload.
- Tests the exploit in a sandboxed Docker container.
- If successful, generates a patch using Claude (as shown in Section 4).
You can manually replicate this validation using a local LLM:
ollama run llama2 "Analyze this C snippet for stack overflow: <code>"
For exploit generation with pwntools (Linux):
from pwn import
p = process('./vuln_bin')
p.sendline(b'A'64 + p32(0xdeadbeef))
p.interactive()
RAPTOR automates the entire loop, saving exploits in ./generated_exploits/.
3. Fuzzing Workflows Integrated with LLM Seed Generation
RAPTOR combines AFL++ with LLM‑generated initial seeds to speed up coverage. It first runs Semgrep to identify input‑handling functions, then prompts GPT to generate diverse seed inputs.
Step‑by‑step:
- Prepare target binary `./target` with instrumentation (
afl-gcc -o target target.c). - Run RAPTOR’s fuzzing module:
raptor fuzz --target ./target --input "user_input" --seeds 1000 --timeout 3600
Behind the scenes, RAPTOR creates a seeds directory with LLM‑generated inputs (e.g., JSON, XML, long strings, special chars), then launches:
afl-fuzz -i seeds/ -o findings/ -t 1000 -- ./target @@
On Windows (using WinAFL or wsl2 + afl):
Using wsl2 wsl afl-fuzz -i /mnt/c/seeds -o /mnt/c/findings -t 1000 -- /mnt/c/target.exe @@
RAPTOR monitors crashes and uses LLM to triage each unique crash, discarding false positives and labeling memory corruption types (heap overflow, UAF, etc.).
4. Autonomous Patch Generation and Verification
Once RAPTOR confirms a vulnerability (e.g., a SQL injection or buffer overflow), it invokes Claude‑3 or GPT‑4 to produce a minimal patch. For a Python Flask SQLi vulnerability:
Original code (`app.py`):
query = f"SELECT FROM users WHERE name = '{user_input}'"
RAPTOR’s generated patch (saved as `patch.diff`):
- query = f"SELECT FROM users WHERE name = '{user_input}'"
+ query = "SELECT FROM users WHERE name = ?"
+ cursor.execute(query, (user_input,))
Step‑by‑step verification:
- RAPTOR applies the patch and re‑runs the original exploit to confirm remediation.
- It performs regression testing using the existing test suite (or a lightweight smoke test).
- Outputs a verified patch report with CWE mapping.
Example command to manually test a patch:
git apply patch.diff pytest tests/test_security.py
RAPTOR can also generate patches for binaries using binary rewriting techniques (via LIEF or angr) – inserting hooks or NOPs to disable vulnerable instructions.
5. OSS Forensics – Tracking Vulnerabilities Across Dependencies
RAPTOR includes an OSS forensics module that scans your codebase for open‑source components, cross‑references known CVEs, and uses LLMs to determine if the vulnerable code path is reachable.
Step‑by‑step:
raptor forensics --sbom cyclonedx.json --vuln-db cve.sqlite
- Parses SBOM (CycloneDX/SPDX) or generates one via
syft packages ./. - Queries local CVE database (NVD) for each component.
- For each CVE, RAPTOR retrieves vulnerable function names and runs static analysis to check reachability.
- Generates a prioritized report with exploitability scores (LLM‑assessed).
Manual alternative on Linux:
Generate SBOM using Syft syft dir:/app -o cyclonedx-json > sbom.json Check dependencies against OSV grep -E "log4j|openssl" sbom.json
RAPTOR automates this for every build, integrating into CI/CD pipelines (GitHub Actions, Jenkins).
6. Multi‑Model Analysis Pipelines for Red Teaming
RAPTOR supports concurrent analysis with different LLMs (Ollama, Claude, GPT, Gemini) and aggregates results. This reduces model‑specific biases.
Configuration example (`raptor.yaml`):
models: - provider: ollama model: codellama - provider: anthropic model: claude-3-opus - provider: openai model: gpt-4-turbo voting: majority
Step‑by‑step:
- Run static analysis on a binary (e.g., a custom network daemon).
- Each LLM receives the same assembly and pseudocode (from Ghidra) and answers: “Vulnerability type? Offset? Exploit primitive?”
- RAPTOR aggregates via majority voting. If consensus is weak, it invokes a resolver model (usually Claude).
4. Outputs a unified report with confidence scores.
Example manual multi‑tool approach:
Run Semgrep and CodeQL separately semgrep --config p/owasp-top-ten ./src codeql database create ./db --language=cpp codeql database analyze ./db --format=sarif > codeql_out.json
RAPTOR wraps these and the LLM layer into one command: raptor analyze --target ./src --multi-model.
7. Cloud Hardening & API Security Automation
RAPTOR extends to API security by analyzing OpenAPI/Swagger specs, running fuzzing against endpoints, and using LLMs to craft authentication bypass attempts.
Step‑by‑step for AWS‑hosted API:
- Provide OpenAPI spec (
api.yaml). - RAPTOR runs static analysis on the spec for misconfigurations (missing rate limits, broken object level auth).
- It then calls `schemathesis` (API fuzzer) and feeds unexpected payloads generated by GPT.
- For cloud hardening, RAPTOR can parse IAM policies and suggest least‑privilege changes.
Linux commands to mimic part of this:
Install schemathesis pip install schemathesis schemathesis run https://api.example.com/openapi.json --hypothesis-max-examples=100 Check IAM policy with policy_sentry pip install policy_sentry policy_sentry analyze policy --input-json iam_policy.json
RAPTOR automates the loop – if it finds an IDOR vulnerability, it generates a patch and a CloudFormation snippet to restrict access.
What Undercode Say:
- Key Takeaway 1: RAPTOR demonstrates that LLMs are no longer just advisory – they actively drive the entire vulnerability research lifecycle, from discovery to patching, reducing manual effort by 70‑80%.
- Key Takeaway 2: The framework’s multi‑model voting mechanism significantly reduces false positives and model hallucination, making autonomous security research viable for production environments.
Analysis (10 lines):
RAPTOR represents a paradigm shift where AI agents coordinate specialized tools (Semgrep, AFL++, Z3) rather than replacing them. This hybrid approach ensures deterministic correctness for low‑level tasks (fuzzing, SMT solving) while leveraging LLMs for creative reasoning (exploit crafting, patch writing). The inclusion of local models (Ollama) also addresses data privacy concerns for enterprise codebases. However, risks remain – autonomous exploit generation could be abused, and LLM‑generated patches may introduce new bugs. The framework’s verification step (re‑testing exploits) partially mitigates this. For blue teams, RAPTOR automates patch validation and OSS forensics, enabling faster response. Future iterations should incorporate formal verification for generated patches. Overall, RAPTOR sets a new baseline for AI‑driven DevSecOps.
Expected Output:
RAPTOR Analysis Report – Target: ./vulnerable_app [+] Static analysis completed (Semgrep, CodeQL): 24 findings [+] LLM validation (Claude+GPT): 12 true positives - CWE-121 (Stack buffer overflow) – parse_input() at main.c:42 - CWE-89 (SQL injection) – login_handler() at api.py:15 [+] Exploit generation: 2 exploit scripts ready (./exploits/) [+] Fuzzing: 3 unique crashes, 1 new heap overflow [+] Patch generation: patches written and verified (./patches/) [+] Forensics: 4 vulnerable OSS deps (log4j 2.14.1, openssl 1.1.1k) Recommendation: Apply patches and update dependencies.
Prediction:
Within 18 months, autonomous frameworks like RAPTOR will become standard in both red and blue team toolkits, forcing defensive strategies to adopt AI‑driven monitoring and real‑time patch generation. We will likely see “LLM vs. LLM” security testing – where offensive agents compete against defensive agents. However, regulatory pressure (e.g., EU AI Act) may limit fully autonomous exploit generation, requiring human‑in‑the‑loop approval for critical systems. Open‑source clones will proliferate, democratizing advanced vulnerability research but also lowering the barrier for malicious actors. Organizations must invest in AI‑aware security governance and continuous validation of AI‑generated code.
▶️ Related Video (82% Match):
🎯Let’s Practice For Free:
IT/Security Reporter URL:
Reported By: 0xfrost Autonomous – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
