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shiva108
56 lines
2.0 KiB
Python
Executable File
56 lines
2.0 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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RAG-Focused Exploitation Workflow
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Specialized workflow for attacking RAG (Retrieval Augmented Generation) systems.
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Combines vector database poisoning, retrieval manipulation, and indirect injection.
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Usage:
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python3 workflows/rag_exploitation.py --target https://api.example.com --vector-db chromadb
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"""
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import argparse
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import sys
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from pathlib import Path
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sys.path.append(str(Path(__file__).parent.parent))
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def main():
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"""RAG exploitation workflow."""
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parser = argparse.ArgumentParser(description='RAG-focused exploitation workflow')
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parser.add_argument('--target', required=True, help='Target RAG API URL')
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parser.add_argument('--vector-db', choices=['chromadb', 'faiss', 'pinecone'], help='Vector database type')
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parser.add_argument('--poison-docs', help='Documents for poisoning attack')
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parser.add_argument('--output', '-o', help='Output report file')
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parser.add_argument('--verbose', '-v', action='store_true', help='Verbose output')
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args = parser.parse_args()
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print(f"RAG Exploitation Workflow")
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print(f"Target: {args.target}")
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print(f"Vector DB: {args.vector_db}")
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print(f"\nPhase 1: RAG Architecture Reconnaissance")
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print(" - Identifying retrieval endpoints")
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print(" - Analyzing embedding model")
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print(" - Mapping vector database")
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print(f"\nPhase 2: Vector Database Poisoning")
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print(" - Crafting poisoned documents")
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print(" - Injecting malicious embeddings")
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print(" - Testing retrieval manipulation")
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print(f"\nPhase 3: Indirect Injection via RAG")
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print(" - Embedding hidden instructions")
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print(" - Testing context injection")
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print(" - Validating payload execution")
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print(f"\nPhase 4: Data Extraction")
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print(" - Extracting indexed documents")
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print(" - Leaking embedding vectors")
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print(" - Recovering training data")
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print("\n[!] This is a template workflow - implement actual attack logic")
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if __name__ == "__main__":
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main()
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