detecting-ai-model-prompt-injection-attacks

# Detecting AI Model Prompt Injection Attacks ## When to Use - Scanning user inputs to LLM-powered applications before they are forwarded to the model - Building an input validation layer for chatbots, AI agents, or retrieval-augmented generation (RAG) pipelines - Monitoring logs of LLM interactions to retrospectively identify prompt injection attempts - Evaluating the effectiveness of existing prompt injection defenses through red-team testing - Classifying prompt injection payloads during security incident investigations involving AI systems **Do not use** as the sole defense mechanism against prompt injection -- always combine with output validation, privilege separation, and least-privilege tool access. Not suitable for detecting jailbreaks that do not involve injection of adversarial instructions. ## Prerequisites - Python 3.10+ with pip for installing detection dependencies - The `transformers` and `torch` libraries for running the DeBERTa-based classifier model - The `protectai/deberta-v3-base-prompt-injection-v2` model from Hugging Face (downloaded on first run, approximately 700 MB) - Network access to Hugging Face Hub for initial model download (offline mode supported after first download) - Sample prompt injection payloads for testing (the script includes a built-in test suite) ## Workflow ### Step 1: Install Detection Dependencies Install the required Python packages for all three detection layers: ```bash pip install transformers torch sentencepiece prot...