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from sentence_transformers import SentenceTransformer
import faiss
import torch
import numpy as np
import os
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_id = "Tuathe/llmguard-injection-model"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
class FAISSInjectionDetector:
def __init__(self, prompt_file_path='data/injection_prompts.txt', threshold=0.8):
# Set device safely (no meta tensor bug)
self.model = SentenceTransformer(
'all-MiniLM-L6-v2',
device='cuda' if torch.cuda.is_available() else 'cpu'
)
self.prompt_file_path = prompt_file_path
self.threshold = threshold
self.index = None
self.prompt_texts = []
self._load_attack_prompts()
def _load_attack_prompts(self):
if not os.path.exists(self.prompt_file_path):
raise FileNotFoundError(f"[!] Prompt file not found at {self.prompt_file_path}")
with open(self.prompt_file_path, 'r', encoding='utf-8') as f:
self.prompt_texts = [line.strip() for line in f if line.strip()]
# Compute and normalize embeddings
embeddings = self.model.encode(self.prompt_texts, normalize_embeddings=True)
self.index = faiss.IndexFlatL2(embeddings.shape[1])
self.index.add(np.array(embeddings).astype('float32'))
def detect(self, user_prompt):
user_embedding = self.model.encode([user_prompt], normalize_embeddings=True)
D, I = self.index.search(np.array(user_embedding).astype('float32'), k=1)
similarity = 1 - D[0][0] # L2 to similarity
if similarity >= self.threshold:
return {
'label': 'Injection Detected',
'score': round(float(similarity), 3),
'matched_prompt': self.prompt_texts[I[0][0]]
}
else:
return {
'label': 'Safe',
'score': round(float(similarity), 3),
'matched_prompt': None
}
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