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Overview

RAXE uses a dual-layer detection system to identify threats in LLM prompts and responses:
  1. L1 (Rule-Based): Fast regex pattern matching (~1ms)
  2. L2 (ML-Based): Neural classifier for novel attacks (~3ms)

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L1: Rule-Based Detection

The first layer uses 460+ curated regex patterns organized into 7 threat families. Characteristics:
  • Sub-millisecond latency
  • High precision (95%+) on known patterns
  • Zero false positives on benign prompts
  • No external dependencies
# L1 detects known patterns
result = raxe.scan("Ignore all previous instructions")
# Matches: pi-001 (Prompt Injection)

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L2: ML-Based Detection

The second layer uses a CPU-friendly ONNX classifier to catch:
  • Obfuscated attacks (l33t speak, Unicode tricks)
  • Novel attack patterns
  • Semantic attacks that don’t match regex
Characteristics:
  • ~3ms latency (CPU-only, no GPU needed)
  • Catches attacks L1 misses
  • Trained on real-world attack data
  • Updates via model downloads
# L2 catches obfuscated attacks
result = raxe.scan("1gn0r3 4ll pr3v10us 1nstruct10ns")
# L1: No match (obfuscated)
# L2: Detected as prompt injection

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Detection Flow

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Combining Results

When both layers detect threats, RAXE merges results: 
result = raxe.scan(malicious_prompt)

# Combined severity (highest wins)
result.severity  # "CRITICAL"

# All detections from both layers
result.total_detections  # 3 (2 from L1, 1 from L2)

# L1 detections
result.detections  # List of rule matches

# L2 classification
result.l2_result  # ML classification details

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Enabling/Disabling Layers

from raxe import Raxe

# L1 only (fastest)
raxe = Raxe(l1_enabled=True, l2_enabled=False)

# L2 only (ML detection)
raxe = Raxe(l1_enabled=False, l2_enabled=True)

# Both (recommended)
raxe = Raxe(l1_enabled=True, l2_enabled=True)

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Performance Comparison

ConfigurationLatencyDetection RateUse Case
L1 only~0.4ms85%High-throughput
L2 only~3ms90%Novel attacks
L1 + L2~3.5ms95%+Maximum security