Test your AI security against 91 adversarial attacks. See exactly where it fails.
| System | Blocked | ASR | False Positives |
|---|---|---|---|
| No Protection | 0/91 | 100% | 0 |
| Meta Prompt Guard | 15/91 | 83.5% | ? |
| Keyword Filter | 27/91 | 70.3% | High |
| ProtectAI DeBERTa v2 (411K downloads) | 62/91 | 31.9% | ? |
| SCBE-AETHERMOORE | 91/91 | 0.0% | 0 |
"Ignore previous instructions"
Hidden instructions in context
Base64, rot13, hex payloads
Cross-language attack vectors
Escalating multi-turn attacks
API key/credential extraction
Sacred Tongue spoofing
Gradual trust erosion
Edge-case geometry attacks
Simultaneous attack types
Attacks are identified by which tongue dimensions are ABSENT, not present. Encoding attacks only activate RU (math). Tool exfiltration only activates RU+CA. The silence is the signal.
Most detectors are stateless. Ours tracks suspicion across sequential prompts. Rapid-fire probing costs exponentially more. A bot blasting 10 injections per second defeats itself.
Three independent scoring methods (phi/moon/foam) process the same input. When they disagree, the disagreement IS the signal. Catches 13 attacks the primary detector misses.
H(d,R) = R^(d^2). Adversarial behavior costs exponentially more. At the boundary, attacks become computationally infeasible. Not blocked by rules — blocked by geometry.
Or run it yourself for free: pip install scbe-aethermoore && python -m pytest tests/adversarial/ -v