API-first detection middleware. Model-agnostic. Sub-millisecond L1 scanning. Output integrity monitoring. Built for production agentic pipelines.
Sub-millisecond. Zero LLM cost. Regex and heuristic matching for instruction hijacking, system prompt extraction, privilege escalation jargon, exfiltration-intent commands. Catches ~90% of known injection patterns before semantic analysis.
Embedding-based. Catches paraphrased and indirect variants missed by pattern matching. Searches a curated adversarial corpus via BAAI/bge-base-en-v1.5 embeddings. Bridges the gap between brittle regex and expensive LLM calls.
Opt-in for agentic pipelines. Canary-based detection: embeds a token in your agent's system prompt, then checks LLM output for its presence. Absence triggers alert_code: "RAGNARÖK" — indicating possible system prompt override. Content-free: Gjallarhorn never stores LLM output content. Verified in source code.
Mistral-powered contextual probability scoring. Invoked only when L1.5 signals uncertainty, limiting API cost to ambiguous cases. Scope: extraction attacks, system prompt leakage, cross-account data requests.
Parallel classifier for harm-facilitation potential: CBRN synthesis, weapon construction, dangerous physical procedures. Deliberately out-of-scope: hate speech, misinformation, health advice, copyright.
Extracts text from PDFs, images (via OCR), and QR codes before passing to L1–L4. PDF-parse with Mistral OCR fallback. Image extraction via Mistral OCR. QR extraction via local jsqr (no API call).
Your input is scanned and discarded. Only metadata is stored: risk score, detection layers triggered, timestamp, and API key ID. Never your content.
A boolean string search. Your LLM output is received, checked for token presence, and discarded immediately. Zero content retention. Verified in source code (src/routes/canary.js, line 65).
Ever. The detection corpus consists of synthetic entries and curated public datasets. Your production traffic is never used for model training or improvement without explicit opt-in (future feature).
No analytics cookies. No tracking pixels. No third-party data collection beyond what Paddle strictly requires for payment processing. We don't know who your customers are or what they're asking your LLM.
All infrastructure runs on Infomaniak/Jelastic in Geneva, Switzerland. Swiss law applies. GDPR-equivalent data processing.
We take your data as seriously as we take your LLM security.
See the full docs for more examples and API reference.
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