Key features
- Canary tokens: APAC system prompt exposure detection via embedded secrets
- Vector database: APAC organization-specific injection pattern memory
- LLM detection: semantic APAC injection analysis for novel attack patterns
- Three-layer defence: APAC configurable heuristic + vector + LLM pipeline
- Python SDK: APAC integration with LangChain and custom LLM pipelines
- Open-source: self-hosted APAC deployment for data sovereignty
Best for
- APAC developers building LLM agents and applications with sensitive system prompts who need prompt injection detection — particularly APAC teams where system prompt exposure would reveal proprietary APAC business logic or bypass AI safety guardrails.
Limitations to know
- ! Latency overhead per APAC request depending on detection layers enabled
- ! Canary token approach may not catch all APAC injection variants
- ! Smaller APAC community than LLM Guard — fewer APAC examples and support resources
About Rebuff
Rebuff is a prompt injection detection framework purpose-built for LLM applications — using three detection layers (heuristic rules, LLM-based semantic detection, and canary token embedding) to identify when APAC users are attempting to override system prompts or inject malicious instructions into LLM applications. APAC developers building AI agents and LLM-powered applications use Rebuff to protect system prompts from override attacks.
Rebuff's canary token approach embeds unique secret tokens in system prompts — if a prompt injection attack succeeds in extracting the system prompt, the canary tokens appear in the APAC LLM response, triggering a detected injection alert. This canary layer catches injections that bypass heuristic and LLM-based detection by confirming whether the system prompt was actually revealed.
Rebuff's vector database memory stores previously detected APAC prompt injection patterns — when a new APAC input arrives, Rebuff checks it against the injection pattern database using semantic similarity. APAC teams running production applications accumulate an APAC organization-specific database of injection attempts, improving detection over time as more APAC attacks are observed.
Rebuff's three-layer detection provides configurable trade-offs for APAC security vs performance: heuristic rules (fastest, lowest accuracy), vector database check (fast, good accuracy), and LLM-based detection (slowest, highest accuracy). APAC teams can enable only the layers appropriate for their APAC latency budget and security requirements.
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