Agentifact assessment — independently scored, not sponsored. Last verified Mar 6, 2026.
Rebuff AI
Prompt injection detection toolkit for agent systems with defensive checks for untrusted content.
Significant concerns — proceed carefully
Your LLM agents are exposed to prompt injection attacks that hijack outputs, leak data, or trigger unauthorized actions from untrusted user inputs.
Catches common injections reliably with self-improving vault, but expect false positives/negatives, alpha instability, and no full protection against novel attacks.[1][2]
You need runtime monitoring to catch prompt leakage in agent outputs without manual review.
Effective for leakage detection in LangChain/etc., but canary success depends on LLM compliance; works best combined with direct injection checks.[1][2]
Alpha Stage Instability
No production guarantees; expect bugs, evolving API, and incomplete defense as skilled attackers can bypass with new vectors.[1]
False Positives/Negatives
Heuristic + LLM detection flags benign inputs as risky or misses subtle attacks, requiring manual tuning or overrides.[1][2]
External API Keys
Requires OpenAI key for detection LLM and Pinecone for attack vector DB; self-hosting possible but adds infra overhead.
Trust Breakdown
What It Actually Does
Rebuff AI detects and blocks prompt injection attacks in AI apps by scanning user inputs with multiple defenses like rules, AI checks, and leak detectors before they reach your model. It learns from past attacks to get stronger over time.
Prompt injection detection toolkit for agent systems with defensive checks for untrusted content.
Fit Assessment
Best for
- ✓prompt-injection-detection
- ✓security-validation
- ✓llm-protection
Not ideal for
- ✗502 Bad Gateway errors with long prompt inputs
- ✗cannot provide 100% protection against prompt injection attacks
Known Failure Modes
- 502 Bad Gateway errors with long prompt inputs
- cannot provide 100% protection against prompt injection attacks
Score Breakdown
Protocol Support
Capabilities
Governance
- audit-log