What makes AI agents a distinct attack surface?

An AI agent holds credentials, calls tools, and acts on instructions that arrive as untrusted text. That combination means a successful prompt injection is not just a bad answer — it is an authenticated action taken on the attacker's behalf, using permissions the agent legitimately holds.

Can you detect prompt injection?

AMaze™ detects the consequences rather than trying to classify the prompt. Synthetic tools, credentials, and data sources are exposed to the agent's environment; a model that has been steered into exfiltration or privilege escalation reaches for them, and that reach is a confirmed signal.

Does this work with RAG pipelines?

Yes. Synthetic documents placed in a retrieval corpus are never a legitimate answer to a real user question, so retrieval of one indicates either an injection attempt or an adversary probing what the pipeline can reach.

Does it slow down the model or sit inline?

No. Nothing is placed between the user and the model, so inference latency is unaffected. Detection comes from what the agent's environment contains, not from inspecting each request.

Proactive Defense for AI Agents & LLMs

Your chatbots, AI agents, and RAG pipelines are a new attack surface. AMaze extends proactive cyber resilience to AI, engaging jailbreaks, prompt injection, and data exfiltration with AI-SCAs before they reach your models, data, or tools, every attempt confirmed on the same unified attack chain as IT and OT.

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AI agents and LLMs defended by MirrorMire

Confirmed adversary interaction with synthetic AI surfaces

AI surfaces don't behave like traditional infrastructure. A chatbot, an agentic or MCP stack, or a RAG pipeline can be talked into leaking secrets, calling tools it shouldn't, or revealing its own context, without ever tripping a network alarm.

Traditional security can't see these attacks. Prompt injection, jailbreaks, and exfiltration happen inside the model conversation, where firewalls and EDR have no visibility.

AMaze turns AI into defensible ground. AI-SCAs and Neural Echoes mirror real AI tools, data, and context, so any attempt to jailbreak, exfiltrate, or probe is a confirmed adversary, mapped to OWASP LLM Top-10 and MITRE ATLAS, and correlated onto the same unified attack chain as your IT and OT.

How AMaze Defends AI

Deploy AI defense the way that fits your stack, in front of an endpoint, inside your agent, or alongside your MCP tools, with no change to how your models are built. Across all three, jailbreaks meet a deterministic refusal and exfiltration attempts are answered with fabricated, canary-tagged credentials, keys, and records.

MCP Tool Guard

Synthetic MCP tools and resources sit alongside your agentic stack. No legitimate workflow has reason to call them, so any invocation is a high-fidelity signal of a hostile agent or operator.

LLM Proxy Sidecar

Sits in front of any OpenAI-compatible endpoint to refuse jailbreaks, catch exfiltration prompts, and scan responses for leaked secrets, turning every attempt into a confirmed signal.

Embeddable SDK

Drop guard and response-scanning calls directly into your own agent, so AI defense travels with your application wherever it runs.

Canary-Based Proof

Exfiltration attempts are answered with fabricated, canary-tagged credentials, keys, and records. A resurfaced canary is unambiguous, session-attributed proof — courtroom-grade evidence, not a suspicion score — mapped to OWASP LLM Top-10 and MITRE ATLAS.

See prompt abuse caught in the act

A synthetic AI copilot mirrors your real assistant. When an adversary attempts prompt injection or tries to exfiltrate data through it, AMaze maps the attempt to the OWASP LLM Top 10 and treats it as a confirmed adversary interaction — not a false positive to triage.

AMaze detecting an LLM prompt-injection and jailbreak attempt via a synthetic AI copilot

See AMaze in action

Common questions

What makes AI agents a distinct attack surface?: An AI agent holds credentials, calls tools, and acts on instructions that arrive as untrusted text. That combination means a successful prompt injection is not just a bad answer — it is an authenticated action taken on the attacker's behalf, using permissions the agent legitimately holds.
Can you detect prompt injection?: AMaze detects the consequences rather than trying to classify the prompt. Synthetic tools, credentials, and data sources are exposed to the agent's environment; a model that has been steered into exfiltration or privilege escalation reaches for them, and that reach is a confirmed signal.
Does this work with RAG pipelines?: Yes. Synthetic documents placed in a retrieval corpus are never a legitimate answer to a real user question, so retrieval of one indicates either an injection attempt or an adversary probing what the pipeline can reach.
Does it slow down the model or sit inline?: No. Nothing is placed between the user and the model, so inference latency is unaffected. Detection comes from what the agent's environment contains, not from inspecting each request.

Defend Your AI Before Adversaries Reach It

See how AI-SCAs bring proactive cyber resilience to chatbots, agents, and RAG, on the same unified attack chain as IT and OT.

Request a Demo

Proactive Defense for AI Agents & LLMs