What is AI agent security?

AI agent security protects autonomous AI agents from threats like prompt injection, jailbreaks, and data exfiltration. RAXE provides runtime security that monitors every stage of the agent lifecycle—from user input to tool execution to response output.

What is an LLM firewall?

An LLM firewall inspects and filters inputs and outputs to large language models, blocking malicious prompts and preventing data leaks. Unlike cloud-based solutions, RAXE runs locally with <10ms latency and zero data exfiltration.

How does prompt injection detection work?

RAXE uses 5 detection layers: L1 Pattern Rules for known attack signatures, L2 On-Device ML classifiers, L3 Statistical Analysis, L4 SLM Judge (optional), and L5 Memory Correlation for multi-turn attacks. All detection runs locally in under 10ms.

AI Agent Security
& LLM Firewall

Block prompt injection, tool misuse, and data exfiltration—before they reach your LLM.
Runs locally. On-device, on-prem, or in your VPC. Your prompts never leave.

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100% Local

<10ms

CPU-First

SOC 2 In Progress

No Data Leaves

OWASP Aligned

Every Runtime Needs Its Security Layer

62% of enterprises deploy agents. <0.1% have runtime security.

NIDS/IPS → Network runtime

WAF → Application runtime

EDR → Endpoint runtime

RAXE → Agent runtime

Observe. Classify. Enforce. All locally.

Agents Are the Fastest-Growing Attack Surface

Single compromised agent = enterprise-wide exposure Agents connect to code, tickets, email, databases. One breach cascades everywhere.

Prompt injection is OWASP #1 for LLMs Attackers hijack agent behavior through malicious inputs to exfiltrate data and bypass guardrails.

WAF/EDR/NIDS have zero visibility Existing tools monitor network, endpoints, apps—not what entered the agent or what actions it took.

Cloud scanning exfiltrates your data Sending prompts to cloud APIs exposes PII, violates data residency, and adds 800-900ms latency.

Defense in Depth. Enforced Locally.

5-layer detection at every stage of the agent lifecycle—no prompts ever leave your boundary.

Coming Soon

See RAXE in Action 2 min explainer

User Input

Prompt Analysis

→

Agent Reason

Action Analysis

→

Tools Execute

Policy Check

→

Memory Store

Memory Analysis

→

Response

Output Analysis

5 Detection Layers

L1 Pattern Rules

L2 On-Device ML

L3 Statistical Analysis

L4 SLM Judge Optional

L5 Memory Correlation

No prompt exfiltration—detection inside your boundary

CPU-optimized int8 classifiers for sub-10ms P99 latency

Policy tuning without re-platforming—security configures, devs don't change code

Get Started

Developers

pip install raxe

Free forever. Source available. Full transparency.

View on GitHub

Security & Compliance

Auditability, data residency, SIEM exports, and enforceable controls for regulated industries.

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AI Agent Security & LLM Firewall