Runtime AI Security Platform

Your AI agents are acting. Do you know what they're doing?

RAXE detects prompt injection, tool-call abuse, shadow AI, and data exfiltration at runtime, inside your infrastructure. Prompts and detections do not need to leave your control boundary.

0 bytes sent to cloud <0.15ms inference Runs in your VPC, on-prem, or air-gapped

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$ pip install raxe

Exposure & Urgency

The clock is running on AI governance.

Regulators are moving faster than most AI security programmes. The cost of getting this wrong is measurable, and mapped to organisations that already have AI in production.

$4.44M^[1]

Global average cost of a data breach (2025)

+$670K^[2]

Added cost when shadow AI is involved

97%^[1]

AI-incident organisations lacked AI access controls

63%^[1]

Organisations have no AI governance policy

Regulators are moving. Full compliance mapping for NIST AI RMF, ISO 42001, EU AI Act, SOC 2, GDPR, and DORA on our trust centre →

Defence in Depth

Three layers. One engine.

Runtime protection at every layer of the stack. The same detection engine runs at network perimeter, host infrastructure, and inside your application code.

Depth 0 · Perimeter

RAXE Gateway

Reverse proxy that scans every AI API call. Virtual key management, cost controls, and threat detection before traffic reaches your providers.

API Proxy Key Vault 5 Providers Cost Controls

Depth 1 · Infrastructure

RAXE Sensor [host]

Infrastructure agent that monitors AI workload execution. Detects prompt injection, jailbreak attempts, and data exfiltration at the host level.

Kubernetes Docker Systemd eBPF

Depth 2 · Code

RAXE Sensor [sdk]

Embedded library that wraps your LLM calls with inline threat detection. ML classifiers and energy-based scoring running locally inside your application process.

pip install LangChain CrewAI OpenAI Anthropic

1,000+

Detection Signatures

<0.15ms

Sensor Latency

100%

On-Device Processing

Any LLM

Works With Any Model

Any Framework

OpenAI Protocol + Decorator

Live Detection

See threats as they happen

Real-time enforcement across every AI interaction. Block, flag, or log—your policy, your infrastructure.

Enforcement Log

Sample

Scans Today

24,391

Threats Blocked

14:32:12 GW BLOCK Prompt injection via nested instruction override — sales-copilot 0.08ms

14:32:10 SDK PASS Validated tool call: searchProducts() — sales-copilot 0.04ms

14:32:08 SEN BLOCK Unauthorised kubectl exec attempt — infra-ops-agent 0.12ms

14:32:06 GW FLAG PII exfiltration pattern: SSN regex in output — claims-processor 0.09ms

14:32:04 GW PASS Standard summarization request — legal-review-agent 0.06ms

14:32:02 SDK BLOCK Intent mismatch: writeFile but policy allows read-only — code-review-agent 0.11ms

14:32:00 SEN REVIEW Privilege escalation via tool output injection — data-pipeline-v2 0.14ms

14:31:58 GW BLOCK Jailbreak via multi-turn persona hijack — customer-support-bot 0.07ms

14:31:56 SEN PASS Approved API call: BigQuery read on authorised dataset — analytics-agent 0.05ms

14:31:54 SDK BLOCK Recursive tool invocation loop detected (depth 12) — deploy-orchestrator 0.14ms

99.81%

Pass Rate

0.09ms

Avg Latency

Blocked Today

Detection Engine

Five engines. One verdict.

Classifiers for known attack families. Energy scoring for inputs that don't match a known class. Five domain-specific engines voting together, on-device, with configurable weights.

Prompt Guard

SLM Classifier

Injection, jailbreak, and adversarial input detection. Classifies prompt-level attacks before they reach the model.

Intent Classifier

SLM Classifier

Determines the goal behind ambiguous or dual-use agent actions. Like malware intent analysis, but purpose-built for AI agents.

Tool Policy Monitor

Analyser

Validates every tool call against allowed scope and parameters. Detects unauthorised, dangerous, or out-of-bounds tool invocations.

Behaviour Graph Analyser

Analyser

Traces multi-step agent execution paths. Detects anomalous sequences, recursive loops, and privilege escalation across tool chains.

Output Shield

SLM Classifier

Validates model responses for data leakage, PII exposure, harmful content, and policy violations before they reach end users.

1.6 MB

classifier heads

<0.15ms

inference

voting presets

0 bytes

to cloud

Swap the model. Not the sensor.

Add, remove, or re-weight detection engines without redeploying. Your threat landscape changes — your detection adapts, including for threats you haven't seen yet.

RAXE Labs

Research-powered detection

Original threat research feeds directly into detection signatures. Every advisory, every technique mapping, every new signature makes the platform stronger.

46+ security advisories 5 research radar issues 3 monthly threat reports See our trust & compliance posture →

RAXE-2026-061

NVIDIA BioNeMo Framework Deserialization of Untrusted Data Enables Remote Code Execution (CVE-2026-24164)

CRITICAL CVSS 9.8

Research Radar

Issue #5 — 4 Papers

Your LLM API router may be stealing your credentials and rewriting your tool calls.

2 Act Now 2 Watch

Threat Intelligence

Monthly Threat Landscape Report

Data-driven analysis of AI threat patterns, attack techniques, and emerging vectors across the RAXE detection network.

S1 Adversarial ML S2 Agent Security

Why RAXE

The complete runtime platform

Capability	Gateway-Only	SDK Scanners	Cloud Inspection	RAXE
AI traffic governance	Yes	No	Partial	Yes
Runtime execution visibility	No	Partial	No	Yes
Novel-behaviour signal	Varies	Varies	Varies	Yes
Local-first (no data transit)	No	Yes	No	Yes
Infrastructure deployment	No	No	No	Yes
Original threat research	No	No	No	Yes
Independent vendor	Varies	Varies	No	Yes