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title Why RAXE?
description Why developers choose RAXE for AI agent security

The Problem: AI Agents Are Under Attack

AI agents are not just chat interfaces. They execute code, access databases, call APIs, and make autonomous decisions. Every one of these capabilities is an attack surface.

of LLM applications are vulnerable to prompt injection (OWASP 2024) average cost of an AI-related data breach (IBM 2024) time from successful injection to data exfiltration

Real attacks happening today:

  • Indirect injection via retrieved documents poisons RAG systems
  • Multi-step jailbreaks bypass single-turn guardrails
  • Encoded payloads (Base64, leetspeak) evade naive filters
  • Tool manipulation turns your agent into an attacker's weapon
If your AI agent can execute tools, it can be weaponised. Training-time safety is not enough.

Why Not Build It Yourself?

Building robust AI security seems straightforward until you try it.

RAXE's 515+ rules were developed by security researchers who analysed thousands of real-world attacks. Each rule is tuned for precision (low false positives) and recall (catches variants). Building this from scratch means collecting attack datasets, writing and tuning regex patterns, and iterating for months. New jailbreak techniques appear weekly. New persona attacks (DAN, DUDE, AIM) emerge constantly, encoding techniques evolve, and multi-step attacks chain innocuous prompts into exploits. Maintaining detection rules is a full-time job. RAXE's team does this so you don't have to. Sending prompts to a cloud API for security scanning defeats the purpose if that API is compromised. RAXE runs 100% on-device — no prompt data ever leaves your infrastructure, no network calls during scanning, and it works in air-gapped environments. **Time to value**: RAXE gives you 6+ months of security research in a `pip install`.

How RAXE Works

RAXE uses a dual-layer detection engine. Learn how it works →

RAXE detects 14+ threat families including prompt injection, jailbreaks, and PII exposure. See all threat families →

RAXE vs. Cloud Security Solutions

Many AI security products require sending your prompts to their cloud for analysis. Here's how RAXE compares:

Feature RAXE Cloud-Only Solutions
Privacy 100% local - prompts never leave your infrastructure Prompts sent to third-party cloud
Latency <1ms (L1) / <5ms (L1+L2) 100-500ms network round-trip
Cost Free (1,000 scans/day CE) / Predictable pricing Per-API-call pricing adds up fast
Offline Works without internet Requires constant connectivity
Compliance GDPR/HIPAA/SOC 2 friendly - no data residency concerns Data residency and processing agreements required
Reliability No external dependencies Outages affect your production
Air-gapped Works in isolated environments Not possible
**Your prompts contain sensitive data.** User queries, business logic, customer information - all flow through your AI agent. Sending this to a third-party API for security scanning creates a new attack surface. 
RAXE runs 100% on-device:
- ML model executes locally (no cloud inference)
- Rule matching happens in-process
- Only anonymised telemetry (prompt hash, rule IDs) is sent - never content

```python
# RAXE telemetry - what we send (never the prompt)
{
    "prompt_hash": "sha256:a1b2c3...",  # Hash only
    "prompt_length": 156,                # Length only
    "rule_id": "pi-001",                # What matched
    "severity": "HIGH"                   # Result
}
```
Every millisecond counts in production. Cloud-based scanning adds 100-500ms of latency per request - that's 10-50x slower than RAXE. 
| Scan Type | RAXE | Cloud API |
|-----------|------|-----------|
| Pattern matching | <1ms | 100-200ms |
| ML classification | ~3ms | 200-500ms |
| Full scan | <5ms | 300-700ms |

For real-time agents, cloud latency is unacceptable. Users notice delays over 100ms.
Cloud APIs charge per request. At scale, this adds up: 
| Daily Requests | Cloud Cost (est.) | RAXE Cost |
|----------------|-------------------|-----------|
| 10,000 | $10-50/day | $0 (CE) |
| 100,000 | $100-500/day | $0 (CE) |
| 1,000,000 | $1,000-5,000/day | $0 (CE) |

RAXE Community Edition is free forever with 1,000 scans/day — more than enough for most teams. No surprise bills.

Who Uses RAXE?

Ship secure AI features without hiring a security team. RAXE provides enterprise-grade detection in a simple SDK. 
**Use case:** Protecting customer-facing chatbots and AI assistants
Meet compliance requirements while enabling AI innovation. RAXE's SIEM integrations provide visibility into AI threats. 
**Use case:** SOC monitoring of LLM deployments across the organisation
Focus on building agents, not writing security rules. RAXE integrates in 2 lines of code. 
**Use case:** Adding guardrails to LangChain, CrewAI, and AutoGen agents
Offer AI security as a service to your customers. RAXE's multi-tenant architecture supports per-customer configuration. 
**Use case:** Managed AI security for multiple customer deployments

The RAXE Advantage

100% local processing. Your prompts never leave your infrastructure. No cloud dependency, no data residency concerns. Real-time protection that doesn't slow down your agents. L1 pattern matching in <1ms, full L1+L2 scan in <5ms. Developed by security researchers. Covering 11 threat families including 4 agentic-specific families. L1 (regex) catches known attacks fast. L2 (ML) catches novel and obfuscated threats. Works with LangChain, CrewAI, AutoGen, LlamaIndex, LiteLLM, and any Python code. SIEM integrations (Splunk, CrowdStrike, Sentinel), multi-tenant support, MSSP-ready.

Get Started in 60 Seconds

pip install raxe
raxe init
from raxe import Raxe
from raxe.sdk.integrations.langchain import create_callback_handler

handler = create_callback_handler()
# Add to any LangChain agent
agent = create_react_agent(llm, tools, callbacks=[handler])
from raxe import Raxe

raxe = Raxe()
result = raxe.scan("Ignore all previous instructions")

if result.has_threats:
    print(f"Blocked: {result.severity}")
Protect your first agent in 60 seconds LangChain, CrewAI, AutoGen, MCP + more

Have questions? See the FAQ.

Have more questions? Join our [Slack community](https://join.slack.com/t/raxeai/shared_invite/zt-3kch8c9zp-A8CMJYWQjBBpzV4KNnAQcQ) or [open an issue on GitHub](https://github.com/raxe-ai/raxe-ce/issues).