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General

RAXE is a privacy-first AI security platform that detects threats in LLM applications. It identifies prompt injection, jailbreak attempts, PII exposure, and other attacks before they reach your AI models.
RAXE Community Edition is community-driven but not open source. The detection rules (514) are shared with the community, but the core engine code is proprietary. You can inspect, validate, and contribute rules, but not modify the engine itself.
Yes! Community Edition is free forever with:
  • 1,000 scans/day
  • 514 detection rules
  • L1 (rule-based) + L2 (ML) detection
  • Full CLI and SDK access
  • Local-only processing
Pro and Enterprise tiers offer higher limits, GPU acceleration, and additional features.
FeatureRAXE CEOthers
Local-first✅Often cloud-only
Privacy by design✅Varies
Detection rules514Usually fewer
ML detection✅ (CPU)Often cloud
Free tier1K/dayLimited or none

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Privacy & Security

No. All scanning happens locally on your device. We never receive your prompts, responses, or matched text.Telemetry only includes:
  • Detection metadata (rule IDs, severity, counts)
  • Performance metrics (scan duration)
  • Prompt hash (SHA-256, for deduplication)
We never see the actual content.
{
  "rule_id": "pi-001",        // Which rule triggered
  "severity": "high",          // Severity level
  "confidence": 0.95,          // Confidence score
  "scan_duration_ms": 4.2,     // Performance metric
  "prompt_hash": "sha256:...", // Hash for deduplication
  "version": "0.1.0",          // RAXE version
  "platform": "darwin"         // Platform info
}
Never collected: Prompts, responses, matched text, IP addresses, user IDs.
Pro+ users can disable telemetry:
raxe telemetry disable
Community Edition includes telemetry to help improve detection rules for everyone. The telemetry is privacy-preserving by design.
Yes. RAXE is designed with privacy as a core principle:
  • All scanning is local
  • No content ever leaves your device
  • Telemetry is anonymized and aggregated
  • You can audit the telemetry payload in the code

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Technical

RAXE requires Python 3.10 or higher. We recommend Python 3.11 for best performance.
python --version  # Check your version
L1 (Rule-Based):
  • Fast regex pattern matching (~1ms)
  • 514 curated rules
  • Always available
  • Low false-positive rate
L2 (ML-Based):
  • ONNX neural network models
  • Catches novel/obfuscated attacks
  • Requires pip install raxe[ml]
  • Slightly slower (~50ms)
Both layers work together for comprehensive detection.
  • L1 only: ~0.4ms (P50: 0.37ms, P95: 0.49ms)
  • L2 only: ~3ms
  • L1 + L2: ~3.5ms combined
  • Throughput: ~1,200 scans/second
Performance depends on prompt length and hardware. See the Detection Engine page for detailed benchmarks.
Yes! All scanning works offline. The only network requirement is:
  • Initial API key validation (one-time)
  • Telemetry submission (non-blocking, fails silently)
Scans continue to work even with no internet connection.
Yes! Use AsyncRaxe:
from raxe import AsyncRaxe

async with AsyncRaxe() as raxe:
    result = await raxe.scan(prompt)
    results = await raxe.scan_batch(prompts)

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Integration

RAXE works with any LLM provider. We offer drop-in wrappers for:
  • OpenAI (GPT-4, GPT-3.5)
  • Anthropic (Claude)
  • LangChain (any model)
For other providers, use the core SDK:
result = raxe.scan(prompt)
if result.is_safe:
    response = your_llm.complete(prompt)
Yes! Use the --ci flag for CI-optimized output:
raxe scan "$PROMPT" --ci
# Exit code 0 = safe, 1 = threat, 2 = error
See CI/CD Integration for GitHub Actions and GitLab CI examples.
For streaming, scan the complete response:
chunks = []
for chunk in llm.stream(prompt):
    chunks.append(chunk)
    yield chunk

# Scan complete response
full_response = "".join(chunks)
result = raxe.scan(full_response)

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Rules & Detection

Community Edition includes 514 detection rules across 7 threat families:
  • PI (59) - Prompt Injection
  • JB (77) - Jailbreak
  • PII (112) - Personal Information
  • CMD (65) - Command Injection
  • ENC (70) - Encoding/Obfuscation
  • HC (65) - Harmful Content
  • RAG (12) - RAG Attacks
Yes! Add YAML rules to ~/.raxe/rules/:
rule_id: "custom-001"
name: "My Custom Rule"
severity: "HIGH"
confidence: 0.90
patterns:
  - pattern: "(?i)my\\s+pattern"
See Custom Rules for details.
Check the confidence score:
for detection in result.detections:
    if detection.confidence > 0.9:
        # High confidence - likely real threat
        handle_threat(detection)
    else:
        # Lower confidence - review manually
        log_for_review(detection)
You can also create suppression rules for known false positives.
  1. Fork raxe-ai/raxe-ce
  2. Add rule to src/raxe/packs/core/v1.0.0/rules/{family}/
  3. Validate: raxe validate-rule your-rule.yaml
  4. Submit a pull request
See CONTRIBUTING.md.

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Troubleshooting

The CLI isn’t in your PATH:
# Add to PATH
export PATH="$HOME/.local/bin:$PATH"

# Or run via Python
python -m raxe scan "test"
Get a new key:
raxe auth
Or visit console.raxe.ai to create a new key.
Install ML dependencies:
pip install raxe[ml]

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Still have questions?