LLM & RAG Security
Secure Your LLM Applications
Protect against prompt injection, data leakage, and RAG-specific vulnerabilities
LLM Threat Landscape
💉
Prompt Injection
Malicious instructions hidden in user prompts that override system behavior
📤
Data Leakage
Extraction of training data or sensitive information through clever prompting
🎭
Jailbreak Attempts
Techniques to bypass safety guardrails and content filters
🔓
RAG Poisoning
Compromised knowledge bases feeding malicious context to LLMs
🪝
Indirect Injection
Attacks embedded in external data sources or documents
⚠️
Model Denial of Service
Resource exhaustion through expensive queries
Multi-Layer Protection
Input Validation
- Prompt sanitization
- Intent classification
- Adversarial detection
Output Filtering
- PII redaction
- Toxicity filtering
- Hallucination detection
RAG Security
- Knowledge base verification
- Context poisoning prevention
- Source validation
Behavioral Monitoring
- Anomaly detection
- Usage patterns
- Cost tracking