Llm Security

---
name: llm-security
description: Security guidelines for LLM applications based on OWASP Top 10 for LLM 2025. Use when building LLM apps, reviewing AI security, implementing RAG systems, or asking about LLM vulnerabilities like "prompt injection" or "check LLM security".
---

# LLM Security Guidelines (OWASP Top 10 for LLM 2025)

Comprehensive security rules for building secure LLM applications. Based on the OWASP Top 10 for Large Language Model Applications 2025 - the authoritative guide to LLM security risks.

## How It Works

1. When building or reviewing LLM applications, reference these security guidelines
2. Each rule includes vulnerable patterns and secure implementations
3. Rules cover the complete LLM application lifecycle: training, deployment, and inference

## Categories

### Critical Impact
- **LLM01: Prompt Injection** - Prevent direct and indirect prompt manipulation
- **LLM02: Sensitive Information Disclosure** - Protect PII, credentials, and proprietary data
- **LLM03: Supply Chain** - Secure model sources, training data, and dependencies
- **LLM04: Data and Model Poisoning** - Prevent training data manipulation and backdoors
- **LLM05: Improper Output Handling** - Sanitize LLM outputs before downstream use

### High Impact
- **LLM06: Excessive Agency** - Limit LLM permissions, functionality, and autonomy
- **LLM07: System Prompt Leakage** - Protect system prompts from disclosure
- **LLM08: Vector and Embedding Weaknesses** - Secure RAG systems and embeddings
- **LLM09: Misinformation** - Mitigate hallucinations and false outputs
- **LLM10: Unbounded Consumption** - Prevent DoS, cost attacks, and model theft

## Usage

Reference the rules in `rules/` directory for detailed examples:

- `rules/prompt-injection.md` - Prompt injection prevention (LLM01)
- `rules/sensitive-disclosure.md` - Sensitive information protection (LLM02)
- `rules/supply-chain.md` - Supply chain security (LLM03)
- `rules/data-poisoning.md` - Data and model poisoning prevention (LLM04)
- `rules/output-handling.md` - Output handling security (LLM05)
- `rules/excessive-agency.md` - Agency control (LLM06)
- `rules/system-prompt-leakage.md` - System prompt protection (LLM07)
- `rules/vector-embedding.md` - RAG and embedding security (LLM08)
- `rules/misinformation.md` - Misinformation mitigation (LLM09)
- `rules/unbounded-consumption.md` - Resource consumption control (LLM10)
- `rules/_sections.md` - Full index of all rules

## Quick Reference

| Vulnerability | Key Prevention |
|--------------|----------------|
| Prompt Injection | Input validation, output filtering, privilege separation |
| Sensitive Disclosure | Data sanitization, access controls, encryption |
| Supply Chain | Verify models, SBOM, trusted sources only |
| Data Poisoning | Data validation, anomaly detection, sandboxing |
| Output Handling | Treat LLM as untrusted, encode outputs, parameterize queries |
| Excessive Agency | Least privilege, human-in-the-loop, minimize extensions |
| System Prompt Leakage | No secrets in prompts, external guardrails |
| Vector/Embedding | Access controls, data validation, monitoring |
| Misinformation | RAG, fine-tuning, human oversight, cross-verification |
| Unbounded Consumption | Rate limiting, input validation, resource monitoring |

## Key Principles

1. **Never trust LLM output** - Validate and sanitize all outputs before use
2. **Least privilege** - Grant minimum necessary permissions to LLM systems
3. **Defense in depth** - Layer multiple security controls
4. **Human oversight** - Require approval for high-impact actions
5. **Monitor and log** - Track all LLM interactions for anomaly detection

## References

- [OWASP Top 10 for LLM Applications 2025](https://genai.owasp.org/llm-top-10/)
- [MITRE ATLAS - Adversarial Threat Landscape for AI Systems](https://atlas.mitre.org/)
- [NIST AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework)