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LLM Security Guidelines (OWASP Top 10 for LLM 2025)

Security rules for building secure LLM applications, based on the OWASP Top 10 for LLM Applications 2025.

How to Use This Skill

Proactive mode — When building or reviewing LLM applications, automatically check for relevant security risks based on the application pattern. You don't need to wait for the user to ask about LLM security.

Reactive mode — When the user asks about LLM security, use the mapping below to find relevant rule files with detailed vulnerable/secure code examples.

Workflow

1. Identify what the user is building (see "What Are You Building?" below)
2. Check the priority rules for that pattern
3. Read the specific rule files from rules/ for code examples
4. Apply the secure patterns or flag vulnerable ones

What Are You Building?

Use this to quickly identify which rules matter most for the user's task:

Categories

Critical Impact

• LLM01: Prompt Injection (rules/prompt-injection.md) - Prevent direct and indirect prompt manipulation
• LLM02: Sensitive Information Disclosure (rules/sensitive-disclosure.md) - Protect PII, credentials, and proprietary data
• LLM03: Supply Chain (rules/supply-chain.md) - Secure model sources, training data, and dependencies
• LLM04: Data and Model Poisoning (rules/data-poisoning.md) - Prevent training data manipulation and backdoors
• LLM05: Improper Output Handling (rules/output-handling.md) - Sanitize LLM outputs before downstream use

High Impact

• LLM06: Excessive Agency (rules/excessive-agency.md) - Limit LLM permissions, functionality, and autonomy
• LLM07: System Prompt Leakage (rules/system-prompt-leakage.md) - Protect system prompts from disclosure
• LLM08: Vector and Embedding Weaknesses (rules/vector-embedding.md) - Secure RAG systems and embeddings
• LLM09: Misinformation (rules/misinformation.md) - Mitigate hallucinations and false outputs
• LLM10: Unbounded Consumption (rules/unbounded-consumption.md) - Prevent DoS, cost attacks, and model theft

See rules/_sections.md for the full index with OWASP/MITRE references.

Quick Reference

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
• MITRE ATLAS - Adversarial Threat Landscape for AI Systems
• NIST AI Risk Management Framework

Weekly Installs

313

Repository

semgrep/skills

GitHub Stars

163

First Seen

Jan 20, 2026

Security Audits

Gen Agent Trust HubPassSocketPassSnykWarn

Installed on

gemini-cli279

codex273

opencode268

github-copilot265

amp242

cursor242