llm-risk-assess

name	llm-risk-assess
description	Comprehensive LLM security assessment against OWASP Top 10 for LLM Applications 2025. Use when reviewing LLM-integrated applications, RAG pipelines, chatbots, AI agents, or GenAI features. Covers prompt injection, data poisoning, supply chain, excessive agency, and more with real-world attack scenarios and testing methodologies.
license	CC-BY-4.0

LLM Risk Assessment (2025)

Comprehensive evaluation of LLM applications against OWASP Top 10 for LLM Applications 2025. Follow the detailed procedure in plays/tier4-ai-security/llm-risk-assess.md.

Steps

1. Architecture & Threat Modeling

   • Map LLM provider (OpenAI, Anthropic, local models)
   • Document data flows: user input → preprocessing → prompt construction → LLM → output processing → actions
   • Identify RAG components, tool integrations, memory systems
   • Define trust boundaries and attack surfaces

2. Automated Security Testing

   • Run prompt injection probes (Garak, Giskard, custom scripts)
   • Test output handling vulnerabilities
   • Scan for secrets in prompts and configurations
   • Validate vector database security

3. Assess All 10 OWASP LLM 2025 Risks with attack scenarios:

   • LLM01 Prompt Injection — Direct/indirect injection, jailbreaks, goal hijacking, delimiter bypasses
   • LLM02 Sensitive Information Disclosure — Training data leakage, PII exposure, system info extraction, memorized secrets
   • LLM03 Supply Chain — Model poisoning, malicious dependencies, insecure plugins, provenance issues
   • LLM04 Data and Model Poisoning — Training data poisoning, RAG poisoning, embedding manipulation
   • LLM05 Improper Output Handling — XSS, command injection, SQLi, path traversal via LLM outputs
   • LLM06 Excessive Agency — Unauthorized tool calls, permission escalation, dangerous action chains
   • LLM07 System Prompt Leakage — Prompt extraction attacks, secret disclosure, instruction reverse engineering
   • LLM08 Vector and Embedding Weaknesses — Adversarial embeddings, retrieval poisoning, similarity attacks
   • LLM09 Misinformation — Hallucinations, authoritative presentation, grounding failures, harmful domains
   • LLM10 Unbounded Consumption — Token exhaustion, cost attacks, resource exhaustion, DoS

4. Red Team Testing

   • Attempt real-world attack scenarios
   • Test defense bypasses and evasion techniques
   • Validate guardrails and safety controls

Output

Comprehensive LLM security report:

• Architecture diagram with trust boundaries
• Risk matrix (all 10 categories with severity/status)
• Detailed findings with proof-of-concept examples
• Red team test results and bypass techniques
• Remediation roadmap with code examples
• Defense validation checklist

OWASP References

• OWASP Top 10 for LLM Applications 2025
• OWASP AI Exchange (owaspai.org)
• OWASP AI Testing Guide
• OWASP Cheat Sheet: Prompt Injection Prevention
• OWASP Prompt Injection Taxonomy (Arcanum)