llm-risk-assess

v2026.09.25

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.

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Install command
npx skhub add owasp/llm-risk-assess
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SKILL.md

LLM Risk Assessment (2025)

Comprehensive evaluation of LLM applications against OWASP Top 10 for LLM Applications 2025. Follow the detailed procedure in plays/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)
Discovery
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Version
Latest version metadata

Version

v2026.09.25

Published

Sep 25, 2026

Category

Uncategorized

License

NOASSERTION

Source path

plugins/ai-security-skills/skills/llm-risk-assess

Default branch

main

Latest commit

79fea6b

Tree SHA

6d787d7