pentest-ai-llm-security

name	pentest-ai-llm-security
description	AI/LLM application security testing — prompt injection, jailbreaking, data exfiltration, and insecure output handling per OWASP LLM Top 10.

Pentest AI/LLM Security

Purpose

AI-integrated applications introduce entirely new attack surfaces. Prompt injection is the "SQLi of AI." Neither Shannon nor any existing skill addresses this domain. OWASP LLM Top 10 (2025) defines the methodology.

Prerequisites

Authorization Requirements

• Written authorization with AI/LLM testing scope explicitly included
• Model access details — API endpoints, model versions, tool/function access
• Data sensitivity classification — what data the LLM can access
• Rate limit awareness — LLM API costs can escalate quickly

Environment Setup

• Garak for automated LLM vulnerability scanning
• Burp Suite for API interception of LLM requests/responses
• Python scripts for custom prompt injection payloads
• Local proxy to capture full request/response chains

Core Workflow

1. Integration Point Discovery: Identify all LLM integration points — chat interfaces, content generation, RAG pipelines, AI search, code completion, summarization.
2. Direct Prompt Injection: Override system prompts, extract system prompt content, inject instructions that change model behavior.
3. Indirect Prompt Injection: Embed malicious instructions in documents/emails/web pages the LLM processes, poisoned RAG context.
4. Data Exfiltration: Extract training data, PII from context windows, other users' conversation history, system config details.
5. Insecure Output Handling: LLM output rendered as HTML (XSS via LLM), used in SQL queries (SQLi via LLM), used in system commands.
6. Excessive Agency: LLM with tool access performing unauthorized actions, privilege escalation through tool chains, resource abuse.
7. Classification: Document findings with OWASP LLM Top 10 (2025) classification and remediation guidance.

OWASP LLM Top 10 (2025) Coverage

Category	Test Focus	Status
LLM01 Prompt Injection	Direct and indirect injection	✅
LLM02 Sensitive Information Disclosure	Data exfiltration, PII leakage	✅
LLM03 Supply Chain	Model provenance, plugin trust	✅
LLM04 Data and Model Poisoning	Training data integrity	✅
LLM05 Improper Output Handling	XSS/SQLi via LLM output	✅
LLM06 Excessive Agency	Unauthorized tool use	✅
LLM07 System Prompt Leakage	System prompt extraction	✅
LLM08 Vector and Embedding Weaknesses	RAG poisoning	✅
LLM09 Misinformation	Hallucination exploitation	✅
LLM10 Unbounded Consumption	Resource exhaustion	✅

Tool Categories

Category	Tools	Purpose
LLM Scanning	Garak, rebuff	Automated prompt injection testing
API Interception	Burp Suite, mitmproxy	LLM API request/response capture
Prompt Fuzzing	Custom Python scripts	Payload generation and testing
Output Analysis	Browser DevTools, Burp	Insecure output rendering detection

References

• references/tools.md - Tool function signatures and parameters
• references/workflows.md - Attack pattern definitions and test vectors