LLM Penetration Testing
Tactical Vulnerability Hunting - LLM Penetration Testing is a technical, deep-tier assessment of the entire AI application stack.
It targets the technical "plumbing" where the AI meets your databases, APIs, and users, specifically addressing the
OWASP Top 10 for LLMs.
- Prompt Injection Defense: Neutralizing both direct (user-input) and indirect (third-party data) injections.
- Insecure Output Handling: Preventing XSS and Remote Code Execution (RCE).
- Vector Database Security: Securing RAG pipelines and AI memory.
- Agency & Autonomy Testing: Ensuring AI agents don’t perform harmful actions.
While AI Red Teaming is broad adversarial testing, Tactical LLM Penetration Testing is a structured hunt for real technical flaws.
At i6, we follow a rigorous 6-phase workflow.
Prompt Injection Defense
Direct & indirect injection protection.
Insecure Output Handling
Stops XSS, SQLi, and RCE risks.
Vector DB Security
Protects RAG pipelines.
Agentic AI Testing
Prevents harmful autonomous actions.
6-Phase Tactical Workflow
Phase 1: Reconnaissance & Surface Mapping
We begin by mapping the "AI Attack Surface." This isn't just the model, but every API, database, and plugin it touches.
- Discovery: Identifying the model version (e.g., GPT-4o, Llama 3) and its hosting environment (Azure, AWS, On-prem).
- Asset Inventory: Mapping RAG (Retrieval-Augmented Generation) sources and external tool-calling capabilities.
Phase 2: Technical Threat Modeling
We identify the specific "Trust Boundaries" where data flows from untrusted users into the secure core of your business.
- Data Flow Analysis: Tracking how a user prompt moves from the UI to the Vector Database.
- Scenario Definition: Mapping against the OWASP LLM Top 10 to determine which vulnerabilities are most likely (e.g., Excessive Agency in an AI Assistant).
Phase 3: Prompt & Logic Injection
This is the active "hunting" phase. We use technical payloads to see if the model's logic can be subverted.
- Direct Injections: Using "Jailbreak" payloads to bypass system instructions.
- Indirect Injections: Placing malicious code in a PDF or a Website that the AI is tasked to "summarize," triggering a hidden command.
Phase 4: Integration & Downstream Exploitation
We test what the AI can do once it's compromised. Can a poisoned prompt lead to a hack of your actual servers?
- Insecure Output Handling: We check if AI-generated text can trigger Cross-Site Scripting (XSS) in the browser or SQL Injection in your database.
- Plugin/Tool Abuse: Testing if an "AI Agent" can be tricked into deleting records or moving funds via its API connections.
Phase 5: Data Exfiltration & PII Sniffing
We try to "bleed" the model of its secrets.
- Sensitive Information Disclosure: Using "Inference Attacks" to force the model to reveal PII (Social Security numbers, keys) hidden in its training data or RAG knowledge base.
- System Prompt Extraction: Forcing the model to reveal its "Internal Instructions" to find backdoors.
Phase 6: Remediation & Hardening
We don't just find the holes; we fill them.
- Guardrail Tuning: Configuring real-time filters (like LlamaFirewall or NeMo) to block similar attacks.
- Sanitization Rules: Implementing strict output validation to ensure the AI never sends raw, executable code to the user.
