Red Teaming Your Own AI: How to Simulate a Prompt Injection Attack

A hacker doesn't need to know Python to break your AI. They just need to know English.

In traditional cybersecurity, you worry about SQL Injection or Cross-Site Scripting (XSS). You build firewalls, patch servers, and encrypt databases.

But in 2026, the most dangerous hacker isn't writing code. They are writing prose.

This is Prompt Injection—the number one vulnerability in the OWASP Top 10 for LLM Applications. If your customer support chatbot falls for it, it could offer a user a ₹1 car (as happened to a Chevrolet dealer) or leak your entire proprietary knowledge base.

To survive this, you need to attack yourself first. This is the art of AI Red Teaming.

This guide is your enterprise AI security guide to simulating attacks, auditing your vulnerabilities, and building the "AI Firewalls" required for SOC 2 compliance for AI companies.

Phase 1: The Setup (Thinking Like an Adversary)

Red Teaming is not "QA Testing." QA tests if the bot works as intended. Red Teaming tests if the bot works in ways you never intended.

The Attack Vectors:

• Direct Injection (Jailbreaking): Commanding the AI to override its system prompt (e.g., the infamous "DAN" or "Do Anything Now" mode).
• Indirect Injection: The AI reads a website or document that contains hidden malicious text (e.g., text in white font on a white background) that commands the AI to exfiltrate data.
• Prompt Leaking: Tricking the AI into revealing its own system instructions ("Repeat everything above this line").

Phase 2: The Simulation (How to Hack Your Own Bot)

Before you buy expensive software, try these manual attacks on your current agent.

Attack #1: The "Grandma" Exploit

The Goal: To bypass ethical filters (stealing software keys) by wrapping the request in a benign, emotional context.

The Test: Does your bot refuse, or does it roleplay?

Attack #2: The "Developer Mode" Override

The Goal: To simulate privilege escalation.

The Test: Does your bot maintain its "User" persona, or does it hallucinate admin privileges?

Attack #3: The Translation Trojan

The Goal: To bypass English-language safety filters.

Phase 3: Automating the Attack (AI Red Teaming Frameworks)

Manual testing is slow. In 2026, you need automated AI risk assessment.

1. Open Source Tools (For Developers)

• Garak: An "LLM vulnerability scanner." It probes your model for hallucinations, data leakage, and toxicity. It’s the Nmap of AI.
• PyRIT (Python Risk Identification Tool): Released by Microsoft, this tool automates red teaming for generative AI, helping you scale your attacks.

2. Enterprise Governance Platforms (For CISOs)

If you are aiming for ISO 42001 certification India, you need audit trails.

• Credo AI vs. Vijil: Credo AI excels at policy management and governance reporting. Vijil is specialized in continuous, automated red teaming specifically for agentic systems.
• Lakera AI: Positioned as an "AI Firewall," Lakera sits between your user and your model, filtering out prompt injections in real-time before they even hit your LLM.

Phase 4: The Defense (Building the Guardrails)

Once you've broken your bot, how do you fix it?

• Input Filtering: Use AI firewall software to scan every user prompt for injection patterns (like "Ignore previous instructions") before processing.
• Output Validation: Never let the AI execute a database query directly. Use a deterministic code layer to validate the SQL/JSON before execution.
• Least Privilege: Your AI agent should not have read access to the entire database. Give it access only to the specific rows it needs (Row-Level Security).

Frequently Asked Questions (FAQ)

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