The Complete Guide to LLM Security Testing in 2026

Every LLM application is an attack surface. Whether you are building a customer support chatbot, an internal knowledge assistant, or an autonomous coding agent, your AI system is vulnerable to adversarial attacks that traditional security testing does not catch. This guide covers what you need to know about LLM security in 2026 -- and how to test for it.

What Is LLM Red Teaming?

LLM red teaming is the practice of systematically probing AI systems for vulnerabilities by simulating adversarial attacks. Unlike traditional penetration testing that targets network infrastructure or web applications, LLM red teaming targets the model layer itself -- the prompts, the reasoning, the tool calls, and the output handling.

A red team assessment typically involves:

• Reconnaissance -- Probing the model to understand its system prompt, capabilities, and boundaries.
• Attack execution -- Running hundreds of adversarial inputs across multiple attack categories.
• Escalation -- Multi-turn conversations that gradually push the model past its safety boundaries.
• Reporting -- Documenting vulnerabilities with severity ratings, reproduction steps, and remediation guidance.

The OWASP LLM Top 10 Explained

The OWASP Top 10 for LLM Applications is the industry standard framework for understanding LLM security risks. Here is each category and what it means for your application:

Types of Attacks You Need to Test For

Prompt Injection

The most prevalent LLM attack. Direct injection embeds malicious instructions in user input. Indirect injection hides instructions in external data sources (web pages, PDFs, emails) that the model processes through RAG or tool use.

# Example: Direct prompt injection test
import evalguard

client = evalguard.Client(api_key="eg_...")

results = client.security.scan(
    target="https://api.yourapp.com/v1/chat",
    categories=["prompt-injection"],
    variants=["direct", "indirect", "few-shot", "encoding"],
    depth="comprehensive",
)

for vuln in results.vulnerabilities:
    print(f"[{vuln.severity}] {vuln.category}: {vuln.description}")
    print(f"  Payload: {vuln.payload[:100]}...")
    print(f"  Fix: {vuln.remediation}")

Jailbreaking

Jailbreak attacks bypass safety training through role-playing scenarios, hypothetical framing, encoding tricks (Base64, ROT13, Unicode), and multi-turn social engineering. EvalGuard's scanner includes 50+ jailbreak variants, from classic DAN-style prompts to sophisticated multi-turn escalation chains.

Data Exfiltration

Attackers extract sensitive information from your system prompt, RAG context, or the model's training data. Common techniques include asking the model to "repeat everything above", encoding extraction through markdown image links, and side-channel attacks that infer private data from model behavior.

Tool Abuse & Excessive Agency

If your LLM has access to tools (APIs, databases, file systems), attackers can manipulate the model into making unauthorized tool calls. This includes reading files outside the allowed scope, sending data to external endpoints, or chaining tool calls to escalate privileges.

How to Test Your LLM with EvalGuard

EvalGuard provides three ways to security-test your LLM applications:

1. Automated scanning -- Point the scanner at your API endpoint and let it run 246 attack plugins automatically. Results are mapped to the OWASP LLM Top 10 with severity ratings and remediation advice.

# Full OWASP scan in one command
scan = client.security.scan(
    target="https://api.yourapp.com/v1/chat",
    templates="owasp-top-10",
    depth="comprehensive",
)

print(scan.summary)
# Grade: B+ | 0 Critical | 2 High | 5 Medium | 8 Low
# Full report: https://app.evalguard.ai/scans/sc_abc123

2. CI/CD integration -- Add security scans to your deployment pipeline. Block deployments that introduce new critical or high-severity vulnerabilities.

3. Continuous monitoring -- The AI Gateway inspects every request and response in real time, blocking prompt injection attempts and flagging anomalous patterns before they reach your model.

Best Practices for LLM Security

Getting Started

EvalGuard's free tier includes 5 red team scans per month -- enough to security-test your application before every major release. Install the SDK and run your first scan in under 2 minutes:

pip install evalguard

# Or with npm
npm install @evalguard/sdk

Read the full security testing documentation or jump straight to the quickstart guide.