AI Security Testing Framework: A Practical Guide to Offense and Defense for Large Language Models

With the widespread application of large language models like GPT-4 and Claude, AI security has become a key issue in industrial practice. The ai-security-lab framework introduced in this article is a systematic set of security testing tools and methodologies that help researchers and developers test and harden LLM security, covering core areas such as jailbreak attacks, prompt injection, and vulnerability scanning, providing a practical guide for AI security offense and defense.

LLM faces the following main security threats:

1. Prompt Injection: Attackers construct inputs to override original instructions, inducing the model to perform unintended operations, which can be indirectly achieved through user input or external data sources;
2. Jailbreak Attacks: Bypassing the model's security barriers to generate blocked content, with techniques constantly evolving (e.g., DAN prompts, role-playing, etc.);
3. Data Extraction Attacks: Inducing the model to leak sensitive information from training data (such as private data, system prompts, etc.).

The ai-security-lab framework provides three core testing capabilities:

Jailbreak Technology Testing

Built-in multiple jailbreak modes, including role-playing attacks, hypothetical scenarios, code obfuscation, step-by-step induction, etc., to evaluate the model's resistance to bypass techniques.

Prompt Injection Detection

Automated tools detect indirect prompt injection, the degree of system prompt isolation, and the risk of context contamination in multi-turn conversations, suitable for scenarios with RAG or integrated external APIs.

Automated Vulnerability Scanning

Perform systematic testing on mainstream models such as GPT-4, Claude, and Gemini, generating vulnerability reports, reproducible attack examples, and repair suggestions.

Based on test results, the following hardening measures can be taken:

Input Layer Protection

Strict input validation and filtering, prompt isolation, content security pre-screening;

Model Layer Hardening

Optimize system prompts, post-output processing, adversarial training;

Architecture Layer Design

Least privilege principle to restrict tool calls, human-machine collaborative review, security monitoring and alerts.

Future challenges include multimodal attacks, model theft, supply chain security, alignment issues, etc. AI security is an ongoing process, and the ai-security-lab framework provides an extensible testing foundation. For organizations deploying LLMs in production environments, systematic security testing has become a necessity.