AI Security Lab: Large Model Offensive and Defensive Technologies & Automated Vulnerability Detection Practices

With the widespread application of large language models like ChatGPT and Claude in production systems, their security issues have shifted from academic research to real-world threats. This article delves into large model security testing methods, covering a complete system from threat landscape to offensive and defensive technologies, automated vulnerability detection, and defense strategies, providing systematic security practice references for organizations relying on large models.

Urgency of Security

After integrating large models into production systems, the attack surface expands rapidly. Enterprises face risks such as prompt injection, data poisoning, jailbreak attacks, and model theft, making it essential to build systematic AI security testing capabilities.

Threat Types

• Prompt Injection: Directly/indirectly implant malicious instructions to induce the model to ignore original instructions or leak sensitive information;
• Jailbreak Attacks: Bypass safety alignment mechanisms using techniques like encoding conversion and multilingual mixing to generate harmful content;
• Training Data and Supply Chain Attacks: Poison training sets to implant backdoors; pre-trained weights/third-party plugins become attack vectors;
• Inference-side Attacks: Membership inference leaks sensitive information from training data; model extraction reconstructs alternative models.

Red Team Testing Framework

Simulate real attack behaviors, including four core links: threat modeling, attack library construction, automated scanning, and manual verification.

Adversarial Sample Generation

Generate inputs through minor semantic perturbations to test the model's robustness and boundary handling capabilities, quickly identifying vulnerabilities.

Security Benchmark Evaluation

Establish quantifiable dimensions (harmful content generation rate, privacy leakage risk, etc.), and conduct regular tests to track security change trends.

Static Analysis Tools

Detect security anti-patterns in code/configurations (hard-coded keys, unsafe prompt templates, etc.), and integrate with CI/CD to achieve security left-shift.

Dynamic Fuzz Testing

Adopt semantic-preserving mutation strategies, input random/semi-random data to observe abnormal behaviors.

Model Behavior Monitoring

Real-time monitoring in production environments: output toxicity score, sensitive information matching, behavior deviation degree, triggering alarm or blocking mechanisms.

• Input Purification and Validation: Multi-layer defense (syntax filtering, semantic analysis, model re-audit);
• Principle of Least Privilege: Restrict the model's data access and operation scope to control attack impact;
• Output Audit and Filtering: Independent content audit layer (lightweight classifier/rule engine) to judge security;
• Continuous Security Updates: Follow up on the latest attack technologies, and regularly update security policies and tools.

Leading vendors and institutions have invested resources to build AI security systems:

• OpenAI's Red Teaming Network;
• Anthropic's Responsible Scaling Policy;
• Various open-source security testing frameworks provide references for the industry. Enterprises should build adaptive security systems based on their own scenarios.

Large model security cannot be achieved once and for all; continuous investment is needed to build systematic capabilities: through red team testing, automated scanning tools, and in-depth defense strategies, effectively control risks while enjoying the value of large models. The AI Security Lab is committed to popularizing this capability to the developer community.