AI Security — Risks, Testing, and Mitigation

AI security protects AI systems from attacks like prompt injection, data leakage, model poisoning, and adversarial examples. Test for these risks before production, use red teaming to find vulnerabilities, and implement secure deployment practices.

What Are the Top AI Security Risks?

AI systems face unique security risks beyond traditional software vulnerabilities. Key risks include prompt injection attacks, data leakage, model poisoning, adversarial examples, and unauthorized access to models or training data.

Common AI security risks:

Prompt injection (malicious inputs override system prompts)
Data leakage (training data extracted from models)
Model poisoning (adversarial training data corrupts models)
Adversarial attacks (inputs designed to cause errors)
Unauthorized model access (theft or misuse of models)

What Are Prompt Injection Attacks?

Prompt injection attacks manipulate LLMs by injecting malicious instructions into user inputs. Attackers override system prompts to make the model ignore safety guidelines, reveal sensitive data, or perform unauthorized actions.

Types of prompt injection:

Direct injection (obvious malicious prompts)
Indirect injection (hidden in seemingly normal text)
Jailbreaking (bypassing safety filters)
Prompt leaking (extracting system prompts)

How Does Data Leakage Happen in LLMs?

Data leakage occurs when LLMs reveal training data through their outputs. Attackers use techniques like membership inference, model extraction, or prompt engineering to extract sensitive information that was in the training dataset.

Prevention strategies:

Filter training data for sensitive information
Use differential privacy techniques
Monitor outputs for data leakage patterns
Limit model access and outputs

What Is AI Red Teaming?

AI red teaming is the practice of systematically testing AI systems for security vulnerabilities, safety issues, and failure modes. Red teams simulate attacks to find weaknesses before malicious actors exploit them.

Red teaming activities:

Prompt injection testing
Adversarial example generation
Bias and fairness testing
Performance under stress
Edge case discovery

Frequently Asked Questions

How do I protect against prompt injection attacks?

Use input sanitization, prompt engineering to separate user inputs from system prompts, output filtering, rate limiting, and monitoring. Test regularly with red team exercises to find vulnerabilities.

Can I prevent all data leakage from LLMs?

Complete prevention is difficult, but you can reduce risk by filtering training data, using differential privacy, monitoring outputs, and limiting model access. Assume some leakage risk and plan accordingly.

How often should I run AI red team exercises?

Run red team exercises before production deployment, after major model updates, and quarterly for ongoing systems. Also run them when new attack vectors are discovered or when security incidents occur.

What's the difference between AI security and traditional cybersecurity?

AI security includes traditional cybersecurity (network, access control) plus AI-specific risks like prompt injection, model poisoning, adversarial attacks, and data leakage. You need both traditional and AI-specific security measures.

Should I use open-source or proprietary models for security?

Both have trade-offs. Open-source models allow security audits and customization but require more security management. Proprietary models offer managed security but less transparency. Choose based on your security requirements and resources.

TL;DR