How do you ensure data security in your company's AI usage? Artificial intelligence applications are rapidly spreading with new security threats. Learn about prompt attacks and defense strategies for GenAI systems. Expert recommendations on AI security and GenAI data protection.

Next Generation GenAI Threats: A Comprehensive Look at Prompt Attacks

Introduction

Artificial intelligence applications (especially generative AI systems) are rapidly spreading in many sectors with the potential to increase efficiency and automate processes. However, this rapid adaptation also brings with it new security threats. In particular, techniques called "prompt attacks" can cause serious damage by manipulating GenAI systems. This article provides a comprehensive assessment of the types, effects, and defense strategies of these attacks.

What are Prompt Attacks?

Prompt attacks aim to manipulate the inputs given to the AI system so that the system produces unexpected or harmful outputs. These attacks can be carried out directly (malicious prompts sent by the user) or indirectly (malicious content embedded in data sources).

Effect-Based Classification

1. Goal Hijacking

The goal of the model is manipulated to produce unexpected and unwanted outputs. For example, a resume analysis system can select the wrong candidates thanks to hidden commands.

2. Guardrail Bypass

Harmful content is produced by bypassing system security measures. Examples such as code injection and toxic content production fall into this category.

3. Information Leakage

Sensitive information such as the model's training data or system prompts are intended to be leaked. The technique known as "leak replay" allows information learned from previous sessions to be recalled.

4. Infrastructure Attacks

It covers threats such as causing service interruptions or running malicious code by abusing the model's system resources.

Technical-Based Classification

Prompt Engineering: Carefully prepared prompts to produce malicious outputs.

Social Engineering: Creating manipulative content by abusing user trust.

Obfuscation: Techniques to make malicious commands invisible.

Knowledge Poisoning: Intentionally corrupting training data with malicious information.

These techniques are usually used in combination to increase the impact of attacks.

New Threats: Multimodal Attacks

New generation attacks are not limited to text alone, but can also include multiple modes such as images, audio, and video. For example, it is possible to manipulate the model with commands hidden in an image ("typographic attacks"). This makes it even more difficult to detect and prevent attacks.

Defense Strategies

1. Input and Output Protection Shields

Prompt similarity analysis

Anomalous pattern detection

Role-based access control

2. Model and Data Source Security

Source security in RAG systems

Integrity control in training data

3. Infrastructure Security

Filtering that limits resource consumption

Malware and URL scanning

Code injection control

Conclusion

Prompt attacks pose a growing threat to GenAI applications. A comprehensive security framework should be created by considering both the technical and effective dimensions of these attacks. Application developers, users, and system administrators should understand these risks and implement appropriate defense measures. Acting with dynamic and up-to-date security strategies against new threats will ensure the safe use of GenAI.