This session dives into the world of Generative AI (GenAI) security, exploring both the potential dangers and the strategies to combat them.
1. 3 Key Risks of GenAI
The session will unpack three major security risks associated with GenAI:
Data Leakage: GenAI models are trained on massive amounts of data. Malicious actors might try to exploit vulnerabilities in these models to steal sensitive information hidden within the training data.
Output Robustness: GenAI outputs, whether text or code, might not always be reliable. Attackers could manipulate the models to generate misleading or harmful content.
Toxicity & Misuse: GenAI can be misused to create hateful content, spread disinformation, or launch social engineering attacks.
2. Attack Demonstration on Demo RAG App
The session will take things a step further by showcasing real-world attack scenarios on a sample application (Demo RAG App) using different techniques:
Training: Adversaries might try to inject malicious data into the training process to manipulate the model's behavior.
Querying: Crafting specific queries could trick the model into revealing sensitive information.
Bypass: Attackers might exploit loopholes in the system to bypass security measures.
Poisoning: Intentionally feeding the model corrupted data to alter its outputs.
Misusing: Using the model for unintended purposes, like generating spam or fake news.
3. 3 Mitigations for GenAI Security
The session will then explore potential solutions to address these security concerns:
Red Team LLM (Large Language Model): Simulating attacks using specialized AI models to identify weaknesses in GenAI systems before real attackers do.
Penetration Testing for GenAI Apps: Developing specialized penetration testing methods specifically designed to uncover vulnerabilities in GenAI applications.
AI Web Application Firewall (WAF): Implementing security filters tailored to protect GenAI applications from malicious attacks.
Closing Remarks
The session will conclude by summarizing the key takeaways and emphasizing the importance of building robust security measures into GenAI systems from the ground up.
