November 21, 2023 at 09:57AM
Jailbreaking and prompt injection pose rising threats to generative AI (GenAI), tricking the AI with specific prompts or concealing malicious data. GenAI models used in coding can have security vulnerabilities. Training AI on sensitive data can risk exposure. Traditional security approaches are inadequate. Two potential defense approaches are blackbox defense (intelligent monitoring system) and whitebox defense (fine-tuning the model). Evolving threat management and flexibility in defense techniques are necessary. Prioritizing new security measures is key for effective, ethical, and safe interactions between machines and humans in the AI era.
Based on the meeting notes, it is evident that there are rising threats to generative AI, or GenAI, such as jailbreaking and prompt injection. Jailbreaking involves tricking the AI with specific prompts to produce harmful or misleading results, while prompt injection conceals malicious data or instructions within typical prompts. These threats can lead to vulnerabilities or reputational risks.
Furthermore, reliance on generated content, including using GenAI models like Microsoft CoPilot or ChatGPT to help write or revise source code, introduces the risk of security vulnerabilities and other problems that developers may not be aware of. Training an AI on sensitive or proprietary data also poses the risk of data leakage and the inference of personally identifiable information (PII) and access tokens. Detecting such data leakage can be challenging due to the unpredictable behavior of the AI model.
Traditional security approaches like rule-based firewalls, data obfuscation, and rule-based filtering have limitations when it comes to addressing the dynamic and adaptive nature of GenAI threats.
Moving forward, it is recommended to develop more intelligent defenses for GenAI. One potential approach is a blackbox defense, which involves an intelligent monitoring system that analyzes the outputs of GenAI models for threats. This approach is suitable for commercial closed-source models where modifying the model itself is not possible. Another approach is a whitebox defense, which delves into the model’s internals, providing both a shield and the knowledge to use it. This method allows for fine-tuning the model against known malicious prompts and is more comprehensive and effective against unseen attacks.
Additionally, it is necessary to have evolving threat management for GenAI security. GenAI threats are constantly evolving, and security systems need to adapt and learn from past breaches to anticipate future strategies. Monitoring, detecting, and responding to attacks on GenAI will be crucial, along with implementing a threat intelligence strategy to track emerging threats.
It is also recommended to preserve flexibility in defense techniques for GenAI. As the field of GenAI is still relatively new, it is important to design systems that can accommodate new defense strategies as they are discovered.
Overall, prioritizing new security measures that ensure effective, ethical, and safe interactions between machines and humans is crucial in the AI era.