Understand the Most Common GenAI Cyber-Attacks: The More You Know, the Better You Can Protect

GenAI has quickly become the new technology darling, and with that, the bullseye for cyber criminals. GenAI solutions rely on vast amounts of data and complex algorithms to perform tasks ranging from natural language processing to image recognition.

It is precisely this reliance on data and algorithms that makes them vulnerable to various forms of manipulation because bad actors know they can create havoc. Messing up with models can lead to incorrect outputs, data breaches, or even complete system failures.

According to the National Institute of Standards and Technology (NIST), there are several types of cyberattacks specifically aimed at manipulating the behavior of AI systems. In this blog, I will explore these cyberattacks and provide insights into how organizations adopting GenAI solutions can protect their systems.

Types of GenAI Attacks

According to NIST, the following are the most common cyber-attacks on GenAI. To avoid these attacks, and ensure that your GenAI solutions are trustworthy, you need to be vigilant and defend against them.

1. Model Evasion Attacks: This is the most common attack and involves the intentional manipulation of input data to cause an AI system to make errors. These attacks can be subtle, such as small changes in images that are imperceptible to humans, but cause AI algorithms to misclassify the image. A good example can be a spam detection system. The GenAI model can be evaded by slightly altering the text of a spam email to avoid detection, allowing spam to reach users' inboxes.

2. Data Poisoning Attacks: In this scenario attackers deliberately corrupt the training data used to build GenAI models. By injecting malicious data into the training set, adversaries can significantly degrade the performance and accuracy of the GenAI system. For example, in a financial fraud detection system, an attacker might introduce fraudulent transactions labeled as legitimate into the training data, leading to a model that fails to identify actual fraud. Here is an idea on how you can prevent data poisoning attacks.

*Related read: Can a cyberattack cause the next financial meltdown

3. Privacy Attacks: Privacy attacks involve adversaries attempting to replicate or steal the underlying GenAI model and ultimately the data. By querying the AI system and analyzing its responses, attackers can reconstruct the model, gaining insights into proprietary algorithms and the data. If they can find and access the online data sources and add altered data to them, then the model can be tainted forever, as making the LLM model unlearn the undesired patterns is a difficult task.

4. Abuse Attacks: Those attacks aim to manipulate a GenAI system's original purpose by feeding it legitimate, but incorrect data from compromised sources. This can cause GenAI models to spread hate speech, fuel discrimination, produce content inciting violence against certain groups, or generate and circulate images, text, or malicious code that could trigger a cyberattack.

How to Protect Your GenAI Systems

To defend against those common AI cyber-attacks implement robust security measures and continue to deploy, monitor, and optimize those data and AI security measures. Best practices, you should apply include:

• Use data encryption and secure protocols to protect data in transit and at rest.
• Regularly update and patch AI systems to address known vulnerabilities.
• Perform regular security audits and vulnerability assessments to identify and mitigate potential risks.
• Monitor AI system performance for any anomalies that could indicate an attack.
• Employ adversarial training to make AI models more resilient against adversarial attacks.
• Implement anomaly detection systems to identify and respond to unusual patterns of behavior.
• Rigorous model testing and ongoing reinforcement learning from human feedback.

It is not solely the responsibility of technology teams to build a comprehensive defense strategy. Like data security, GenAI security should also be a team sport, and everyone should act responsibly. Shadow AI is a real problem for many organizations, and executive leadership needs to foster a security-first culture. Organizations must invest in time and resources to educate employees about the importance of AI security and best practices.

Conclusion

As you continue to integrate GenAI solutions into your operations, understanding and mitigating the risks posed by cyberattacks is crucial. From manipulating GenAI system to produce adversarial outputs to very major safety risks in critical applications, like autonomous driving, malicious attacks on GenAI solutions can lead to unpleasant consequences, if not dire results.

By recognizing the types of attacks identified by NIST and implementing robust security measures, businesses can protect their AI systems and ensure their long-term success. Stay proactive, stay informed, and stay secure.

For more insights on protecting your AI systems, contact our team of experts today.