Adrian Johnson
TLDR:
AI systems are vulnerable to “poisoning attacks,” where bad actors corrupt the data used to train the AI model. This can result in the spread of misinformation, unreliable outcomes, and potentially catastrophic consequences. Defending against such attacks is challenging, but organizations can take steps to protect their AI systems, such as sourcing data from the original source, embedding security measures at every step of the AI system’s creation and deployment, and staying informed about evolving threat landscapes.
If Fake Data is Used to Train AI, You Have a Problem
A new U.S. government study conducted by the National Institute of Standards and Technology (NIST) has highlighted the vulnerability of AI systems to “poisoning attacks.” These attacks involve corrupting the data used to train AI models, which can lead to misinformation, unreliable outcomes, and potential harm in critical areas such as finance and healthcare. The study found that poisoning attacks are relatively easy to mount and require minimal knowledge of the targeted AI system. Adversaries can control just a few dozen training samples to impact the entire training set.
The Risk of Poisoning Attacks on AI Systems
By poisoning AI systems used in news aggregation sites or social media platforms, bad actors can spread misinformation and propaganda more effectively. They can also manipulate AI systems to produce unreliable or harmful outcomes, eroding trust in these systems. The increasing integration of AI into various domains, such as self-driving cars, medical diagnostics, and chatbots, poses a significant concern as these systems rely on extensive data for training. The data, often sourced from websites and user interactions, is susceptible to manipulation by malicious entities, compromising the integrity and behavior of AI systems.
Protecting AI Systems from Bad Data
Defending against poisoning attacks is challenging but not impossible. Experts recommend sourcing data for training directly from the original source to ensure its validity. However, this can be difficult due to the large volume of information required to train AI models. Embedding security measures at every step of an AI system’s creation and deployment is crucial. This includes implementing red teaming plans to test models and attack surfaces, ensuring data storage security and privacy enforcement, and having measures in place to detect and respond to policy violations. Staying informed about evolving threat landscapes and techniques used by adversaries is also essential for effective defense.
Conclusion
Poisoning attacks pose a significant threat to AI systems by corrupting the data used for their training. Adversaries can manipulate AI models to spread misinformation, produce unreliable outcomes, and potentially cause catastrophic consequences in critical domains. Defending against these attacks requires organizations to prioritize the security of AI systems at every stage of their creation and deployment. By taking proactive measures and staying informed about evolving threats, organizations can safeguard their AI systems from the risk of poisoning attacks.
