AI is at risk of embedding inversion attacks, which target all information used by AI to generate responses. In this topic, we’ll take a look at how AI is at risk of these types of attacks and what you can do about it.
Data Breach Risks > Embedding Inversion
Converting human readable information into more machine-friendly formats has been used in a variety of fields, from biometrics to population statistics to multimedia. The goal of the conversion, called embedding, was usually to represent sensitive data in a way that couldn’t easily be read by a person.
When the sensitive information is valuable to an attacker, they work to collect the embedding and then reconstruct the original information. This sophisticated attack is called embedding inversion and results in high-value information being reconstructed into a human readable format.
How is AI changing things?
Embedding inversion attacks pose serious threats to vector databases and AI systems, particularly those that store information like biometrics, facial recognition, private history and medical information [38]. For organizations looking to adopt AI and who hold valuable information that makes them a target, this type of attack can pose a significant risk.
Here’s how this attack works:
- A malicious actor finds a way to access the information in the vector database used by AI
- They use traditional attacks to collect the vectors, which are the mathematical representations of the original information, stored in the RAG system that your AI uses
- They then use their own specialized AI to reconstruct the vectors so the original information is human readable
What can you do about it?
To mitigate the risk of embedding inversion attacks on AI technologies, you should:
- Use traditional methods to protect and harden databases
- Ensure that sensitive information is identified and redacted or treated specially when being ingested
- Understand how your vendor’s AI implementation mitigates the risk of these types of attacks
- Use an AI firewall to inject noise into results with sensitive information to make reconstruction impossible
In Summary: Use new and traditional security tactics to protect the data your AI can access.In Summary: Continuously rebalance reserved vs on-demand capacity to help control costs.
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