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Malice in Agentland: Down the Rabbit Hole of Backdoors in the AI Supply Chain
Created by
Haebom
Author
Leo Boisvert, Abhay Puri, Chandra Kiran Reddy Evuru, Nicolas Chapados, Quentin Cappart, Alexandre Lacoste, Krishnamurthy DJ Dvijotham, Alexandre Drouin
Outline
Fine-tuning an AI agent based on its own interaction data is effective in enhancing agent capabilities, but it can also introduce serious security vulnerabilities within the AI supply chain. This study demonstrates that attackers can easily insert undetectable backdoors that trigger specific trigger phrases to perform unsafe or malicious actions. This is validated through three realistic threat models: 1) direct corruption of fine-tuned data, 2) environmental contamination, and 3) supply chain contamination. Experimental results show that even by corrupting less than 2% of collected traces, a backdoor can be inserted that allows the agent to leak confidential user information with a success rate of over 80% when a specific trigger is present. Furthermore, we demonstrate that existing safeguards fail to detect or prevent such malicious actions.
Takeaways, Limitations
•
Emphasizes the need for rigorous security verification of the data collection process and model supply chain in AI agent development.
•
Demonstrates that attackers can insert a critical backdoor with just a small amount of data corruption
