et al., 2020; Wang et al., 2023), which improves their utility in the complicated real world. But the attempt
of crafting misleading or even harmful queries will undoubtfully persist as well (Perez & Ribeiro, 2022).
Moreover, due to training on massive web data, foundation models are faced with long-lasting training-time
security issues in deep learning, such as backdoor attacks (Kurita et al., 2020; Cui et al., 2022) and data
poisoning attacks (Wallace et al., 2021).
In addition to foundation models, the incorporated tools could be new attack targets for adversaries. For
example, the attackers could maliciously modify the usage documentation or even the tools themselves (e.g.
attacking a news API to give biased reports) to mislead the model into erroneous outcomes. The key challenge
lies in the interplay between foundation models and tools, since a safe and robust system requires the models
Tool Learning with Foundation Models