Keep on Swimming: Real Attackers Only Need Partial Knowledge of a Multi-Model System
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866929569550827520 |
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| author | Collado, Julian Stangl, Kevin |
| author_facet | Collado, Julian Stangl, Kevin |
| contents | Recent approaches in machine learning often solve a task using a composition of multiple models or agentic architectures. When targeting a composed system with adversarial attacks, it might not be computationally or informationally feasible to train an end-to-end proxy model or a proxy model for every component of the system. We introduce a method to craft an adversarial attack against the overall multi-model system when we only have a proxy model for the final black-box model, and when the transformation applied by the initial models can make the adversarial perturbations ineffective. Current methods handle this by applying many copies of the first model/transformation to an input and then re-use a standard adversarial attack by averaging gradients, or learning a proxy model for both stages. To our knowledge, this is the first attack specifically designed for this threat model and our method has a substantially higher attack success rate (80% vs 25%) and contains 9.4% smaller perturbations (MSE) compared to prior state-of-the-art methods. Our experiments focus on a supervised image pipeline, but we are confident the attack will generalize to other multi-model settings [e.g. a mix of open/closed source foundation models], or agentic systems |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_23483 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Keep on Swimming: Real Attackers Only Need Partial Knowledge of a Multi-Model System Collado, Julian Stangl, Kevin Machine Learning Artificial Intelligence Cryptography and Security Computer Vision and Pattern Recognition Multiagent Systems Recent approaches in machine learning often solve a task using a composition of multiple models or agentic architectures. When targeting a composed system with adversarial attacks, it might not be computationally or informationally feasible to train an end-to-end proxy model or a proxy model for every component of the system. We introduce a method to craft an adversarial attack against the overall multi-model system when we only have a proxy model for the final black-box model, and when the transformation applied by the initial models can make the adversarial perturbations ineffective. Current methods handle this by applying many copies of the first model/transformation to an input and then re-use a standard adversarial attack by averaging gradients, or learning a proxy model for both stages. To our knowledge, this is the first attack specifically designed for this threat model and our method has a substantially higher attack success rate (80% vs 25%) and contains 9.4% smaller perturbations (MSE) compared to prior state-of-the-art methods. Our experiments focus on a supervised image pipeline, but we are confident the attack will generalize to other multi-model settings [e.g. a mix of open/closed source foundation models], or agentic systems |
| title | Keep on Swimming: Real Attackers Only Need Partial Knowledge of a Multi-Model System |
| topic | Machine Learning Artificial Intelligence Cryptography and Security Computer Vision and Pattern Recognition Multiagent Systems |
| url | https://arxiv.org/abs/2410.23483 |