Summary of Dissecting Adversarial Robustness Of Multimodal Lm Agents, by Chen Henry Wu et al.
Dissecting Adversarial Robustness of Multimodal LM Agents
by Chen Henry Wu, Rishi Shah, Jing Yu Koh, Ruslan Salakhutdinov, Daniel Fried, Aditi Raghunathan
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Agent Robustness Evaluation (ARE) framework systematically examines the robustness of autonomous agents in real environments by viewing the agent as a graph showing the flow of intermediate outputs between components. The framework decomposes robustness as the flow of adversarial information on the graph. Experimental results show that existing agents can be successfully broken with imperceptible perturbations, and inference-time compute can open up new vulnerabilities. Furthermore, adding new components to an agent can harm its robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a world where machines learn like humans do! Right now, we’re using language models to build autonomous agents that interact with our environment. But, there’s a problem: these agents are not safe from bad guys who want to trick them. To fix this, researchers created 200 special challenges for agents and developed a new way to test their safety. They found out that some agents can be broken easily, even when we only change one tiny part of the image! This is important because it helps us build better, safer machines. |
Keywords
» Artificial intelligence » Inference
