AdvNav: Behavior-Guided Black-Box Adversarial Attacks on Vision-Language Navigation

2026-07-13 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

AdvNav is a novel behavior-guided black-box adversarial attack framework designed to disrupt Vision-and-Language Navigation (VLN) systems by disturbing an agent's first-person views during navigation. Unlike most existing methods that require white-box access to model gradients, AdvNav operates without such access, addressing a critical gap for real-world deployed systems and avoiding computationally exhaustive recursive backpropagation. It overcomes the limitations of prior black-box techniques, which often struggle with the multi-step sequential perception-action loops inherent in VLN tasks. AdvNav employs a dual-granularity behavior-based feedback mechanism, aggregating trajectory-level performance, action-level reward, and a deviation indicator from the agent's self-output behaviors. This feedback guides a hybrid optimization strategy that adaptively tunes perturbation strength and genetically evolves noise spatial structure. Evaluated on the R2R dataset against Transformer-based HAMT and LLM-based MapGPT, AdvNav achieved Attack Success Rates of 49.70%, 65.96%, and 87.30%, demonstrating its effectiveness and generality.

Key takeaway

For AI Security Engineers developing or deploying Vision-and-Language Navigation systems, you must recognize their significant vulnerability to black-box adversarial attacks. AdvNav demonstrates that even without model gradient access, systems like HAMT and MapGPT can be disrupted with high success rates (up to 87.30%). Prioritize integrating robust defense mechanisms against first-person view perturbations and consider behavior-guided attack simulations during your model evaluation to enhance resilience.

Key insights

Black-box adversarial attacks on VLN systems can be effective using behavior-guided feedback and hybrid optimization.

Principles

• Gradient-free attacks are crucial for real-world deployed systems.
• Multi-granularity behavioral feedback enhances black-box optimization.
• Hybrid optimization improves adversarial noise discovery.

Method

AdvNav uses dual-granularity behavior-based feedback (trajectory-level, action-level, deviation indicator) to guide a hybrid optimization strategy. This strategy adaptively tunes perturbation strength and genetically evolves noise spatial structure to find disruptive noise configurations.

In practice

• Evaluate VLN models against black-box, behavior-guided attacks.
• Design VLN models with resilience to first-person view disturbances.
• Incorporate multi-granularity feedback for robust agent training.

Topics

• Vision-Language Navigation
• Black-Box Adversarial Attacks
• Embodied AI
• Gradient-Free Optimization
• Transformer Models
• LLM-based Navigation

Best for: Research Scientist, AI Scientist, AI Security Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.