FuzzingRL: Reinforcement Fuzz-Testing for Revealing VLM Failures

2026-03-10 · Source: cs.LG updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Cybersecurity & Data Privacy · Depth: Expert, extended

Summary

FuzzingRL is a novel framework designed to automatically identify and amplify vulnerabilities in Vision-Language Models (VLMs) by generating adversarial questions. Inspired by software fuzz-testing, it employs vision-language fuzzing to create diverse input query variants and adversarial reinforcement fine-tuning to guide a question generator towards increasingly challenging queries. This approach significantly degrades VLM accuracy; for example, Qwen2.5-VL-32B's accuracy dropped from 86.58% to 65.53% over four reinforcement learning iterations. The framework outputs reproducible failure cases with metadata, creating an attributable error profile. A fuzzing policy trained on one VLM, such as Qwen2.5-VL-7B, demonstrates transferability, effectively reducing the performance of other diverse VLMs by exposing common failure patterns in spatial reasoning, counting, and sensitivity to instruction phrasing.

Key takeaway

For AI scientists and research scientists developing or deploying Vision-Language Models, FuzzingRL offers a robust method to proactively uncover critical failure modes. You should integrate this reinforcement fuzz-testing approach into your VLM evaluation pipelines to move beyond static benchmarks, ensuring more resilient and trustworthy multimodal AI systems before production deployment. This will help you identify and mitigate vulnerabilities related to spatial reasoning, compositional understanding, and linguistic biases.

Key insights

FuzzingRL automatically generates adversarial questions to expose and amplify Vision-Language Model vulnerabilities through iterative refinement.

Principles

• Combine input diversification with adaptive guidance.
• Reward incorrect VLM predictions to steer question generation.
• Fuzzing policies can transfer across VLM architectures.

Method

FuzzingRL uses vision-language fuzzing across 24 subdimensions and 8 roles to generate diverse queries. Adversarial reinforcement fine-tuning, with GPT-4o and human judges, optimizes the question generator to maximize VLM failure rates via DPO.

In practice

• Use FuzzingRL to stress-test new VLM deployments.
• Analyze FuzzingRL's error profiles to prioritize VLM improvements.
• Apply fuzzing roles to diversify existing VLM benchmarks.

Topics

• Vision-Language Models
• Fuzz Testing
• Reinforcement Learning
• Adversarial Training
• VLM Robustness

Best for: AI Scientist, Research Scientist, AI Researcher, Machine Learning Engineer, Deep Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.LG updates on arXiv.org.