FuzzingRL: Reinforcement Fuzz-Testing for Revealing VLM Failures
arXiv:2603.06600v1 Announce Type: new Abstract: Vision Language Models (VLMs) are prone to errors, and identifying where these errors occur is critical for ensuring the reliability and safety of AI systems. In this paper, we propose an approach that automatically generates questions designed to deliberately induce incorrect responses from VLMs, thereby revealing their vulnerabilities. The core of this approach lies in fuzz testing and reinforcement finetuning: we transform a single input query into a large set of diverse variants through vision and language fuzzing. Based on the fuzzing outcomes, the question generator is further instructed by adversarial reinforcement fine-tuning to produce increasingly challenging queries that trigger model failures. With this approach, we can consistently drive down a target VLM's answer accuracy -- for example, the accuracy of Qwen2.5-VL-32B on our generated questions drops from 86.58\% to 65.53\% in four RL iterations. Moreover, a fuzzing policy
arXiv:2603.06600v1 Announce Type: new Abstract: Vision Language Models (VLMs) are prone to errors, and identifying where these errors occur is critical for ensuring the reliability and safety of AI systems. In this paper, we propose an approach that automatically generates questions designed to deliberately induce incorrect responses from VLMs, thereby revealing their vulnerabilities. The core of this approach lies in fuzz testing and reinforcement finetuning: we transform a single input query into a large set of diverse variants through vision and language fuzzing. Based on the fuzzing outcomes, the question generator is further instructed by adversarial reinforcement fine-tuning to produce increasingly challenging queries that trigger model failures. With this approach, we can consistently drive down a target VLM's answer accuracy -- for example, the accuracy of Qwen2.5-VL-32B on our generated questions drops from 86.58\% to 65.53\% in four RL iterations. Moreover, a fuzzing policy trained against a single target VLM transfers to multiple other VLMs, producing challenging queries that degrade their performance as well.
Executive Summary
This article proposes FuzzingRL, a novel approach to uncovering vulnerabilities in Vision Language Models (VLMs) using fuzz testing and reinforcement learning. By generating diverse variants of input queries through vision and language fuzzing, the approach can deliberately induce incorrect responses from VLMs. The question generator is then fine-tuned through adversarial reinforcement learning to produce increasingly challenging queries that trigger model failures. The authors demonstrate the effectiveness of FuzzingRL by reducing the accuracy of a target VLM from 86.58% to 65.53% in four iterations. The approach also shows promise in transferring to multiple other VLMs. This research has significant implications for the development and deployment of AI systems, highlighting the need for robust testing and validation to ensure their reliability and safety.
Key Points
- ▸ FuzzingRL is a novel approach to identifying vulnerabilities in VLMs through fuzz testing and reinforcement learning.
- ▸ The approach generates diverse variants of input queries through vision and language fuzzing to deliberately induce incorrect responses from VLMs.
- ▸ The question generator is fine-tuned through adversarial reinforcement learning to produce increasingly challenging queries that trigger model failures.
Merits
Strength
The approach demonstrates significant reductions in VLM performance, highlighting its potential for identifying vulnerabilities and ensuring AI system reliability and safety.
Demerits
Limitation
The approach requires significant computational resources and may not be scalable for large-scale VLMs, limiting its practical application.
Expert Commentary
FuzzingRL represents a significant advancement in the field of AI testing and validation. By leveraging fuzz testing and reinforcement learning, the approach offers a novel and effective method for identifying vulnerabilities in VLMs. While the approach has significant merits, its limitations in terms of scalability and computational resources must be carefully considered. Nevertheless, the implications of FuzzingRL are far-reaching, and its potential to improve AI system reliability and safety makes it a critical area of research and development.
Recommendations
- ✓ Future research should focus on scaling up FuzzingRL to accommodate larger VLMs and reducing its computational requirements.
- ✓ The approach should be further explored in applications where reliability and safety are critical, such as healthcare and transportation.