# FalseCrashReducer: Mitigating False Positive Crashes in OSS-Fuzz-Gen Using Agentic AI

arXiv: https://arxiv.org/abs/2510.02185 · HTML: https://mlsanity.com/paper/2510.02185/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 22 of its 69 references that are in the map.
- First posted on arXiv 2 Oct 2025.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 22 of 22)

- [Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs](https://mlsanity.com/paper/2306.13063/) (2023)
- [Decomposed Prompting: A Modular Approach for Solving Complex Tasks](https://mlsanity.com/paper/2210.02406/) (2022)
- [CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges](https://mlsanity.com/paper/2401.07339/) (2024)
- [ADaPT: As-Needed Decomposition and Planning with Language Models](https://mlsanity.com/paper/2311.05772/) (2023)
- [AgentScope: A Flexible yet Robust Multi-Agent Platform](https://mlsanity.com/paper/2402.14034/) (2024)
- [CodeRAG-Bench: Can Retrieval Augment Code Generation?](https://mlsanity.com/paper/2406.14497/) (2024)
- [The Art, Science, and Engineering of Fuzzing: A Survey](https://mlsanity.com/paper/1812.00140/) (2018)
- [Cycles of Thought: Measuring LLM Confidence through Stable Explanations](https://mlsanity.com/paper/2406.03441/) (2024)
- [Prompt Fuzzing for Fuzz Driver Generation](https://mlsanity.com/paper/2312.17677/) (2023)
- [How Effective Are They? Exploring Large Language Model Based Fuzz Driver Generation](https://mlsanity.com/paper/2307.12469/) (2023)
- [On Mitigating Code LLM Hallucinations with API Documentation](https://mlsanity.com/paper/2407.09726/) (2024)
- [The Confidence Paradox: Can LLM Know When It's Wrong](https://mlsanity.com/paper/2506.23464/) (2025)
- [Harnessing Large Language Models for Seed Generation in Greybox Fuzzing](https://mlsanity.com/paper/2411.18143/) (2024)
- [An Empirical Study of OSS-Fuzz Bugs](https://mlsanity.com/paper/2103.11518/) (2021)
- [Agent That Debugs: Dynamic State-Guided Vulnerability Repair](https://mlsanity.com/paper/2504.07634/) (2025)
- [Contextual API Completion for Unseen Repositories Using LLMs](https://mlsanity.com/paper/2405.04600/) (2024)
- [LLM Confidence Evaluation Measures in Zero-Shot CSS Classification](https://mlsanity.com/paper/2410.13047/) (2024)
- [Incoherence as Oracle-less Measure of Error in LLM-Based Code Generation](https://mlsanity.com/paper/2507.00057/) (2025)
- [Fixing Security Vulnerabilities with AI in OSS-Fuzz](https://mlsanity.com/paper/2411.03346/) (2024)
- [Representation Consistency for Accurate and Coherent LLM Answer Aggregation](https://mlsanity.com/paper/2506.21590/) (2025)
- [An Empirical Study of Fuzz Harness Degradation](https://mlsanity.com/paper/2505.06177/) (2025)
- [Do Unit Proofs Work? An Empirical Study of Compositional Bounded Model Checking for Memory Safety Verification](https://mlsanity.com/paper/2503.13762/) (2025)

## Built on by (most cited first, 2 of 2)

- [SmartOracle -- An Agentic Approach to Mitigate Noise in Differential Oracles](https://mlsanity.com/paper/2601.15074/) (2026)
- [The History Is the Detector: Executing CVE Patch History, End-to-End](https://mlsanity.com/paper/2609.05335/) (2026)

## Abstract (arXiv)

Fuzz testing has become a cornerstone technique for identifying software bugs and security vulnerabilities, with broad adoption in both industry and open-source communities. Directly fuzzing a function requires fuzz drivers, which translate random fuzzer inputs into valid arguments for the target function. Given the cost and expertise required to manually develop fuzz drivers, methods exist that leverage program analysis and Large Language Models to automatically generate these drivers. However, the generated fuzz drivers frequently lead to false positive crashes, especially in functions highly structured input and complex state requirements. This problem is especially crucial in industry-scale fuzz driver generation efforts like OSS-Fuzz-en, as reporting false positive crashes to maintainers impede trust in both the system and the team. This paper presents two AI-driven strategies to reduce false positives in OSS-Fuzz-Gen, a multi-agent system for automated fuzz driver generation. First, constraint-based fuzz driver generation proactively enforces constraints on a function's inputs and state to guide driver creation. Second, context-based crash validation reactively analyzes function callers to determine whether reported crashes are feasible from program entry points. Using 1,500 benchmark functions from OSS-Fuzz, we show that these strategies reduce spurious crashes by up to 8%, cut reported crashes by more than half, and demonstrate that frontier LLMs can serve as reliable program analysis agents. Our results highlight the promise and challenges of integrating AI into large-scale fuzzing pipelines.