FunFuzz: An LLM-Powered Evolutionary Fuzzing Framework

• URL: http://arxiv.org/abs/2605.02789v1
• Date: Mon, 04 May 2026 16:30:47 GMT
• Title: FunFuzz: An LLM-Powered Evolutionary Fuzzing Framework
• Abstract summary: We present FunFuzz, a multi-island evolutionary fuzzing framework that runs several isolated searches in parallel.<n>We evaluate FunFuzz on compiler fuzzing, where inputs are source programs and success is measured by compiler coverage and unique compiler-internal failures.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern fuzzers increasingly use Large Language Models (LLMs) to generate structured inputs, but LLM-driven fuzzing is sensitive to prompt initialization and sampling variance, which can reduce exploration efficiency and lead to redundant inputs. We present FunFuzz, a multi-island evolutionary fuzzing framework that runs several isolated searches in parallel and periodically migrates high-value candidates to maintain diversity. FunFuzz derives initial generation prompts from documentation and initializes islands with topic-specific instructions, then continuously adapts prompts using feedback-guided selection. During fuzzing, candidates are prioritized by incremental compiler coverage, while compiler-internal failure signals are used to identify crash-inducing inputs. We evaluate FunFuzz on compiler fuzzing, where inputs are source programs and success is measured by compiler coverage and unique compiler-internal failures. Across repeated 24-hour campaigns on GCC and Clang, FunFuzz achieves higher compiler coverage than previous LLM-driven baselines and discovers more unique failure-triggering inputs.

Related papers

• SDLLMFuzz: Dynamic-static LLM-assisted greybox fuzzing for structured input programs [6.165429775333409]
  SDLLMFuzz is a dynamic-static LLM-assisted greybox fuzzing framework for structured-input programs.<n>Our approach integrates structure-aware seed generation with static crash analysis, forming a unified feedback loop.<n> Experimental results show that SDLLMFuzz significantly outperforms traditional greybox fuzzers.
  arXiv  Detail & Related papers  (2026-04-20T03:10:31Z)
• From Early Encoding to Late Suppression: Interpreting LLMs on Character Counting Tasks [49.57538588967748]
  Large language models (LLMs) exhibit failures on elementary symbolic tasks such as character counting in a word, despite excelling on complex benchmarks.<n>We uncover a consistent phenomenon across modern architectures, including LLaMA, Qwen, and Gemma.<n>We show that symbolic reasoning failures in LLMs are not due to missing representations or insufficient scale, but arise from structured interference within the model's computation graph.
  arXiv  Detail & Related papers  (2026-04-01T11:40:12Z)
• Prism: Efficient Test-Time Scaling via Hierarchical Search and Self-Verification for Discrete Diffusion Language Models [96.0074341403456]
  Inference-time compute has re-emerged as a practical way to improve LLM reasoning.<n>Most test-time scaling (TTS) algorithms rely on autoregressive decoding.<n>We propose Prism, an efficient TTS framework for dLLMs.
  arXiv  Detail & Related papers  (2026-02-02T09:14:51Z)
• Discovering 100+ Compiler Defects in 72 Hours via LLM-Driven Semantic Logic Recomposition [15.27741331581011]
  We propose FeatureFuzz, a compiler fuzzer that combines features to generate programs.<n>Over 24-hour campaigns, FeatureFuzz uncovered 167 unique crashes, which is 2.78x more than the second-best fuzzer.<n>Through a 72-hour fuzzing campaign, FeatureFuzz identified 113 bugs in GCC and LLVM, 97 of which have already been confirmed by compiler developers.
  arXiv  Detail & Related papers  (2026-01-18T11:33:45Z)
• Fuzzwise: Intelligent Initial Corpus Generation for Fuzzing [14.734454356396157]
  In mutation-based greybox fuzzing, generating high-quality input seeds for the initial corpus is essential.<n>FuzzyWise integrates separate phases for generating a large corpus and subsequently minimizing it.<n>FuzzyWise achieves high code coverage and triggers more runtime errors compared to the baselines.
  arXiv  Detail & Related papers  (2025-12-24T22:17:29Z)
• Hybrid Fuzzing with LLM-Guided Input Mutation and Semantic Feedback [0.0]
  I present a hybrid fuzzing framework that integrates static and dynamic analysis with Large Language Model (LLM)-guided input mutation and semantic feedback.<n>Our method achieves faster time-to-first-bug, higher semantic diversity, and a competitive number of unique bugs compared to state-of-the-art fuzzers.
  arXiv  Detail & Related papers  (2025-11-06T02:38:24Z)
• Semantic-Aware Fuzzing: An Empirical Framework for LLM-Guided, Reasoning-Driven Input Mutation [0.5336076422485075]
  Security vulnerabilities in Internet-of-Things devices, mobile platforms, and autonomous systems remain critical.<n>Traditional mutation-based fuzzers primarily perform byte or bit-level edits without semantic reasoning.<n>We present an open-source framework that integrates reasoning LLMs with AFL++ on Google's FuzzBench.
  arXiv  Detail & Related papers  (2025-09-23T19:57:29Z)
• LLAMA: Multi-Feedback Smart Contract Fuzzing Framework with LLM-Guided Seed Generation [56.84049855266145]
  We propose a Multi-feedback Smart Contract Fuzzing framework (LLAMA) that integrates evolutionary mutation strategies, and hybrid testing techniques.<n>LLAMA achieves 91% instruction coverage and 90% branch coverage, while detecting 132 out of 148 known vulnerabilities.<n>These results highlight LLAMA's effectiveness, adaptability, and practicality in real-world smart contract security testing scenarios.
  arXiv  Detail & Related papers  (2025-07-16T09:46:58Z)
• $\mathbb{USCD}$: Improving Code Generation of LLMs by Uncertainty-Aware Selective Contrastive Decoding [64.00025564372095]
  Large language models (LLMs) have shown remarkable capabilities in code generation. The effects of hallucinations (e.g., output noise) make it challenging for LLMs to generate high-quality code in one pass. We propose a simple and effective textbfuncertainty-aware textbfselective textbfcontrastive textbfdecoding.
  arXiv  Detail & Related papers  (2024-09-09T02:07:41Z)
• FuzzCoder: Byte-level Fuzzing Test via Large Language Model [46.18191648883695]
  We propose to adopt fine-tuned large language models (FuzzCoder) to learn patterns in the input files from successful attacks. FuzzCoder can predict mutation locations and strategies locations in input files to trigger abnormal behaviors of the program.
  arXiv  Detail & Related papers  (2024-09-03T14:40:31Z)
• Parallel Decoding via Hidden Transfer for Lossless Large Language Model Acceleration [54.897493351694195]
  We propose a novel parallel decoding approach, namely textithidden transfer, which decodes multiple successive tokens simultaneously in a single forward pass. In terms of acceleration metrics, we outperform all the single-model acceleration techniques, including Medusa and Self-Speculative decoding.
  arXiv  Detail & Related papers  (2024-04-18T09:17:06Z)
• NAPG: Non-Autoregressive Program Generation for Hybrid Tabular-Textual Question Answering [52.10214317661547]
  Current numerical reasoning methods autoregressively decode program sequences. The accuracy of program generation drops sharply as the decoding steps unfold due to error propagation. In this paper, we propose a non-autoregressive program generation framework.
  arXiv  Detail & Related papers  (2022-11-07T11:25:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.