OverrideFuzz uses semantic-aware grammar fuzzing with reflection to model override hooks and dynamic rebinding, producing coverage growth and inputs that match known vulnerability patterns on CPython, Lua, and QuickJS without discovering new bugs in the evaluation window.
Lee, Joshua Wang, Michael Pelican, David J
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
DARPA's AI Cyber Challenge (AIxCC, 2023--2025) is the largest competition to date for building fully autonomous cyber reasoning systems (CRSs) that leverage recent advances in AI -- particularly large language models (LLMs) -- to discover and remediate vulnerabilities in real-world open-source software. This paper presents the first systematic analysis of AIxCC. Drawing on design documents, source code, execution traces, and discussions with organizers and competing teams, we examine the competition's structure and key design decisions, characterize the architectural approaches of finalist CRSs, and analyze competition results beyond the final scoreboard. Our analysis reveals the factors that truly drove CRS performance, identifies genuine technical advances achieved by teams, and exposes limitations that remain open for future research. We conclude with lessons for organizing future competitions and broader insights toward deploying autonomous CRSs in practice.
fields
cs.CR 4years
2026 4representative citing papers
Chai uses AI to enhance differential testing for cryptographic misuse, cataloging library-level flaws and propagating them to find over 100 vulnerabilities including a critical one in a widely deployed SSL library.
Revelio combines LLMs, static analysis, and sanitizer-verified PoVs to scalably discover memory safety vulnerabilities in repository-scale code, finding 19 new bugs in long-fuzzed projects at low cost.
QuartetFuzz introduces the Four Principles framework for harness correctness and deploys an autonomous LLM agent that produces verified harnesses, yielding 29 confirmed bugs across 23 projects and identifying violations in existing harnesses.
citing papers explorer
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OverrideFuzz: Semantic-Aware Grammar Fuzzing for Script-Runtime Vulnerabilities
OverrideFuzz uses semantic-aware grammar fuzzing with reflection to model override hooks and dynamic rebinding, producing coverage growth and inputs that match known vulnerability patterns on CPython, Lua, and QuickJS without discovering new bugs in the evaluation window.
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Chai: Agentic Discovery of Cryptographic Misuse Vulnerabilities
Chai uses AI to enhance differential testing for cryptographic misuse, cataloging library-level flaws and propagating them to find over 100 vulnerabilities including a critical one in a widely deployed SSL library.
-
Revelio: Cost-Efficient Agentic Memory Safety Vulnerability Detection For Repository-Scale Codebases
Revelio combines LLMs, static analysis, and sanitizer-verified PoVs to scalably discover memory safety vulnerabilities in repository-scale code, finding 19 new bugs in long-fuzzed projects at low cost.
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Quality-Assured Fuzz Harness Generation via the Four Principles Framework
QuartetFuzz introduces the Four Principles framework for harness correctness and deploys an autonomous LLM agent that produces verified harnesses, yielding 29 confirmed bugs across 23 projects and identifying violations in existing harnesses.