This paper evaluates modern reentrancy detection tools, finding that leading LLMs significantly outperform most existing static analyzers and ML models on both real-world and handcrafted benchmarks.

LibScan is an automated framework that detects eight categories of smart contract library misuse by combining LLM-based semantic reasoning with rule-based analysis, achieving 85.15% accuracy on real-w…

This paper analyzes the Solana Rug Pull ecosystem by creating a large-scale, manually verified dataset of fraudulent tokens, identifying three key behavioral patterns, and characterizing the resulting…

The paper introduces SCDBench, a comprehensive benchmark dataset and methodology that rigorously evaluates LLM-based smart contract decompilers, finding that while frontier models can produce compilab…

The paper introduces SCDBench, a comprehensive benchmark dataset and methodology that rigorously evaluates LLM-based smart contract decompilers, finding that while frontier LLMs can generate compilabl…

SAILOR automates the construction of symbolic execution harnesses by combining static analysis and LLM-based synthesis, significantly improving the scalability and effectiveness of vulnerability disco…

The paper introduces a novel multi-LLM orchestration system combined with symbolic execution to successfully detect memory vulnerabilities in uncompilable, incomplete Rust CVE code snippets, achieving…

The paper introduces Sentinel, a novel proxy-based system that achieves comprehensive, type-agnostic reentrancy protection for smart contracts by intercepting all external calls.

GoAT-X introduces a novel framework that structures cross-chain smart contract auditing as a Graph of Auditing Thoughts, significantly improving the detection of complex, semantic vulnerabilities in m…

The paper introduces SymTEE, an LLM-assisted symbolic execution framework that detects missing input validation vulnerabilities in TEE applications without needing complex, real TEE setups.

The paper introduces Heimdall, an automated pipeline that uses LLMs and formal verification to safely and automatically migrate legacy, potentially buggy eBPF programs written in C to memory-safe Rust…

The paper introduces Phoenix, a training-free multi-agent framework that detects code vulnerabilities by synthesizing project-specific behavioral contracts, significantly outperforming existing method…

The paper introduces COBALT, a Z3 SMT-based formal verification engine, to proactively detect arithmetic vulnerabilities (CWE-190/191/195) in the critical infrastructure surrounding frontier AI models…

EvoPoC introduces a knowledge-driven agentic system that automates the synthesis of verifiable and economically viable exploits for DeFi smart contracts, achieving high recall and significant revenue…

The paper introduces PSR extsuperscript{2}, a novel static analysis framework that significantly improves the detection of atomicity violations in smart contracts by combining structural path searchin…

The paper introduces Sol2Vy, a framework that enables cross-language knowledge transfer from Solidity to Vyper, allowing effective vulnerability detection in low-resource smart contracts without needi…

The paper introduces SEC-bench Pro, a rigorous benchmark for evaluating LLM-based bug hunting on complex software, finding that even advanced agents struggle with long-horizon security tasks.

This paper conducts a large-scale empirical study demonstrating that Java library exploits can accurately identify affected versions, achieving high recall and precision, and proposes strategies for e…

The paper introduces SecPI, a fine-tuning pipeline that teaches reasoning language models (RLMs) to autonomously internalize structured security reasoning, significantly improving secure code generati…

The paper introduces an LLM-based framework that uses vulnerability-specific prompting and a large-scale dataset to achieve high-precision, scalable detection of multiple smart contract vulnerabilitie…