The paper introduces LogicEval, a systematic framework and dataset (LogicDS) to evaluate automated repair techniques for logical software vulnerabilities, finding that prompt sensitivity and context l…

Walma is a machine learning framework that uses memory snapshot classification to detect memory corruption and external tampering in WebAssembly, demonstrating practical feasibility with low overhead.

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 RAVEN, a Retrieval-Augmented Vulnerability Exploration Network, which uses LLM agents and RAG to automatically generate comprehensive, structured vulnerability analysis reports fo…

The paper introduces TLSCheck 2.0, an enhanced memory forensics plugin for Volatility 3, designed to efficiently detect and analyze suspicious TLS callbacks in process memory.

MemHint is a neuro-symbolic static analysis pipeline that significantly improves memory leak detection in C/C++ by combining LLM semantic understanding with Z3 symbolic reasoning, detecting more leaks…

AutoSOUP is a system that automates component-level memory-safety verification by generating Safety-Oriented Unit Proofs, leveraging a hybrid LLM-based architecture to overcome manual workflow limitat…

NeuroLog is a novel, build-free neuro-symbolic pipeline that combines LLM-derived dataflow facts, Datalog, and SMT solving to systematically discover and synthesize exploitable memory safety vulnerabi…

The paper introduces VULNSCOUT-C, a compact, specialized transformer model that achieves state-of-the-art performance in C code vulnerability detection while maintaining low inference cost, making it…

VeriCWEty proposes an embedding-based framework to detect and classify common software vulnerabilities (CWEs) in Verilog RTL code at both module and line levels, achieving high detection accuracy.

The paper introduces SLYP, an agentic pipeline that significantly improves the discovery of race condition vulnerabilities in Windows COM binaries and autonomously generates verified proof-of-concept…

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 proposes a novel framework, Speculative Non-Interference (SNI), and a tool, Spectector, to formally detect and verify security vulnerabilities arising from complex interactions of multiple s…

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…

This paper systematically studies memory poisoning attacks in LLM agents, identifying multiple vulnerabilities and proposing a new benchmark to assess the risk.

FuzzingBrain V2 is a multi-agent LLM system that significantly improves automated vulnerability discovery by ensuring all reported bugs are fuzzer-reproducible and handling complex cross-function depe…

The paper introduces Patch2Vuln, a pipeline that uses an LLM agent to reconstruct security vulnerabilities by analyzing differences between old and new Linux binary packages, successfully localizing p…

The paper introduces a novel memory forensics framework to perform runtime analysis of Go malware, successfully recovering critical execution state and artifacts that are invisible to traditional stat…

The paper introduces GRIEF, a greybox fuzzer that discovers critical, concurrency-related vulnerabilities in LLM serving systems by treating timed multi-request traces as inputs, finding issues like c…

The paper introduces NeuroTaint, a novel taint tracking framework that adapts information flow analysis for LLM agents by modeling taint propagation as semantic transformation and causal influence, si…