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 MemTrace, a framework that treats LLM memory pipelines as traceable graphs to systematically diagnose and automatically correct memory-related errors, boosting performance by up t…

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 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 codebadger, a Model Context Protocol (MCP) server that integrates Joern's Code Property Graph (CPG) with LLMs, enabling large language models to perform large-scale, semantic prog…

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…

Veritas is a semantically grounded framework that detects memory corruption vulnerabilities in stripped binaries by combining static analysis, LLM-based reasoning, and runtime validation, achieving hi…

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 study conducts a large-scale longitudinal analysis of CodeQL, finding that while the tool is effective at detecting vulnerabilities, its detection capabilities are not guaranteed to be stable acr…

MemMorph introduces a novel memory poisoning attack that biases LLM agent tool selection by injecting crafted records into the agent's long-term memory, achieving high success rates even against moder…

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…

The paper proposes a general, compiler-integrated framework for secure content composition that minimizes the syntactic difference between secure and insecure coding practices.

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…

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

This study formally verified 3,500 AI-generated code artifacts and found that a majority (55.8%) contain exploitable security vulnerabilities, regardless of the LLM used.

This paper investigates how Byte-Pair Encoding (BPE) tokenization causes Code LLMs to disproportionately memorize certain types of secrets, a phenomenon termed 'gibberish bias'.

The paper analyzes LLM vulnerability detection using mechanistic interpretability, finding that models primarily rely on safety detectors rather than direct vulnerability signature recognition.

This paper proposes using transformer-based models on program slices to accurately detect C/C++ software vulnerabilities by capturing both local and global contextual information.

The paper proposes a novel symbolic execution technique that combines speculative library preloading and custom software hooks to recover Control Flow Graphs (CFGs) from binaries that use dynamic code…