This paper proposes approaches for guiding a fuzzer using feedback derived from a control-flow-graph-like structure during the fuzzing of black-box devices and obfuscated compiled binaries.

The paper proposes DALC-CT, a dynamic analysis tool that verifies the constant-time property of cryptographic code by comparing instruction mix distributions across multiple execution traces.

This paper investigates the forensic analysis of agentic AI systems using OpenClaw, proposing an agent artifact taxonomy and highlighting the challenges posed by non-determinism in agent-mediated exec…

The paper introduces TraceSafe-Bench, a comprehensive benchmark, and finds that securing LLM agents requires jointly optimizing for structural reasoning and safety alignment to mitigate risks during m…

The paper introduces Trace, a forensic framework that fingerprints the model family of autonomous AI attack agents using terminal behavior, enabling subsequent prompt injection to extract system promp…

The paper introduces Test-Driven Forensics, an approach that treats forensic expectations as executable tests to detect and measure the degradation of repeatability and confidence in digital forensic…

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 RAGCharacter, a forensic framework that enables black-box, character-level traceback to pinpoint the exact poisoned span in retrieved evidence responsible for a misgeneration even…

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…

The paper proposes TRACE, a novel agentic jailbreaking framework that successfully bypasses safety mechanisms of advanced LLM agents by decomposing malicious tasks and disguising harmful subtasks with…

The paper introduces OpenClawBench, a large-scale dataset and framework for measuring process-side anomalies in real-world agent execution trajectories, demonstrating that task success does not guaran…

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…

This survey provides a systematic framework and taxonomy for evidence tracing and execution provenance in LLM agents, addressing the difficulty of verifying and auditing complex agent behaviors.

The paper introduces FORGE, a feedback-driven execution system that improves LLM-based binary analysis by interleaving reasoning and tool interaction, achieving high-quality vulnerability discovery on…

This paper evaluates the improvement of statistical fault localization by augmenting it with execution features.

The paper introduces DrvHorn, a novel automated tool that detects reference counting bugs in Linux kernel drivers by transforming the verification problem into an assertion checking task, successfully…

The paper introduces BOUNDARY FLOW, an LLVM-based framework that enhances kernel fuzzing and analysis by extracting per-task, state-aware data-flow information (arguments and return values) at functio…

This paper identifies a behavioral invariant in LLM agents under memory poisoning attacks, which can be used to detect such attacks with high accuracy.