Reveree: Diagnosing LLM Reverse-Engineering Agents
Reveree provides a diagnostic framework for evaluating LLM-based reverse-engineering agents beyond simple CTF scores, helping security teams understand where autonomous binary analysis breaks down.
Summary written by editorial AI · Source link below
arXiv:2609.01185v1 Announce Type: new Abstract: Reverse engineering (RE) is critical to security tasks such as malware analysis and vulnerability discovery, and large language model (LLM) agents are increasingly able to perform it autonomously. Capture-the-flag (CTF) RE challenges have become the standard proxy for measuring this capability, but evaluation rests on a single criterion: whether the agent captures the flag. This solve rate reveals neither where in the RE process an agent fails nor
Editorial Analysis
As LLM agents are increasingly applied to malware analysis and vulnerability research, understanding their failure modes is essential before trusting automated outputs.
Before deploying LLM-based RE tools in production malware triage, benchmark them with Reveree-style diagnostics to quantify reliability limits.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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