DeepDiff: Next-Generation Binary Diffing for Precise Vulnerability and Patch Detection
Sheng Yu, Research Scientist @Deepbits
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All posts →Lessons from Building Agentic Binary Analysis, Part 1: LLMs Skim, They Don't Explore.
The most fundamental failure mode of LLM-powered binary analysis: models evaluate binaries by their most obvious features and stop there. This post walks through how Dr. Binary addresses it with an exhaustive analysis tool that processes every function before the LLM draws any conclusions.
Agentic Binary Analysis: When AI Becomes the Analyst
Exploring how autonomous AI agents are revolutionizing binary code analysis through self-directed reasoning, collaborative multi-agent systems, and adaptive threat detection strategies that operate without human intervention.
How Binary Code AI Changes Malware Defenses?
Existing commercial malware detection engines have a relatively low first-day detection rate for newly discovered samples, and it takes two to three days to gradually reach a detection rate of more than 90%. This leaves a large attack surface for malware. To solve this problem, we developed a new technique that can identify new malware at first sight, without the need for periodic retraining of machine learning models.