Agentic Binary Analysis: When AI Becomes the Analyst
Heng Yin, Founder & CEO @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.
DeepDiff: Next-Generation Binary Diffing for Precise Vulnerability and Patch Detection
We're excited to announce the launch of DeepDiff, Deepbits' groundbreaking solution for vulnerability detection and binary diffing. DeepDiff represents a major leap forward in security analysis. It helps security researchers, reverse engineers, and development teams to pinpoint vulnerable functions and generate precise diffing views across binary files with unmatched accuracy.
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.