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by mrphrazer · MCP Server · ★ 287
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Agentic Malware Analysis Automated deep malware reverse engineering driven by AI agents. A Kali-based Docker environment pairs 50+ RE tools with MCP-connected disassembler backends ([Binary Ninja][binary-ninja-headless-mcp] or [Ghidra][ghidra-headless-mcp]) and a structured multi-phase orchestrator skill that turns a raw binary into a case directory of ranked evidence, validated hypotheses, component maps, and a prioritized deep-analysis plan -- with no human interaction required. Ready for [Claude Code][claude-code] and [Codex CLI][codex-cli]. See the companion blog post [Building a Pipeline for Agentic Malware Analysis][blog-post] for background, a case study, and evaluation. Why Initial malware analysis involves a number of routine steps: collecting hashes and compiler artifacts, extracting strings, inspecting imports, running YARA and capa, correlating the results, and identifying code areas for closer inspection. These steps provide the basis for deeper analysis, but they are often repetitive and time-consuming. This repository automates much of that workflow.
| Stars | 287 |
| Forks | 44 |
| Language | YARA |
| Category | MCP Server |
| License | GPL-2.0 |
| Quality Score | 74.360141389964/100 |
| Last Updated | 2026-03-22 |
| Created | 2026-03-18 |
| Platforms | claude-code, cli, codex, mcp |
| Est. Tokens | ~21k |
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agentic-malware-analysis is Agentic malware analysis environment with MCP-connected disassemblers, RE tooling, and structured workflows for Claude Code and Codex CLI.. It is categorized as a MCP Server with 287 GitHub stars.
agentic-malware-analysis is primarily written in YARA.
You can find installation instructions and usage details in the agentic-malware-analysis GitHub repository at github.com/mrphrazer/agentic-malware-analysis. The project has 287 stars and 44 forks, indicating an active community.
agentic-malware-analysis is released under the GPL-2.0 license, making it free to use and modify according to the license terms.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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