Building Agentic Malware Analysis Pipelines: From LLM Assistance to Autonomous Workflows
Key Takeaways
- ▸AI agents with tool-use capabilities can autonomously perform meaningful portions of malware analysis workflows, moving beyond LLMs' traditional supporting role in reverse engineering
- ▸Structured agentic pipelines—combining agents with appropriate tooling, environment setup, and workflow guidance—substantially outperform general-purpose agents without such constraints
- ▸The Model Context Protocol (MCP) standardizes how agents discover and invoke external analysis tools, enabling integration of disassemblers, decompilers, and custom scripts into agent workflows
Summary
Anthropic researcher oneron has published a comprehensive guide on building agentic malware analysis pipelines that leverage large language models to automate reverse engineering workflows. The work demonstrates how AI agents—LLMs equipped with tool-use capabilities and looping mechanisms—can move beyond passive assistance to actively analyze binaries, make decisions about which analysis steps to pursue, and iteratively refine understanding of malware functionality. The article contrasts traditional LLM-assisted reverse engineering, where humans remain decision-makers, with agentic approaches where models autonomously use tools like disassemblers, decompilers, and string extractors to investigate malware samples. The research includes a concrete malware case study showing how structured agentic workflows outperform general-purpose agents operating without guidance, while also highlighting current limitations of the approach.
- Agentic malware analysis is already practical for various security tasks including CTF challenges, fuzzing harness generation, and exploit generation
Editorial Opinion
This work represents a significant evolution in AI-assisted cybersecurity, demonstrating that autonomous agents can handle substantial portions of expert-level malware analysis. While the structured pipeline approach shows promise over general-purpose agents, the research also acknowledges meaningful limitations remain—suggesting this is a maturing but not yet fully autonomous field. The practical focus on standardized tooling through MCP is particularly valuable for security practitioners seeking to integrate these capabilities into existing workflows.
