# AI-Assisted Malware Analysis Tips > Source: > Published: 2026-09-24 00:00:00+00:00 An AI agent can amplify a malware analyst's skills, but the analyst must direct it and verify its findings. These tips cover how to split the work, set up a contained lab, delegate tasks, and question conclusions. This cheat sheet outlines tips for directing an AI agent as a part of your malware analysis workflow and for verifying the agent’s findings. To print it, use the one-page [PDF](https://zeltser.com/media/docs/ai-assisted-malware-analysis-tips.pdf) version. You can also edit the [Word](https://zeltser.com/media/docs/ai-assisted-malware-analysis-tips.docx) version to customize it for your own needs. ## Team Up With the AI Agent | | Agent-Led | Analyst-Led | |---|---|---| | *Plans the work* | Agent | Analyst | | *Runs tools* | Agent | Both | | *Interprets output* | Agent | Both | | *Verifies findings* | Analyst | Analyst | | *Owns conclusions* | Analyst | Analyst | ## Set Up a Contained Lab - The lab is a controlled environment for running tools and detonating malware (e.g., [REMnux](https://remnux.org/) ,[FLARE-VM](https://github.com/mandiant/flare-vm) ). - Perform the analysis work in a network-isolated lab; snapshot each system’s clean state. - Run the AI agent in the lab, because the sample may contain prompt injection meant to hijack it. - Allow only the connections the agent needs, such as to the AI provider (e.g., [restrict-egress](https://github.com/REMnux/distro/blob/master/files/restrict-egress) ). - Hide earlier reports and other analysis artifacts from the agent or it may reuse their conclusions. - MCP servers give the agent access to the lab’s tools; the [REMnux MCP server](https://github.com/REMnux/remnux-mcp-server) also includes tool guidance. - Use an AI provider or local model consistent with your organization’s or clients’ expectations. - Have the agent ask for your approval before it uploads files, installs tools, or runs the sample. - In agent-led runs, consider capping run time, so the agent can’t retry a failing approach for hours. ## Direct the Agent - Describe the sample in neutral terms, since the AI agent may adopt the verdict your wording implies. - Ask bounded questions, such as what a function does, so the agent returns answers you can check. - If you have a theory about what some code does, ask the agent for alternatives and how to test each one. - Ask the agent to cite the tool output behind each finding, then confirm that output is in its log. - Have the agent search large logs rather than read them whole, so they don’t fill its context window. - For a fresh perspective, start a new session with only the findings you want the agent to consider. - If AI safeguards halt the agent, retry, narrow the request, or join your provider’s researcher program. ## Delegate the Right Tasks - *Triage:* Drafting initial findings for you to verify. - *Parsing:* Writing a script that parses bulky tool output, such as an emulator’s report. - *Decoding:* Writing a script that recovers encoded strings or code in a deterministic way. - *Behavior analysis:* Querying Process Monitor logs, via an integration such as[ProcmonMCP](https://github.com/JameZUK/ProcmonMCP) . - *Code review:* Analyzing and explaining code in Ghidra via an integration such as[GhidrAssistMCP](https://github.com/jtang613/GhidrAssistMCP) . - *Debugging:* Capturing runtime details in a debugger via an integration such as[x64dbg Automate MCP](https://github.com/REMnux/x64dbg-skills-opencode) . - *Automation:* Writing a script (e.g., for a debugger) that repeats a procedure you performed by hand. - *Detection:* Drafting YARA, Sigma, or other rules to detect the sample or the adversary on other systems. - *Reporting:* Drafting a report to capture and share the analysis findings using a[report template](https://zeltser.com/malware-analysis-report) . ## Verify the Agent’s Claims - Learn malware analysis essentials, including assembly, to interpret and validate agent findings. - Expect some AI agent conclusions to be wrong, especially in agent-led work, even if they sound right. - Confirm each important claim with another method, such as behavior or code analysis. - Run core tools yourself to learn what they cover, then delegate broader work and check key claims. - Check that each indicator came from the sample, not from a tool’s own banner or help text. - Check that the agent doesn’t report a tool that failed or timed out as having found nothing. - Confirm the agent covered every item you assigned before accepting that it’s done. - When a claim proves wrong, have the agent drop it and recheck any conclusion that relied on it. - Challenge the agent when it calls packing or anti-debugging malicious, as clean software uses both. - When the agent uses threat intel, check which findings it derived from the sample itself. - Use the model’s training for general knowledge, but supply threat intel newer than its training. ## Question Each Conclusion - What exactly is the claim? - Which evidence supports it? - Is the evidence a string, code capable of the claimed behavior, or the sample observed doing it? - Does any tool output or your own analysis contradict the claim? What remains unknown? - Before acting on the claim, such as by blocking a domain, what should you verify yourself? This cheat sheet is distributed under the [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/).