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malware-dynamic-analysis

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Behavioral analysis of a sample executed in an isolated VM. Use after triage when runtime behavior, C2 traffic, dropped files, persistence, or injection must be observed. Claude produces a tailored VM runbook from triage predictions, then parses the exported text evidence (Procmon CSV, Sysmon JSON/CSV, tshark output, autoruns, strings) on the host to reconstruct behavior and extract IOCs. The analyst runs the VM; Claude never executes the sample.

malware-dynamic-analysis/SKILL.md

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Network or broad shell
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Nov 11, 2026

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Sep 5, 2026
License
MIT

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# Malware Dynamic Analysis

Two halves. **Part A** is a runbook the analyst follows inside the isolated VM (REMnux / FlareVM); you tailor it from triage predictions and it ends with text-format evidence exports. **Part B** is your work: parse that evidence on the host, reconstruct what the sample did, extract behavioral IOCs, and write findings to `analysis_state.md`.

## Execution Model

- **You never execute the sample.** The VM is network-isolated and has no Claude in it. Everything you run is on the host against exported evidence files.
- **Locate skill files.** Scripts and reference files ship in this skill's directory. Set `R="${CLAUDE_PLUGIN_ROOT:-<dir containing this SKILL.md>}"` once (when installed as a plugin `$CLAUDE_PLUGIN_ROOT` is set; otherwise it is this skill folder). Your working directory is the user's analysis workspace, so prefix every script path below with `$R`, e.g. `python3 "$R"/scripts/ioc_extract.py`.
- **Before the VM run:** read the triage findings in `analysis_state.md` and emit a runbook tailored to the predictions (which process names to filter, which Sysmon event IDs matter, expected protocol, expected persistence, expected evasion). Skip the tailoring only if triage is missing — then use the default runbook as-is.
- **After the VM run:** the user hands you an evidence directory. Inventory it, reject binary formats with the conversion command (see **Converting Binary Evidence**), and then work through Part B with Bash and the bundled scripts. Do not ask the user to summarize the evidence for you.
- **Scripts** (relative to this skill dir): `scripts/procmon_summary.py`, `scripts/sysmon_summary.py`; plus `../scripts/ioc_extract.py` at repo root. All stdlib Python 3.
- **Sandbox reports count as evidence.** ANY.RUN / Joe Sandbox / Hybrid Analysis / Threat.Zone (MCP) JSON or text reports go through Part B Step 6.
- **Large files:** `wc -l` first. Use the summary scripts and `grep`, never `cat` a 200k-line Procmon CSV.
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