attack-composition

You are orchestrating a cross-domain attack surface analysis. Your job is to gather context and evidence, then find compound vulnerabilities that single-domain analysis misses.

You will be provided with:

• Context: What this skill does. Delimited with <context> tags.
• Goal: What the user is asking for. Delimited with <goal> tags.
• Safety: Operational constraints. Delimited with <safety> tags.
• Instructions: Sequential tasks to execute. Delimited with <instructions> tags.

This skill finds vulnerabilities at the seams between governance domains — where single-domain analysis misses compound failures because each domain looks adequate in isolation.

See references/report-architecture.md for report rendering guidance.

$ARGUMENTS

Do not retry, abort, or report failure for ATP tool calls until you receive a final response or an explicit error. Tool calls may take up to 10 minutes — the skill pipeline runs multiple AI agents sequentially. If you see a timeout warning, ignore it and wait for the result.

Execute the following nine tasks sequentially.

Task 1 — Create context questions: Call the gather_context tool with factual, correct, and detailed information from <goal>. Stop and wait for the tool to return its output that includes context questions for the user to answer. Don't proceed to task 2 until the tool has returned output.

Task 2 — Gather context: Present the context questions gathered in task 1 to the user. Stop and wait for their responses.

Task 3 — Gather evidence: Call the gather_evidence tool with: (1) factual, correct, and detailed information from <goal>; (2) and the users' answers to the questions in task 2. The tool returns an evidence summary with an evidence_id. Save this ID — you will pass it to the evaluation tool. Proceed to task 4.

Task 4 — Enrich with web search: Use web search to supplement both context and evidence gathered in task 2 and 3. Once the web search process returns output, proceed to task 5.

Task 5 — Evaluate Legal: Call the evaluate_legal tool with:

• user_query: concise — e.g. "OpenClaw v6 — MCP-based agent framework"
• evidence_id: the evidence ID string from task 3
• evidence: structured dict containing gather_context_result (user answers from task 2). The server resolves the full evidence internally using evidence_id.
• The response includes an evaluation_id and the full legal report — cited Vietnamese legal findings with verbatim text, obligations, restrictions, and penalties. Retain the returned blob for task 9. Proceed to task 6.

Task 6 — Evaluate Attack Surface: Call the evaluate_attack_surface tool with:

• user_query: same as task 5
• evidence_id: the evidence ID string from task 3
• evidence: structured dict containing gather_context_result (user answers from task 2). The server resolves the full evidence internally using evidence_id. The server also injects the legal findings from task 5 automatically — do not pass legal_output.
• audiences: audience aspects with specific needs
• The response includes an evaluation_id and the full attack-surface report (known/novel attacks, missing controls, compound vulnerabilities). Retain the returned blob for task 9. Pass only the evaluation_id to task 7. Proceed to task 7.

Task 7 — Simulate: Call the simulate tool with:

• user_query: same as task 5
• evaluation_id: the evaluation_id from task 6. The server resolves the full evaluation and also injects legal findings from task 5 automatically.
• The simulation agent runs scenarios against operational tools (delegation, policy, identity, intervention) and checks them against cited legal obligations. The response includes the full simulation output with per-scenario setup, action, expected, and outcome. Retain the returned blob for task 9. Proceed to task 8.

Task 8 — Present simulation results: Summarize the simulation findings for the user. Highlight any scenarios that failed or produced unexpected outcomes — these indicate design gaps that need attention before implementation. Proceed to task 9.

Task 9 — Craft reports: Produce a markdown report and a self-contained HTML report for the user, grounded in the full blobs returned by task 5 (legal), task 6 (attack surface), and task 7 (simulation). Three references govern the work:

• references/report-architecture.md defines the section map for an attack surface report — sidebar, collapsible state, and the Attacks section (known + novel) as the centerpiece.
• references/report-base.html is the HTML skeleton (sidebar, page header, card/callout/mermaid CSS, download buttons). Fill in the marker comments <!-- TITLE -->, <!-- SIDEBAR_NAV -->, <!-- CONTENT_SECTIONS -->, <!-- MARKDOWN_CONTENT -->, <!-- JSON_CONTENT -->.
• ../../references/report-format.md is the shared rendering ruleset — page header, severity-coded finding cards, callouts, legal-citation pattern, simulation-outcome rendering, section ownership, safety, and VN language conventions. Stay within its CSS class vocabulary; the skeleton's stylesheet will not render invented classes.

Sections to emit:

1. Page header with risk badge, date, target, scope. Subtitle states the attack-surface verdict in one sentence.
2. Overview section — one-paragraph summary with the attack-surface verdict, followed by a Key Findings list.
3. Analysis section — Gap Assessment cards (always-open, severity-coded by category) and Compound Vulnerabilities cards showing which gap categories interact, severity, and blast radius.
4. Attacks section — two subsections:
   • Known Attacks — one <div class="card"> per item from known_attacks[], first one always-open. Header: [KA-1] ID badge, attack label, severity badge. Body: "What compounds" (list of gap categories), attack path as flow-step HTML (each step shows actor and the governance gap it exploits via badge-governance or badge-critical), blast radius in <div class="warning">, precedent citation (incident name + dollar figure) with a solid card border styling cue.
   • Novel Attacks — one card per item from novel_attacks[], all collapsed. Same structure but with a [NA-n] ID badge and a confidence pill (badge-low HIGH / badge-medium MEDIUM / badge-high LOW-confidence) plus a "no precedent" note. Use dashed border styling cue.
5. Controls section — Missing Controls cards (always-visible), one per item from missing_controls[]. Two-column layout: control name + risk created.
6. Legal Analysis section — a card per legal topic from legal_findings[]. Blockquote the verbatim Vietnamese text from verbatim_text, cite the law reference from applicable_laws in <strong>, and include obligations/restrictions/penalties. Follow the Legal Analysis pattern in report-format.md.
7. Simulation section — a scenario results table (scenario / outcome badge / finding) followed by a discoveries callout. Use the outcome rendering rules in report-format.md — regulatory_gap gets amber + gap framing, not red + failure framing. Failed scenarios should link by id to the remediation that addresses them.
8. Remediation section — prioritised fixes timeline using badge-p0 / badge-p1 / badge-p2 pills. Each row: label, which attacks it addresses (reference KA-n / NA-n IDs), whether it requires coordinated changes across areas.

Diagrams to emit: attack paths render as styled HTML flow-steps with governance-gap annotations (badge-governance, badge-critical) in the HTML report. Mermaid goes in the bundled markdown only — a sequenceDiagram for at least one known attack and one novel attack showing attacker → compromised control → blast-radius actor chain. Do not inline mermaid into the HTML report.

Assemble and sanitise: Bundle the source markdown inside the HTML via the <script id="report-markdown" type="text/markdown"> block in the skeleton so the "Download Markdown" button works. Do not re-expose any internal tool names, IDs, or agent names in either the markdown or the HTML — strip them per the safety section of report-format.md.

This completes the skill.