
attack-path-stitcher
Stitches confirmed single-asset findings into multi-hop attack paths across the organization. Builds a graph where nodes are assets and edges are confirmed exploit hops citing the findings that enable them.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/transilienceai/communitytools --skill attack-path-stitcher- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads attack-path-stitcher automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Attack Path Stitcher
The Validation Run task (#3) produces confirmed findings per asset. Real attacker risk lives in chains: a finding on asset A leaks credentials that enable a finding on asset B that pivots into asset C. This skill builds that graph.
Mounted onto cloud-agent task #6.
Trigger
Cron daily (default 03:00 UTC). May also re-run after a Validation Run task batch completes.
Workflow
- Load inputs.
validated/*.json— every confirmed finding across all engagements.artifacts/org-surface.json— the org-wide surface graph (assets, services, network zones, trust relationships).findings/finding-NNN/evidence/raw-source.txt— for credential / token extraction during stitching.
- Build asset nodes. One node per asset in
org-surface.json, attributed with:tier,services,network_zone,trust_relationships. - Build edges — one edge per detected pivot. See
reference/edge-detectors.mdfor the seven detectors:- Credential reuse (creds leaked on A reused as auth on B)
- Shared secret / API key (same secret appears in two assets' evidence)
- Trust-zone transitive access (A in zone X has implicit reach to B in zone X)
- AD path hops (kerberoast / DC sync / RBCD chains)
- Cloud IAM role chains (assume-role from compromised asset)
- SSRF → internal asset reach (A's SSRF reaches B's internal endpoint)
- Supply-chain (A is a dependency of B per
source-code-scanningSBOM)
- Compute reachability closure. For each tier-
crown_jewelnode, BFS backwards through edges to find every external-facing node that can reach it. Mark these as "entry points". - Write graph to
artifacts/attack-paths.jsonplus a human DOT fileartifacts/attack-paths.dot(renderable with Graphviz).
Implementation runs through tools/chain-merger.py which handles the graph construction. The skill provides the rules the tool consults; the tool does the iteration.
Output
{OUTPUT_DIR}/
artifacts/
attack-paths.json # nodes, edges, entry_points, crown_jewel_paths
attack-paths.dot # Graphviz source
attack-paths.md # ranked list of distinct paths (human read)
attack-paths.json schema:
{
"generated_at": "2026-05-13T03:00:00Z",
"nodes": [
{"id": "asset42", "tier": "revenue", "services": ["http/443"], "zone": "dmz",
"external": true, "findings": ["finding-012", "finding-018"], "max_cvss": 9.8}
],
"edges": [
{"src": "asset42", "dst": "asset77", "detector": "credential-reuse",
"via_findings": ["finding-012", "finding-019"],
"evidence": "credential (userpass) present in evidence of asset42 and asset77",
"feasibility": 1.0}
],
"entry_points": ["asset42", "asset05"],
"confirmed_paths": [
{"jewel": "asset99", "paths": [
{"hops": ["asset42", "asset77", "asset99"],
"edges": [{"src":"asset42","dst":"asset77","detector":"credential-reuse","feasibility":1.0,"via_findings":["finding-012"]},
{"src":"asset77","dst":"asset99","detector":"ssrf-reach","feasibility":1.0,"via_findings":["finding-024"]}],
"feasibility": 1.0, "max_cvss": 9.8, "path_class": "confirmed"}
]}
],
"inferred_paths": [
{"jewel": "asset99", "paths": [
{"hops": ["asset05", "asset99"], "edges": [...],
"feasibility": 0.5, "max_cvss": 7.5, "path_class": "inferred"}
]}
],
"truncation": {
"edge_cap_hit": false, "depth_truncated_count": 0,
"topn_dropped_count": 0, "max_depth": 8, "edge_cap": 50000
}
}
Crucial for RFP §3.3 compliance: confirmed_paths contains ONLY paths where every edge has feasibility 1.0 AND every edge cites at least one validated finding. These are the "confirmed attack paths" the RFP requires. inferred_paths carries topology / supply-chain hops with no PoC evidence — surfaced for analyst review but excluded from remediation SLA buckets by risk-prioritiser.
Rules
- Edges require evidence. An edge is only written if at least one finding's evidence corroborates the pivot. No speculative edges.
- Bi-directional ≠ assumed. If A reaches B, do not infer B reaches A. Each direction needs its own evidence.
- Deduplicate by
(src, dst, detector). Multiple findings that enable the same hop merge into one edge withvia_findingslisting all of them. - Feasibility ∈ {1.0, 0.5, 0.25}. Reliable PoC re-run = 1.0; conditional (race, timing, specific user) = 0.5; theoretical (logically follows but never demonstrated) = 0.25.
- Limit path enumeration. For each crown-jewel, return top-10 paths per class (confirmed + inferred separately) sorted by
feasibility × max_cvss / hop_count. Full graph is inattack-paths.jsonfor downstream prioritisation. - Read-only. Stitcher never re-fires PoCs and never touches
findings/. It only reads. - Bound graph size. Stop edge construction at 50,000 edges; cap path-search depth at
--max-depth(default 8 hops). Emittruncation.edge_cap_hit,truncation.depth_truncated_count, andtruncation.topn_dropped_countin the JSON so downstream consumers can detect silent path loss. - Confirmed vs inferred is non-negotiable. A path appears in
confirmed_pathsonly if every edge has feasibility 1.0 AND every edge has a non-emptyvia_findings. Trust-zone-only, shared-secret-only, and supply-chain-only chains land ininferred_paths. This split is the contract that lets the RFP-§3.3 claim "confirmed attack paths" stand. - Schema enforcement on input.
tools/chain-merger.pydropsvalidated/{id}.jsonrows missingfinding_idorasset, or whoseverdict != "VALID", with stderr WARNs. Upstream validator must comply with the schema inprojects/rfp-3.2/task-03-validation-run.md.
References
reference/edge-detectors.md— the 7 detector rules with concrete signal patterns.projects/rfp-3.3/task-06-attack-path-stitcher.md— cloud-agent runtime contract.
FAQ
What does the attack-path-stitcher skill do?
Stitches confirmed single-asset findings into multi-hop attack paths across the organization. Builds a graph where nodes are assets and edges are confirmed exploit hops citing the findings that enable them.
How do I install attack-path-stitcher?
Run npx skills add https://github.com/transilienceai/communitytools --skill attack-path-stitcher in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/attack-path-stitcher/ (personal) or .claude/skills/attack-path-stitcher/ (project).
Which AI agents can use attack-path-stitcher?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is attack-path-stitcher safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains attack-path-stitcher?
It is published by transilienceai in transilienceai/communitytools. It has 1,407 recorded installs.
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