security-pass

## Context An MCP server is a new attack surface with unique properties — tool output feeds back into the LLM's context, scopes gate what the model can do on the user's behalf, and per-request state must stay tenant-scoped. This skill walks a server through eight axes shaped around what the server builder actually controls. Framework-level concerns (transport, JSON-RPC parsing, auto-correlation, error classification) are out of scope — `mcp-ts-core` handles those. **Read the code. Don't trust patterns from memory.** ## When to Use - Before a release - After adding or modifying a batch of handlers or services - Periodically (quarterly-ish) - User asks for a "security review", "audit", "hardening pass", or similar ## Inputs Gather before starting. Ask if unclear: 1. **Scope** — whole server, specific module, or recent diff? 2. **Known concerns** — anything the user already suspects? 3. **Deployment context** — multi-tenant? public network? auth mode? (stdio / local-http / public-http behave differently) 4. **Severity floor** — report all findings, or skip medium/low? ## Steps ### 1. Build the map Surface what you're auditing before diving in. Paths below assume the `mcp-ts-core` layout — adjust to your repo. ```bash find src/mcp-server/tools/definitions -name "*.tool.ts" | sort find src/mcp-server/resources/definitions -name "*.resource.ts" 2>/dev/null | sort find src/mcp-server/prompts/definitions -name "*.prompt.ts" 2>/dev/null | sort find src/services -maxdepth 1 -mindepth 1 -type d | sort ``` Note: tool / resource / prompt counts, auth mode, storage provider, upstream APIs, which tools have `destructiveHint`, which handlers use `ctx.elicit`, which services hold module-scope state, whether the server reads `roots`. **If transport is streamable HTTP or SSE**, also capture: - Bind address (`127.0.0.1` for local, or `0.0.0.0` / public interface?) - Origin allowlist (DNS rebinding mitigation) — configured, or wildcard / missing? - Session ID source (framework CSPRNG, or builder-supplied?) and binding to auth identity - Any unauthenticated routes (`/healthz`, `/sse`, metadata endpoints) — do they leak tool lists or tenant hints? - MCP Authorization spec: if implemented, PKCE enforced, token audience (`aud`) checked, resource indicators used **If `CANVAS_PROVIDER_TYPE=duckdb` is set**, also capture: - Auth mode — canvas in `MCP_AUTH_MODE=none` collapses the composite `(tenantId, canvasId)` scope to `('default', canvasId)`, where the ID is the only differentiator - `CANVAS_MAX_CANVASES_PER_TENANT`, `CANVAS_TTL_MS`, `CANVAS_ABSOLUTE_CAP_MS`, `CANVAS_EXPORT_PATH` values - Whether external rate limiting (CDN, reverse proxy, WAF) fronts the deployment — required to keep the ~10¹⁸ canvasId keyspace operationally infeasible to brute-force Use `TaskCreate` — one task per axis. Mark complete as you go. **Run `fuzzTool` in parallel.** `@cyanheads/mcp-ts-core/testing/fuzz` catches crashes, memory leaks, and prototype pollution automatically on each tool — start it now so results are ready when you reach Axis 5. ### 2. Walk the eight axes #### Axis 1 — LLM-facing surfaces as injection vector Anything the server sends to the client that reaches the LLM's context is a potential injection surface: tool output, resource content, prompt text, and the metadata the LLM reads to decide what to call. Relayed upstream content (tickets, scraped text, emails, DB rows) can carry adversarial instructions even when your code is honest. **Look in:** - Every `*.tool.ts` — `output` schema + `format()` - Every `*.resource.ts` — content returned from `resources/read` - Every `*.prompt.ts` — templated message content - Every definition file — `description`, `title`, `annotations`, and `inputSchema` field descriptions (templated from untrusted data?) **Check:** - Handlers that return raw upstream text / DB rows without structural framing? - Does `format()` wrap untrusted content in delimiters (blockquote, fenced code, `<data>` tags)? - Output schema distinguishes "data" fields from free-form text? - Resource content (`resources/read`) framed the same way tool output is? - Prompt templates interpolate untrusted data without escaping — treating tenant-controlled strings as trusted instructions? - Tool / resource / prompt **descriptions** templated from runtime data? Static strings are safer; templated descriptions enable "tool poisoning" (adversarial metadata steering the LLM toward a dangerous tool). - Descriptions mutated mid-session? Rug-pull surface: client approved the v1 description, server now advertises v2 behavior. **Smell:** `return { body: await fetch(url).then(r => r.text()) }` rendered directly in `format()`. Or: `description: \`Look up ${tenant.customLabel}\`` where `customLabel` is tenant-supplied. #### Axis 2 — Scope granularity Every `auth: [...]` entry is a blast-radius dial. **Look in:** every `*.tool.ts` — `auth:` array. ```bash grep -rn "auth: \[" src/mcp-server/tools/definitions/ ``` **Check:** - Tools with `['admin']`, `['*']`, or `[]`? - A single scope covering two capabilities that should be separated (read vs write)? - Read-only tools never require write scopes? - `MCP_AUTH_DISABLE_SCOPE_CHECKS=true` set in production? When on, both `withRequiredScopes` and `checkScopes` early-return — every authenticated user gets every tool, and runtime tenant patterns like `team:${input.teamId}:write` no longer guard. Acceptable only when paired with a real server-side ACL (path filter, allowlist, upstream API enforcement). **Smell:** every tool shares the same scope string. Or: `MCP_AUTH_DISABLE_SCOPE_CHECKS=true` set without a documented compensating ACL — confirm the deployment relies on a meaningful access control layer below the framework before approving. #### Axis 3 — Destructive ops without elicit `ctx.elicit` moves consent off the LLM and onto the user. Destructive tools without it trust the LLM not to be tricked. **Look in:** handlers with `destructiveHint: true` or side-effecting verbs in names (`delete_*`, `send_*`, `pay_*`, `publish_*`, `drop_*`). ```bash grep -rn "destructiveHint" src/mcp-server/tools/definitions/ grep -rn "ctx.elicit" src/mcp-server/tools/definitions/ ``` **Check:** - Each destructive handler calls `ctx.elicit` before the side effect? - Fallback when elicit is unavailable — **proceeds on the tool annotations, and says so in the tool description?** Do NOT require a refusal here: `ctx.elicit` is `undefined` on *every* Streamable HTTP request (the transport builds a fresh `McpServer` per request, so the SDK never records the client's capabilities — mcp-ts-core#312), so a refusing fallback makes the gated tools permanently unusable on the hosted transport. That is an outage, not a hardening. What IS in scope: a **declined or unparseable** elicit response must never proceed, and any doc claiming the gate protects "clients that support elicitation" must name the HTTP limitation or it overstates the control. - Elicit **response** validated against a Zod schema before use? The returned payload is LLM-mediated, not user-direct — "user confirmed" does not mean "user authored these exact fields." - Consent is scoped to the specific target (e.g., record ID rendered in the prompt), not a generic "proceed?" **Smell:** `destructiveHint: true` file with no `ctx.elicit?.(...)` in it. Or: `const { confirmed } = await ctx.elicit(...)` without a schema — `confirmed` could be anything. #### Axis 4 — Upstream auth shape What credentials the server holds, and the blast radius if one leaks. **Look in:** `src/services/*`, `src/config/server-config.ts`. **Check:** - Each upstream API key scoped to minimum required? (No admin keys for read workflows.) - Services re-mint downstream tokens with correct `aud`, or passthrough the caller's? - Server holds OAuth for N services × M tenants — what does one-tenant compromise expose? - Per-tenant rate limits on upstream calls? **Smell:** one global `API_KEY` used across all tenants + retry loop with no upper bound. #### Axis 5 — Input sinks LLM-supplied inputs feel internal but aren't. Classic sinks apply, amplified. Sampling responses and roots-derived paths are MCP-specific sinks that look internal but carry LLM/client trust. **Look in:** all handlers. ```bash # URL sinks — SSRF grep -rn "z.string().url()" src/ # Path sinks — traversal grep -rn "readFile\|writeFile\|readdirSync\|createReadStream\|statSync" src/ # Shell sinks — command injection grep -rnE "\b(exec|spawn|execSync|spawnSync)\b" src/ # Merges — prototype pollution grep -rn "Object.assign\b\|structuredClone" src/ # Roots — client-shared filesystem grep -rn "roots/list\|ctx.roots" src/ # Schema laxity — fields sneaking past validation grep -rn "\.passthrough()\|\.catchall(" src/mcp-server/ ``` **Check:** - URL-taking tools block private IPs, `file://`, `ftp://`, `localhost`, DNS rebind? - Path-taking tools canonicalize (`path.resolve` + assert `startsWith(root + sep)`)? - Roots-derived paths: resolved result stays within *one* declared root (iterate and assert), not assumed-safe because "the client said so"? - Shell-using tools use an allowlist (never string-concat)? - Regex / glob / filter inputs bounded (length cap, complexity limits, execution timeout) — ReDoS-safe? - User-JSON merges reject `__proto__`, `constructor`, `prototype` keys? - **Input schemas `.strict()`** — unknown fields rejected, not silently passed to downstream code that destructures with `...rest`? - **Output schemas without `.passthrough()` / `.catchall()`** — no accidental exfiltration of fields your schema didn't declare? **Smell:** `z.string().url()` with no allowlist; `readFile(input.path)` with no canonicalization. #### Axis 6 — Tenant isolation `ctx.state` is tenant-scoped. Module-scope state is not. **Look in:** `src/services/*`. ```bash grep -rnE "^(const|let) .* = new (Map|Set|WeakMap|Array)" src/services/ grep -rn "^let " src/services/ ``` **Check:** - Module-scope `Map` / `Set` / cache near tenant-handling code? - Upstream connections pooled per-tenant or shared? - Any code path uses the global `logger` while carrying per-tenant data (bypassing auto-correlated `ctx.log`)? - Could tenant B, served after tenant A, read tenant A's cached data? **Smell:** service file with top-level `const cache = new Map()`. #### Axis 7 — Leakage back What accidentally reaches the LLM, user, or observability sinks. **Look in:** `throw new McpError(...)` and `ctx.fail(reason, msg, data)` sites, error factory calls (`notFound`, `httpErrorFromResponse`, …), `McpError.data` fields (the `data` arg flows through both paths), output schemas, and every logging / telemetry surface — not just `ctx.log`. ```bash grep -rnE "new McpError|ctx\.fail\(|httpErrorFromResponse\(" src/ grep -rnE "\b(ctx\.log|console\.(log|info|warn|error|debug)|logger\.)" src/ grep -rnE "(Sentry\.|captureException|setTag|setContext|addBreadcrumb)" src/ grep -rnE "(setAttribute|setAttributes|span\.)" src/ # OpenTelemetry ``` **Check:** - Error `data` fields (whether passed via `ctx.fail(reason, msg, data)`, `new McpError(code, msg, data)`, or factory calls) carry upstream response bodies, auth headers, stack traces? - `httpErrorFromResponse` body capture sweeping in too much (default 500-byte cap is fine for most APIs but consider `captureBody: false` when the upstream returns auth-bearing payloads)? - Output schemas include token prefixes, internal IDs, session identifiers? - `format()` renders fields that shouldn't leave the server? - `ctx.log.info(msg, body)` where `body` is the raw request (may contain secrets)? - `console.*` calls near auth / token / request-body handling — bypasses structured redaction? - OpenTelemetry span attributes / Sentry breadcrumbs carry tokens, PII, or full request bodies? - Secret / token / HMAC comparisons use `===` or `==` instead of constant-time (`timingSafeEqual` / `crypto.timingSafeEqual`) — leaks length and prefix via timing? **Smell:** `throw new McpError(code, upstream.message, { raw: upstream.body })` or `throw ctx.fail('upstream_failed', e.message, { raw: e.response.body })`. Or: `if (apiKey === expected)` on a request-auth path. #### Axis 8 — Resource bounds Unbounded = DoS of self, upstream, or the LLM's context window (billing-DoS is real). **Look in:** handlers with loops, pagination, retries, or inputs that feed `JSON.parse` / schema validation. ```bash grep -rnE "while\s*\(|for\s*\(.*of" src/mcp-server/tools/definitions/ grep -rn "cursor\|nextPage\|paginate" src/ grep -rn "JSON.parse\b" src/ ``` **Check:** - Pagination loops have a total-items cap? - Retry logic has max attempts + exponential backoff? - Output size proportional to input — is there a ceiling? - Tools callable in a loop fail-fast on degenerate input (empty string, `0`, `null`)? - `JSON.parse` / Zod `.parse()` inputs have a size + nesting-depth limit applied before parse? - **Per-tenant per-tool** call rate limit (a single tenant looping `delete_record` 10k/sec hits you before it hits upstream)? - Concurrency cap on long-running tools so one tenant can't starve the event loop? **Smell:** `while (cursor) { results.push(...); cursor = next; }` with no max count. Or: `JSON.parse(await req.text())` with no `Content-Length` check upstream. #### Axis 9 — Canvas (only if `CANVAS_PROVIDER_TYPE=duckdb`) DataCanvas is opt-in and deliberately trades isolation for cross-agent token-shareable working sets — designed for public-data tabular servers (BrAPI, OpenAlex, etc.) where session-pinning isn't desired. The trade only holds when the deployment matches that assumption. Skip this axis entirely when canvas is disabled (`CANVAS_PROVIDER_TYPE=none`, the default). **Look in:** `src/config/server-config.ts`, every tool reading `ctx.core.canvas?`, deployment config (wrangler / Dockerfile / proxy). **Check:** - Data registered on canvases is **already public** or already-shared-out-of-band. The composite `(tenantId, canvasId)` scope collapses to `('default', canvasId)` in `MCP_AUTH_MODE=none` — anyone with the `canvasId` attaches. - External rate limiting (CDN, reverse proxy, WAF) fronts the deployment so the ~10¹⁸ keyspace can't be brute-forced. Without it, the entropy assumption breaks and discovery becomes feasible. - `CANVAS_MAX_CANVASES_PER_TENANT` sized for the memory budget — default 100 is the floor; raising it lets a single tenant exhaust memory faster. - `CANVAS_TTL_MS` / `CANVAS_ABSOLUTE_CAP_MS` not absurdly long. Defaults (24 h sliding / 7 d absolute) are reasonable; longer widens the window an unreferenced `canvasId` stays guessable. - `CANVAS_EXPORT_PATH` doesn't point into a shared mount, the repo, or a directory another service serves from. The path-sandbox blocks `..` traversal but doesn't prevent the configured root from being a bad choice. - Axis 1 (description templating from canvas-supplied content), Axis 5 (no parallel service runs raw SQL outside the canvas API and bypasses `assertReadOnlyQuery`), and Axis 7 (errors from canvas operations don't leak the failed SQL string back through `McpError.data`) all apply.