fix(qa-lab): wrap malformed model catalog json

CHANGELOG.md

@@ -55,6 +55,7 @@ Docs: https://docs.openclaw.ai
 - Realtime transcription: report malformed provider websocket JSON frames with owned parser errors instead of leaking raw `SyntaxError` objects.
 - Microsoft Foundry: report malformed Azure CLI token JSON with owned auth errors instead of leaking raw parser failures.
 - Gateway/model pricing: report malformed external pricing catalog JSON with source-owned errors instead of leaking raw parser failures.
+- QA Lab: report malformed model-catalog subprocess JSON with an owned error and ignore invalid catalog rows.
 - Models config/auth: stop inferring provider env-var markers from broad `^[A-Z_][A-Z0-9_]*$` strings, and resolve config-backed provider `apiKey` values only through structured env SecretRefs (`secrets.providers[id]` / `secrets.defaults`), so unrelated env vars cannot accidentally become provider credentials. Thanks @sallyom.
 - Media fetch: skip allocating and buffering the response body for bodyless media responses (HEAD probes and 204-style empty bodies), avoiding wasted heap on streams that carry no payload. Thanks @shakkernerd.
 - CLI/onboarding: forward provider-specific auth flags (e.g. `--openai-api-key`) through the onboarding wizard so they reach provider auth methods via `ctx.opts`, letting `--openai-api-key "$OPENAI_API_KEY"` skip the redundant "use existing env var?" prompt in non-interactive harnesses. (#81669) Thanks @sjf.

extensions/qa-lab/src/model-catalog.runtime.test.ts

@@ -1,5 +1,8 @@
 import { describe, expect, it } from "vitest";
-import { selectQaRunnerModelOptions } from "./model-catalog.runtime.js";
+import {
+  parseQaRunnerModelOptionsOutput,
+  selectQaRunnerModelOptions,
+} from "./model-catalog.runtime.js";
 
 describe("qa runner model catalog", () => {
   it("filters to available rows and prefers gpt-5.5 first", () => {
@@ -29,4 +32,29 @@ describe("qa runner model catalog", () => {
       ]).map((entry) => entry.key),
     ).toEqual(["openai/gpt-5.5", "anthropic/claude-sonnet-4-6"]);
   });
+
+  it("reports malformed catalog JSON with an owned error", () => {
+    expect(() => parseQaRunnerModelOptionsOutput("{not json")).toThrow(
+      "qa model catalog returned malformed JSON",
+    );
+  });
+
+  it("ignores invalid catalog rows without failing the model picker", () => {
+    expect(
+      parseQaRunnerModelOptionsOutput(
+        JSON.stringify({
+          models: [
+            null,
+            {
+              key: "openai/gpt-5.5",
+              name: "gpt-5.5",
+              input: "text,image",
+              available: true,
+              missing: false,
+            },
+          ],
+        }),
+      ).map((entry) => entry.key),
+    ).toEqual(["openai/gpt-5.5"]);
+  });
 });

extensions/qa-lab/src/model-catalog.runtime.ts

@@ -68,6 +68,34 @@ export function selectQaRunnerModelOptions(rows: ModelRow[]): QaRunnerModelOptio
   });
 }
 
+function isModelRow(value: unknown): value is ModelRow {
+  if (!value || typeof value !== "object") {
+    return false;
+  }
+  const row = value as Partial<ModelRow>;
+  return (
+    typeof row.key === "string" &&
+    typeof row.name === "string" &&
+    typeof row.input === "string" &&
+    (row.available === true || row.available === false || row.available === null) &&
+    typeof row.missing === "boolean"
+  );
+}
+
+export function parseQaRunnerModelOptionsOutput(stdout: string): QaRunnerModelOption[] {
+  let payload: unknown;
+  try {
+    payload = JSON.parse(stdout) as unknown;
+  } catch {
+    throw new Error("qa model catalog returned malformed JSON");
+  }
+  if (!payload || typeof payload !== "object" || Array.isArray(payload)) {
+    throw new Error("qa model catalog returned invalid JSON payload");
+  }
+  const rows = (payload as { models?: unknown }).models;
+  return selectQaRunnerModelOptions(Array.isArray(rows) ? rows.filter(isModelRow) : []);
+}
+
 const CATALOG_ABORT_ERROR_MESSAGE = "qa model catalog aborted";
 
 function createCatalogAbortError() {
@@ -199,8 +227,7 @@ export async function loadQaRunnerModelOptions(params: { repoRoot: string; signa
       });
     });
 
-    const payload = JSON.parse(Buffer.concat(stdout).toString("utf8")) as { models?: ModelRow[] };
-    return selectQaRunnerModelOptions(payload.models ?? []);
+    return parseQaRunnerModelOptionsOutput(Buffer.concat(stdout).toString("utf8"));
   } finally {
     await fs.rm(tempRoot, { recursive: true, force: true });
   }