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authorTianren Dong <tianrendong.dtr@gmail.com>2026-05-31 14:19:04 -0400
committerTianren Dong <tianrendong.dtr@gmail.com>2026-05-31 14:19:04 -0400
commitd21ebf7b96c90943ab48ffcd6d5b02c6f7438c04 (patch)
tree00a005deb29fd4071c0d67be8e533bab5972d850 /docs/COMPARISON.md
parent0fc5b8950601a36dfffdc8f7163bb12fb94f353e (diff)
0.15.29: CSP bypass via CDP Runtime.evaluate for evaluate/snapshot/waitFor/initScript
- evaluateInTab/executeInTab now run through CDP Runtime.evaluate instead of new Function() in MAIN world, so chrome_evaluate/chrome_snapshot work on pages that block unsafe-eval (github.com, many bank/SaaS apps). Preserves value markers (undefined/function/symbol/bigint/Error), DOMRect expansion, and the expression/statement fallback; keeps {ok,value} error envelope. - page.waitFor polls via service-worker-side CDP evaluation. - chrome_navigate initScript uses Page.addScriptToEvaluateOnNewDocument. - Add npm test (test-suite/unit/csp-eval.test.mjs) and in-browser regression challenge 42 strict-csp-evaluate; update challenge 39 notes + docs/primer.
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## Public benchmarks worth knowing (for axis 2 / axis 3 comparison)
-Pi-chrome itself ships a benchmark suite ([`../test-suite/`](../test-suite)) of **41 primitive challenges** plus **4 hermetic BrowserGym-style long-horizon tasks** covering trusted input, pointer humanization, keyboard fidelity, drag/drop, Shadow DOM, iframes, file uploads, strict-CSP screenshot fallback, dynamic waits, tab lifecycle, network observability, fingerprint leaks, and agent-safety honeypots. Scoring tracks expected outcomes per challenge instead of raw PASS count, with `core`, `conditional`, and `quality` gate buckets. That's **driver-level** grading.
+Pi-chrome itself ships a benchmark suite ([`../test-suite/`](../test-suite)) of **42 primitive challenges** plus **4 hermetic BrowserGym-style long-horizon tasks** covering trusted input, pointer humanization, keyboard fidelity, drag/drop, Shadow DOM, iframes, file uploads, strict-CSP screenshot fallback and CDP eval/snapshot bypass, dynamic waits, tab lifecycle, network observability, fingerprint leaks, and agent-safety honeypots. Scoring tracks expected outcomes per challenge instead of raw PASS count, with `core`, `conditional`, and `quality` gate buckets. That's **driver-level** grading.
For **agent-level** comparison (axis 2), the public benchmarks worth citing: