AI coding tools are being shipped fast. In too many cases, basic security is not keeping up.

In our latest research, we found the same sandbox trust-boundary failure pattern across tools from Anthropic, Google, and OpenAI. Anthropic fixed and engaged quickly (CVE-2026-25725). Google did not ship a fix by disclosure. OpenAI closed the report as informational and did not address the core architectural issue.

That gap in response says a lot about vendor security posture.

We benchmarked Opus 4.6's ability to find simple C vulns and found that the model flags about 1 in 4 flaws -- with a very high false positive rate and lots of inconsistency from run to run. Techniques like judge agents and requiring the model to justify its results improve the results to some extent, but they're still not great.

I’ve been experimenting with Chrome DevTools Protocol primitives to build tools for reversing and debugging JavaScript at runtime.

The idea is to interact with execution by hooking functions without monkeypatching or modifying application code.

Conceptually, this is closer to a Frida-style instrumentation model (onEnter/onLeave handlers), but applied to the browser via CDP.

Early experiments include:

• attaching hooks to functions at runtime
• inspecting and modifying arguments and local variables
• overriding return values (unfortunately limited to sync functions due to CDP constraints)
• following return values to their consumer (best-effort / heuristic)
• conditional stepping (stepIn / stepOut / stepOver)

All implemented via CDP (debugger breakpoints + runtime evaluation), so this also works inside closures and non-exported code.

I’d really appreciate feedback — especially from people doing reverse engineering, bug bounty, or complex frontend debugging.