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To thwart reverse engineering by agents, dev proposes blinding them

Setting aside the question of whether enlisting an AI model to reimplement an app really immunizes the creator from legal risk, those making apps face the possibility that someone will have an LLM probe published app binaries and reconstruct them through reverse engineering.

✦ Extractive summary — built from the sources' own reporting•From 1 source•1 min read•Updated November 10, 2026, 8:35 PM UTC
Key points
  • "Coding agents can reverse-engineer shipped packages, CLIs, and WASM in minutes," Zhang told The Register in an email.
  • It's for hardening shipping code against reverse engineering and LLM-aided analysis.
  • "We also drive REA (a reverse-engineering agent) against those builds," he said.

We raise the cost — we don't claim it is unbreakable." The project website says as much, noting that it raises the effort for analysis and adds decoy code, but doesn't promise protection against traditional interactive reverse engineering tools like Ghidra or Frida.

Additional details
  • Timo Zhang's project is called Blind LLM Agents (BLA), and as its name suggests, it attempts to interfere with efforts by AI agents to scrutinize app code.
  • Blind LLM Agents is a protection layer for that threat model." Such agents can also help developers build alternatives to existing commercial software.
  • Last month, Artcraft, a two-person development shop, released seven open-source apps that aim to be replacements for Adobe's commercial apps Photoshop, Illustrator, Premiere Pro, Lightroom, After Effects, InDesign, and Acrobat Pro.
  • "A transform pipeline (plus MCP tools / an agent Skill) applies renaming, lane splitting, seed-derived string encryption, build polymorphism, WASM export renaming, decoys, self-seal integrity checks, and a coverage matrix that won't claim protection it can't prove.
  • Behavior stays equivalent; recovery cost goes up." Zhang said various evaluations like red teaming and CrackMe challenges to recover keys or flags from hardened artifacts show that BLA is effective.

How this summary was made: extractive summary — built from the sources' own reporting. It is drawn only from the reporting of the sources listed below — read the originals for full detail. How we summarize →

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The RegisterNovember 10, 2026, 8:35 PM UTC →

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