# To thwart reverse engineering by agents, dev proposes blinding them > Source: > Published: 2026-11-10 20:35:11+00:00 # To thwart reverse engineering by agents, dev proposes blinding them [The Register](https://www.theregister.com) A poke in the AI with a sharp stick Now that LLMs can help analyze commercial software binaries and recreate their functionality, one software developer and student has created a project designed to make this process more difficult. Timo Zhang's [project](https://github.com/qybaihe/bla) is called [Blind LLM Agents](https://bla.tools/) (BLA), and as its name suggests, it attempts to interfere with efforts by AI agents to scrutinize app code. "Coding agents can reverse-engineer shipped packages, CLIs, and WASM in minutes," Zhang told The Register in an email. "Classic obfuscation was built for humans and static tools, not LLMs that reason about meaning. Blind [LLM](https://www.machinebrief.com/glossary/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](https://getartcraft.com/), 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. Artcraft claims these apps are "clean-room reimplementations" written from scratch in Rust using [Anthropic](https://www.machinebrief.com/glossary/anthropic) Opus 5.5. Setting aside [the question](https://www.plagiarismtoday.com/2026/10/14/copyright-and-the-vibe-coded-adobe-clones/) 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. There's in fact a framework for this, [Reverse Engineer Anything](https://rea.tools) (REA). Zhang has faced some [criticism](https://x.com/TeosVerdi/status/2119355649977143530) on social media for creating a limited barrier to copying other people's work (which at this point might simply be the work of another LLM). But not everyone accepts that all publicly accessible binaries should be easily available for review and reuse. BLA cannot prevent committed reverse engineering efforts, but it may add a bit of friction to the process when LLMs are involved. "It doesn't write app code — it hardens what you've already built," Zhang said. "A transform pipeline (plus [MCP](https://www.machinebrief.com/glossary/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](https://www.machinebrief.com/glossary/red-teaming) and CrackMe challenges to recover keys or flags from hardened artifacts show that BLA is effective. "We also drive REA (a reverse-engineering agent) against those builds," he said. "Fail-to-break is the pass. CI tracks defense score, self-verifies integrity coverage, and checks functional equivalence. 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. Zhang said he's aware of other software projects that aim to interfere with AI, such as [Poison Fountain](https://www.theregister.com/software/2026/01/11/ai-insiders-seek-to-poison-the-data-that-feeds-them/4349990) for poisoning [training](https://www.machinebrief.com/glossary/training) data and Anubis for deterring bots and scraping. BLA, he said, is different. It's for hardening shipping code against reverse engineering and LLM-aided analysis. ® Get AI news in your inbox Daily digest of what matters in AI. ## Key Terms Explained [Anthropic](https://www.machinebrief.com/glossary/anthropic) An AI safety company founded in 2021 by former OpenAI researchers, including Dario and Daniela Amodei. [LLM](https://www.machinebrief.com/glossary/llm) Large Language Model. [MCP](https://www.machinebrief.com/glossary/mcp) Model Context Protocol (MCP) is an open standard created by Anthropic that lets AI models connect to external tools, data sources, and APIs through a unified interface. [Red Teaming](https://www.machinebrief.com/glossary/red-teaming) Systematically testing an AI system by trying to make it produce harmful, biased, or incorrect outputs.