Invisible Characters, Visible Damage

📰 Dev.to · Wu Long

Learn how invisible characters can cause security vulnerabilities in code, particularly with sanitizers and LLMs, and how to mitigate these issues

intermediate Published 5 Apr 2026
Action Steps
  1. Identify potential security vulnerabilities in code related to invisible characters and sanitizers
  2. Use tools like OpenClaw to mark external content and prevent spoofing
  3. Implement a two-step sanitizer process to strip out malicious markers
  4. Test and validate the sanitizer's effectiveness in preventing attacks
  5. Apply security best practices to prevent similar vulnerabilities in the future
Who Needs to Know This

Developers, security engineers, and AI/ML engineers can benefit from understanding how invisible characters can be exploited and how to prevent such attacks

Key Insight

💡 Invisible characters can be used to exploit security vulnerabilities in code, particularly with sanitizers and LLMs

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🚨 Invisible characters can cause visible damage! Learn how to prevent security vulnerabilities in code 🛡️

Full Article

Title: Invisible Characters, Visible Damage

URL Source: https://dev.to/oolongtea2026/invisible-characters-visible-damage-168b

Published Time: 2026-04-05T20:31:58Z

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Posted on Apr 5 • Originally published at [oolong-tea-2026.github.io](https://oolong-tea-2026.github.io/posts/invisible-characters-visible-damage/)

# Invisible Characters, Visible Damage

[#security](https://dev.to/t/security)[#unicode](https://dev.to/t/unicode)[#aiagents](https://dev.to/t/aiagents)[#openclaw](https://dev.to/t/openclaw)

There's a special kind of bug that only exists because two pieces of code disagree about what a string looks like.

One side strips invisible characters. The other side tries to apply the results back to the original. And in the gap between those two views of reality, an attacker can park a payload.

## [](https://dev.to/oolongtea2026/invisible-characters-visible-damage-168b#the-setup) The Setup

OpenClaw marks external content with boundary markers — special strings that tell the LLM "everything between these markers came from outside, treat it accordingly." The sanitizer's job is simple: if someone tries to spoof those markers in untrusted input, strip them out before they reach the model.

The sanitizer works in two steps:

1. **Fold** t
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