Do Machines Struggle Where Humans Do? LLM and Human Comprehension of Obfuscated Code

2026-07-01 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, quick

Summary

A recent study investigated whether large language models (LLMs) exhibit similar comprehension failures to humans when faced with obfuscated code. Researchers evaluated several LLMs across five obfuscation tiers using the Block Model, localizing comprehension failures at atom, block, relational, and macro levels. The findings indicate that reasoning-tuned models demonstrate significant alignment with human difficulty patterns across experience levels, while instruction and coder-tuned models show near-zero correlation. Performance under control-flow flattening degrades proportionally to state-space complexity. Additionally, adversarial identifier renaming disrupts comprehension through the interaction of semantic displacement and identifier-level interference. These results suggest reasoning-tuned LLMs more effectively approximate human sensitivity to code complexity.

Key takeaway

For AI Scientists evaluating LLMs for tasks involving code analysis or security, you should prioritize reasoning-tuned models. These models demonstrate a stronger alignment with human comprehension challenges under obfuscation, particularly with control-flow flattening and adversarial identifier renaming. Understanding these specific failure modes allows you to select more robust LLM architectures for handling complex or intentionally obscured codebases, improving the reliability of automated code understanding tools.

Key insights

Reasoning-tuned LLMs approximate human code comprehension difficulties under obfuscation more effectively than other LLM types.

Principles

• Code obfuscation impairs human comprehension.
• Reasoning-tuned LLMs mimic human difficulty patterns.
• Control-flow flattening degrades performance proportionally to state-space complexity.

Method

Evaluated several LLMs across five obfuscation tiers using the Block Model, localizing comprehension failures at atom, block, relational, and macro levels, building on a human study.

In practice

• Prioritize reasoning-tuned LLMs for code analysis.
• Be aware of control-flow flattening impact.
• Consider identifier renaming's disruptive effect.

Topics

• Code Obfuscation
• Large Language Models
• Program Comprehension
• Software Engineering
• Reasoning-tuned LLMs
• Control-flow Flattening

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.