Capture the Flags: Family-Based Evaluation of Agentic LLMs via Semantics-Preserving Transformations

2026-01-27 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Software Development & Engineering · Depth: Expert, extended

Summary

Researchers introduced "CTF challenge families" and a new tool, Evolve-CTF, to evaluate agentic large language models (LLMs) on cybersecurity tasks. Unlike traditional pointwise benchmarks like Cybench and Intercode, CTF families generate semantically equivalent but syntactically diverse challenges from a single CTF using semantics-preserving program transformations. This approach allows for controlled evaluation of LLM robustness and generalization. The study used Evolve-CTF to create families from 16 Python challenges, evaluating 13 agentic LLM configurations with tool access. Findings indicate that LLMs are highly robust to identifier renaming and isolated code insertions but show significant performance degradation with composed transformations and deeper obfuscation methods like PyObfuscator. Explicit reasoning had minimal impact on solution success rates across these challenge families. The work provides a valuable technique, tool, and dataset for future LLM evaluations.

Key takeaway

Research scientists evaluating agentic LLMs for cybersecurity should move beyond single-instance benchmarks. You should integrate tools like Evolve-CTF to generate diverse challenge families, which better expose model robustness to code transformations and obfuscation. This will help you identify true reasoning capabilities versus pattern matching and reveal which models handle complex, multi-layered code changes more effectively, guiding more rigorous model selection and development.

Key insights

CTF challenge families and Evolve-CTF enable robust evaluation of agentic LLMs against code transformations.

Principles

• Semantics-preserving transformations reveal LLM robustness.
• Composed obfuscations significantly degrade LLM performance.
• Explicit reasoning offers minimal benefit for CTF solving.

Method

Evolve-CTF generates CTF families from Python challenges using transformations like renaming, inserting loops, conditionals, functions, comments, and PyObfuscator, then evaluates LLM configurations via the Inspect framework.

In practice

• Use Evolve-CTF to assess LLM robustness to code changes.
• Prioritize testing LLMs against composite and aggressive obfuscations.
• Re-evaluate simple CTFs for discriminative power using challenge families.

Topics

• Agentic LLM Evaluation
• Capture-the-Flag Challenges
• Semantics-Preserving Transformations
• Evolve-CTF Tool
• Code Obfuscation

Code references

• UKGovernmentBEIS/inspect_ai
• Instagram/LibCST
• lobehub/lobe-chat

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Security Engineer

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