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

Researchers introduce Evolve-CTF, a tool that generates families of semantically-equivalent cybersecurity challenges to evaluate the robustness of agentic LLMs. Testing 13 LLM configurations reveals models are resilient to basic code transformations but struggle with obfuscation and composed modifications, providing new benchmarking methodology for AI safety evaluation.

The research addresses a critical gap in AI evaluation methodology by moving beyond single-point benchmark assessments toward family-based testing that measures model robustness across semantic variations. Traditional capture-the-flag benchmarks provide limited insight into whether agentic LLMs truly understand exploit strategies or merely memorize solutions, making this work significant for understanding genuine AI capabilities in adversarial scenarios.

The findings emerge from growing recognition that point-based evaluation fails to capture model generalization—a fundamental concern as LLMs increasingly handle security-sensitive tasks. By generating multiple versions of identical challenges through semantics-preserving transformations (renaming variables, code insertion, obfuscation), researchers isolate what specifically degrades agent performance. The distinction between simple transformations (which models handle well) and composed transformations (which cause significant performance drops) suggests current LLMs rely on pattern matching rather than deep reasoning about exploit mechanics.

For the AI development community, this work establishes a replicable evaluation framework that developers can use to stress-test agentic systems before deployment in real-world security contexts. The finding that explicit reasoning enables provide minimal improvement challenges assumptions about chain-of-thought prompting's universal effectiveness. This has direct implications for how companies architect security-focused AI agents and allocate computational resources.

Looking forward, the Evolve-CTF tool and dataset enable ongoing comparative analysis of newer models and transformation techniques. Researchers will likely extend this methodology to other domains—code generation, vulnerability detection, and compliance checking—where robustness across semantic variations directly impacts trust and reliability.

• →Evolve-CTF generates semantically-equivalent CTF variants to measure LLM robustness beyond single-point benchmarks.
• →Models show strong resilience to renaming and code insertion but fail significantly on composed transformations and obfuscation.
• →Explicit reasoning prompts provide minimal performance improvement in cybersecurity tasks, contradicting broader chain-of-thought assumptions.
• →The tool and dataset establish reproducible evaluation methodology for future agentic LLM assessment in security domains.
• →Performance degradation under obfuscation indicates current models prioritize pattern matching over genuine exploit strategy understanding.