Beyond Success Rate: Cost-Aware Evaluation of Offensive and Defensive Security Agents
New research proposes a cost-aware evaluation framework for AI security agents, moving beyond simple success rates to include inference and tool usage costs.
- Standard security benchmarks often ignore the inference costs associated with complex reasoning and tool calls.
- The study evaluates agents using both offensive (Cybench) and defensive (Splunk BOTS) benchmarks.
- A new metric compares model effectiveness at fixed cost levels to reflect real-world operational constraints.
Current evaluations for AI security agents often focus exclusively on peak performance, such as finding vulnerabilities or completing Capture The Flag (CTF) challenges. While these metrics show maximum capability, they ignore the practical reality of operational security, where every reasoning step and tool call incurs a financial and computational cost.
This study introduces a cost-success lens to evaluate models across both offensive and defensive scenarios. By testing against Cybench offensive challenges and Splunk BOTS defensive investigation tasks, the researchers compare model performance at fixed budget constraints rather than just reporting best-case scenarios.
This shift in methodology provides a more realistic benchmark for deploying autonomous agents in production environments. It highlights the trade-offs between high-reasoning, high-cost models and more efficient, specialized agents.
Provides a more realistic metric for optimizing agentic workflows and tool-use efficiency.
Helps in calculating the actual ROI and operational expense of deploying autonomous security agents.
Introduces a critical dimension (cost) to the evaluation of autonomous AI agents.
- CTF
- Capture The Flag, a type of cybersecurity competition designed to test technical skills.
- Inference budget
- The limit on computational resources or financial cost allocated to a single AI task.
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