Beyond Success Rate: Cost-Aware Evaluation of Offensive and Defensive Security Agents

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Paul Kassianik, Blaine Nelson, Yaron Singer
• Venue: arXiv: cs.CR
• Published: 2026-07-16
• Collected: 2026-07-17
• URL: http://arxiv.org/abs/2607.15263v1
• PDF: https://arxiv.org/pdf/2607.15263v1
• DOI: UNKNOWN

Plain-Language Brief

The researchers evaluated language model security agents based on cost efficiency in addition to their success rates, focusing on offensive CTF challenges and defensive Splunk BOTS v1 investigation challenges. They found that offensive CTF performance improves with additional compute resources, while defensive SOC investigation does not scale the same way and relies more on disciplined tool use, telemetry navigation, and selective enrichment. The authors argue that security-agent benchmarks should also measure economic efficiency and operational fit.

What the Document Says

• Security-agent evaluations commonly measure peak offensive capability under generous inference budgets (vulnerability discovery, exploit development, penetration testing, CTF completion).
• The study evaluates language-model security agents through a cost-success lens on offensive Cybench challenges and defensive Splunk BOTS v1 investigation challenges.
• Instead of reporting only best-case success, the evaluation compares models at fixed cost levels and decomposes performance by inference spend and tool spend.
• Offensive CTF performance improves with additional test-time compute, and scaled open-weight models can approach frontier proprietary systems while remaining cost-competitive.
• Defensive SOC investigation does not scale the same way; success depends more heavily on disciplined tool use, telemetry navigation, and selective enrichment than on raw reasoning budget alone.
• The authors argue that security-agent benchmarks should measure economic efficiency and operational fit alongside task success.
• Results are presented in an interactive website: https://evals.frontier.security.

Key Concepts Mentioned

• Security agents
• Offensive CTF challenges
• Defensive SOC investigation
• Inference spend
• Tool spend
• Cost-success lens
• Economic efficiency
• Operational fit

Claims Made by the Document

• Offensive CTF performance improves with additional test-time compute.
• Scaled open-weight models can approach frontier proprietary systems while remaining cost-competitive in offensive CTF challenges.

Evidence Used

• The results show distinct scaling regimes for red- and blue-team tasks, with offensive CTF performance improving with additional compute resources.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Security-agent evaluations commonly measure peak offensive capability under generous inference budgets, emphasizing vulnerability discovery, exploit development, penetration testing, and CTF completion. Such measurements are useful but incomplete: in operational security, every reasoning step, tool call, telemetry query, and enrichment request consumes budget. We evaluate language-model security agents through this cost-success lens on offensive Cybench challenges and defensive Splunk BOTS v1 investigation challenges. Instead of reporting only best-case success, we compare models at fixed cost levels and decompose performance by inference spend and tool spend. Our results show distinct scalingregimes for red- and blue-team tasks. Offensive CTF performance improves with additional test-time compute, and scaled open-weight models can approach frontier proprietary systems while remaining cost-competitive. Defensive SOC investigation does not scale in the same way: success depends more heavily on disciplined tool use, telemetry navigation, and selective enrichment than on raw reasoning budget alone. We argue that security-agent benchmarks should measure economic efficiency and operational fit alongside task success. Cost-aware, SOC-native evaluations provide a clearer picture of which models are practically useful today and where defensive agents still need to improve. We present an interactive website with our results https://evals.frontier.security.