The Agent Is Not the Scanner: Making AI Security Agents Better

This article presents an empirical study on building effective AI-assisted security workflows, comparing the performance of 11 different language models across three configurations: a control (no tools), skills-only (structured guidance), and MCP-enabled (with external tools). The key finding is that scaffolding benefits are not uniform—weaker models (below 0.60 F1) see significant improvements from skills, while stronger models (above 0.75 F1) actually regress due to overhead. The author also discovered that MCP tools hurt performance on static code snippet benchmarks because there is nothing to run, but are valuable on live targets. Based on these results, the article provides practical recommendations, including routing models based on their strength, separating recon, exploit reasoning, and reporting into different stages with different models, and using deterministic scanners for known vulnerabilities to save costs. 

https://shad0wmazt3r.github.io/ai-security

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