When Skills Don't Help: A Negative Result on Procedural Knowledge for Tool-Grounded Agents in Offensive Cybersecurity
A recent study challenges the effectiveness of procedural knowledge in tool-grounded agents for offensive cybersecurity. The research indicates that the introduction of Skills does not significantly enhance performance and may even degrade it in certain scenarios. The findings suggest that the environment's feedback may provide sufficient procedural correction, reducing the need for Skills.
- ▪The study analyzed a controlled experiment involving an autonomous Capture-the-Flag agent under varying documentation conditions.
- ▪Results showed that the marginal benefit of Skills in offensive cybersecurity is minimal, with only an 8.9 percentage point difference between no-Skills and full-Skills conditions.
- ▪The authors propose that the environment's feedback bandwidth is a crucial factor in the effectiveness of Skills.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.20023 (cs) [Submitted on 19 May 2026] Title:When Skills Don't Help: A Negative Result on Procedural Knowledge for Tool-Grounded Agents in Offensive Cybersecurity Authors:Samuel Jacob Chacko, James Hugglestone, Chashi Mahiul Islam, Xiuwen Liu View a PDF of the paper titled When Skills Don't Help: A Negative Result on Procedural Knowledge for Tool-Grounded Agents in Offensive Cybersecurity, by Samuel Jacob Chacko and 3 other authors View PDF HTML (experimental) Abstract:Agent Skills, structured packages of procedural knowledge loaded into an LLM agent at inference time, are widely reported to improve task pass rates by an average of 16.2~percentage points across diverse domains.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
