Security & Threat Intelligence · 25.07.2026, 15:29 UTC
Prompt Engineering for Security Agents: A Measurable Approach with GEPA
| Schweregrad | info |
|---|---|
| Kategorie | Security & Threat Intelligence |
| Quelle | SpecterOps ↗ |
| Veröffentlicht | 25.07.2026 UTC |
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You may have read about our new GhostWorks initiative here at SpecterOps. As part of this effort, we continually trial different methods of evaluating and improving model performance to help understand what techniques can be applied to our research. This post follows one such attempt to find methods which can have a measurable improvement to our LLM prompt engineering for security agents.
Learn more about the SpecterOps GhostWorks initiative for AI Security
One of the things that has been winding me up about working with LLMs is how unmeasurable prompt modifications can be.
You know how it goes, you task your agent to perform a task, and eventually it starts to deviate from the objective. So you head into your AGENTS.md to correct course, and hope that by shouting at the AI in just the right incantation, you fix the problem.
The issue however is that most of us only really have our vibes to go off when considering if the tweak actually resulted in any measurable difference.
GEPA is one established solution to this problem.
What is GEPA?
I first came across GEPA during my interactions with Dreadnode. As with most things, I like to learn from the ground up before adding a concept to my toolkit. So this post will explore how GEPA works at a high level, and how it can be applied to a working example.
GEPA (Genetic-Pareto) is an optimisation framework positioned by the authors as an alternative to reinforcement learning techniques such as GRPO. The primary purpose behind its creation was to explore prompt refinement, but it can be applied to other novel areas as …