Towards Optimal Agentic Architectures for Offensive Security Tasks
📰 ArXiv cs.AI
Learn how to design optimal agentic architectures for offensive security tasks using LLMs and empirical systems evaluation
Action Steps
- Evaluate the effectiveness of different coordination topologies for agentic security systems using a controlled benchmark
- Design and test agentic architectures with varying numbers of agents to determine the optimal configuration
- Use LLMs to audit live targets and identify vulnerabilities in web/API and binary targets
- Compare the performance of different agentic architectures in whitebox and blackbox evaluation settings
- Apply empirical systems evaluation to determine when additional agents improve system performance and when they only add cost
Who Needs to Know This
Security researchers and engineers can benefit from this knowledge to improve the efficiency of their auditing tools and systems
Key Insight
💡 Empirical systems evaluation can help determine the optimal coordination topology for agentic security systems
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🔍 Improve offensive security with optimal agentic architectures using LLMs! 🚀
Key Takeaways
Learn how to design optimal agentic architectures for offensive security tasks using LLMs and empirical systems evaluation
Full Article
Title: Towards Optimal Agentic Architectures for Offensive Security Tasks
Abstract:
arXiv:2604.18718v1 Announce Type: cross Abstract: Agentic security systems increasingly audit live targets with tool-using LLMs, but prior systems fix a single coordination topology, leaving unclear when additional agents help and when they only add cost. We treat topology choice as an empirical systems question. We introduce a controlled benchmark of 20 interactive targets (10 web/API and 10 binary), each exposing one endpoint-reachable ground-truth vulnerability, evaluated in whitebox and blac
Abstract:
arXiv:2604.18718v1 Announce Type: cross Abstract: Agentic security systems increasingly audit live targets with tool-using LLMs, but prior systems fix a single coordination topology, leaving unclear when additional agents help and when they only add cost. We treat topology choice as an empirical systems question. We introduce a controlled benchmark of 20 interactive targets (10 web/API and 10 binary), each exposing one endpoint-reachable ground-truth vulnerability, evaluated in whitebox and blac
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