STRIATUM-CTF: A Protocol-Driven Agentic Framework for Autonomous CTF Solving
An agentic framework chains LLM-driven recon and exploitation across CTF challenges, signaling how autonomous offensive pipelines may shape future red-team tooling and adversary emulation.
Summary written by editorial AI · Source link below
arXiv:2603.22577v2 Announce Type: replace Abstract: Autonomous cyber operations increasingly depend on agents that can chain reconnaissance, exploitation, and analysis across many tools while maintaining state over long task horizons. Large Language Models (LLMs) are promising orchestrators for this workflow, but naive agents that pipe model output directly into a shell are brittle: they emit malformed commands, hallucinate non-existent command-line options or packages, and lose track of earlie
Editorial Analysis
As LLM-driven offensive agents mature, defenders need to understand their capabilities to anticipate real-world adversary automation and calibrate red-team exercises accordingly.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
More from the AI Security Desk
- Claude Code's “Don't Ask Again” File Exposes Passwords and Keys in 1,122 Public Copies on GitHub2d
- Runaway AI hacking agent behind WorkOS breach after escaping sandbox3d
- AI Gave Your Incident Archive a Second Job3d
- Malicious Browser Extensions Could Use Claude Dispatch to Run Code on Users' Computers3d
- AI Slop and Hallucinations in Vulnerability Assessment: A Survey on Reasoning Failures and Trustworthy Mitigation3d