Attack Planning in the Real World
Not on the map. Why?
Abstract (arXiv)
Assessing network security is a complex and difficult task. Attack graphs have been proposed as a tool to help network administrators understand the potential weaknesses of their network. However, a problem has not yet been addressed by previous work on this subject; namely, how to actually execute and validate the attack paths resulting from the analysis of the attack graph. In this paper we present a complete PDDL representation of an attack model, and an implementation that integrates a planner into a penetration testing tool. This allows to automatically generate attack paths for penetration testing scenarios, and to validate these attacks by executing the corresponding actions -including exploits- against the real target network. We present an algorithm for transforming the information present in the penetration testing tool to the planning domain, and show how the scalability issues of attack graphs can be solved using current planners. We include an analysis of the performance of our solution, showing how our model scales to medium-sized networks and the number of actions available in current penetration testing tools.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Jun 2013.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 2; 2025: 2; 2026: 1.
7 papers.
- 2024PentestAgent: Incorporating LLM Agents to Automated Penetration Testingcited by 46
- 2025Automated Penetration Testing with LLM Agents and Classical Planningcited by 4
- 2025A Unified Modeling Framework for Automated Penetration Testingcited by 1
- 2021Improving Search by Utilizing State Information in OPTIC Planners Compilation to LPcited by 0
- 2022Planning with Dynamically Estimated Action Costscited by 0
- 2024CloudLens: Modeling and Detecting Cloud Security Vulnerabilitiescited by 0
- 2026Symbolic Attack Chain Generation from Atomic Red Team Techniques: An Empirical Study of Predicate Representation Granularitycited by 0
- 2026Symbolic Attack Chain Generation from Atomic Red Team Techniques: An Empirical Study of Predicate Representation Granularity31 Jul 2026
- 2025Automated Penetration Testing with LLM Agents and Classical Planning11 Dec 2025
- 2025A Unified Modeling Framework for Automated Penetration Testing17 Feb 2025
- 2024PentestAgent: Incorporating LLM Agents to Automated Penetration Testing7 Nov 2024
- 2024CloudLens: Modeling and Detecting Cloud Security Vulnerabilities16 Feb 2024
- 2022Planning with Dynamically Estimated Action Costs8 Jun 2022
- 2021Improving Search by Utilizing State Information in OPTIC Planners Compilation to LP15 Jun 2021