Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation
arXiv:2509.20008v2 Announce Type: replace Abstract: Penetration testing, the simulation of cyberattacks to identify security vulnerabilities, presents a sequential decision-making problem well-suited for reinforcement learning (RL) automation. Like many applications of RL to real-world problems, partial observability presents a major challenge, as it invalidates the Markov property present in Markov Decision Processes (MDPs). Partially Observable MDPs require history aggregation or belief state estimation to learn successful policies. We investigate stochastic, partially observable penetration
The increasing sophistication of cyberattacks and the widespread adoption of AI in various sectors necessitate advanced, automated cybersecurity solutions to identify and mitigate vulnerabilities proactively.
Improving the robustness and autonomy of penetration testing through AI can fundamentally enhance cybersecurity defenses, protecting critical infrastructure and data from evolving cyber threats.
Current reactive cybersecurity measures can be augmented or potentially replaced by proactive, AI-driven systems that continuously identify and exploit vulnerabilities before malicious actors do.
- · Cybersecurity firms developing AI solutions
- · Organizations with advanced cyber defense needs
- · AI/ML researchers in security domains
- · Critical infrastructure operators
- · Threat actors relying on known vulnerabilities
- · Companies with outdated cybersecurity practices
- · Manual penetration testing service providers
- · Legacy security software vendors
Automated penetration testing policies become more effective at identifying complex vulnerabilities, reducing mean time to detection.
The proliferation of AI-driven cyber defense tools leads to an 'arms race' with AI-driven cyberattack tools, escalating the complexity of cyber warfare.
The reliance on AI for cybersecurity could introduce new attack surfaces if the AI models themselves are compromised or biased, leading to undetected vulnerabilities.
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