arXiv:2603.19864v2 Announce Type: replace Abstract: Penetration testing, the practice of simulating cyberattacks to identify vulnerabilities, is a complex sequential decision-making task that is inherently partially observable and features large action spaces. Training reinforcement learning (RL) policies for this domain faces a fundamental bottleneck: existing simulators are too slow to train on realistic network scenarios at scale, resulting in policies that fail to generalize. We present NASimJax, a complete JAX-based reimplementation of the Network Attack Simulator (NASim), achieving up to
The increasing sophistication and scale of cyber threats require more advanced and efficient tools for vulnerability testing, pushing the development of faster AI-driven penetration testing frameworks.
This development significantly enhances the ability to identify and mitigate cybersecurity vulnerabilities at scale, improving organizational resilience against cyberattacks and potentially shaping future cyber warfare capabilities.
The ability to train more effective reinforcement learning policies for penetration testing at scale shifts the landscape of cybersecurity defenses and offensive capabilities, as faster and more generalizable AI agents become possible.
- · Cybersecurity firms
- · Defense contractors
- · Organizations with complex network infrastructures
- · AI/ML developers
- · Cyber attackers (potentially)
- · Organizations with slow or traditional security practices
More robust and resilient digital infrastructure due to improved vulnerability identification.
Increased demand for specialized AI/ML security professionals and the integration of AI into standard cyber defense operations.
Escalation of the 'cyber arms race' as both defenders and attackers leverage increasingly sophisticated AI tools, potentially leading to novel forms of conflict.
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