Towards Reliable Local Security Agents: Verifiable Post-Training for Linux Privilege Escalation
arXiv:2603.17673v2 Announce Type: replace-cross Abstract: LLM agents are becoming increasingly important in the security domain, but leading systems are often closed-source, cloud-based, hard to reproduce or use with sensitive code. This creates a need for small, local models that can perform security tasks under strict resource constraints, though effective methods for developing them remain unexplored. In this paper, we address this gap by proposing a two-stage post-training recipe that turns a small local language model into a security agent. To this end, we focus on Linux privilege escalat
The increasing reliance on LLM agents in security, coupled with the need for high-assurance, local, and reproducible solutions, drives the immediate necessity for post-training methods that address these operational constraints.
Developing verifiable post-training for small, local LLMs significantly enhances the security posture of critical infrastructure by offering transparent and auditable AI agents, reducing reliance on opaque cloud-based solutions.
The ability to turn small, local language models into effective and verifiable security agents for critical tasks like privilege escalation redefines the viability of decentralized and air-gapped AI security deployments.
- · Open-source AI foundations
- · Cybersecurity firms
- · Developers of custom AI agents
- · Organizations with strict data locality requirements
- · Closed-source cloud-based security AI providers
- · Traditional endpoint security solutions
- · Attackers relying on common privilege escalation vectors
Enhances the deployment of AI-powered security features in sensitive environments where cloud access is restricted or undesirable.
Accelerates the development of a diverse ecosystem of specialized, auditable local AI agents across various enterprise functions beyond security.
Potentially democratizes advanced AI security capabilities, reducing the competitive advantage of entities with superior access to large, proprietary models or cloud compute.
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Read at arXiv cs.AI