Can Open-Source LLM Agents Replace Static Application Security Testing Tools? An Empirical Assessment
arXiv:2606.11672v1 Announce Type: cross Abstract: This paper explores the value of agentic AI tools for cybersecurity purposes. We evaluate the efficacy of a general-purpose GenAI Large Language Model- (GenAI-) based agent when powered by three different Ollama-hosted general-purpose open source models. We assess each agent's performance using precision, recall, false positive count, and a calculated composite score based upon the interplay of the captured metrics, against the baseline performance of an existing, vetted Static Application Security Testing (SAST) tool, Bandit. Our findings refu
The rapid advancement and widespread availability of open-source LLMs and agentic paradigms are prompting direct comparisons with established cybersecurity tools.
This empirical assessment could validate AI agents as viable or superior alternatives for critical security tasks, significantly impacting the cybersecurity software market and developer practices.
The potential for LLM-based agents to replace or augment traditional Static Application Security Testing (SAST) tools introduces a new highly customizable and potentially cost-effective method for vulnerability detection.
- · Open-source LLM developers
- · Organizations adopting AI agents for security
- · Cybersecurity professionals leveraging AI tools
- · Traditional SAST tool vendors
- · Legacy cybersecurity solution providers
- · Developers resistant to AI-driven security automation
Increased adoption and integration of AI agents into software development lifecycle (SDLC) for security purposes.
Disruption of the traditional cybersecurity tooling market, leading to new specialized AI-agent security companies.
Elevated cyber-risk landscape due to the potential for adversarial AI agents or new types of AI-generated vulnerabilities.
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Read at arXiv cs.AI