arXiv:2606.03453v1 Announce Type: cross Abstract: Vulnerability disclosure volumes now far exceed organizational assessment capacity, yet three adjacent research communities (proof-of-concept generation, vulnerability prioritization, and detection rule engineering) operate largely in isolation. Existing automated exploit generation systems report binary pass/fail outcomes, discarding partial progress and producing no signal for the other two communities. This paper presents FORGE, a multi-agent system that bridges these three silos through graduated exploitation depth. Five specialized agents
The increasing volume of software vulnerabilities necessitates more sophisticated and integrated approaches to exploit generation and detection, pushing for AI-driven solutions.
This development indicates a significant advancement in automated cybersecurity, potentially accelerating the arms race between attackers and defenders and impacting software development cycles.
Traditional siloed approaches to vulnerability management (PoC generation, prioritization, detection rule engineering) are being integrated into a multi-agent AI system, enhancing efficiency and depth.
- · Cybersecurity companies
- · Software developers (using enhanced security tools)
- · Organizations with complex software estates
- · Vulnerability exploit brokers (potentially)
- · Attackers relying on known exploits
- · Organizations with poor security practices
Automated systems can more rapidly identify and generate exploits for vulnerabilities, closing disclosure gaps.
This could lead to widespread adoption of similar multi-agent systems for offensive and defensive cybersecurity, escalating the sophistication of cyber warfare.
The increased automation in vulnerability management might shift the focus of human cybersecurity experts towards more complex, zero-day threat intelligence and red-teaming strategies.
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Read at arXiv cs.AI