arXiv:2607.01764v1 Announce Type: new Abstract: Repository-level vulnerability reproduction is a demanding software engineering (SE) task: an agent must inspect a codebase, infer the input grammar that reaches a vulnerable path, construct a proof-of-conceptv(PoC), and verify that the crash disappears on the patched build. Recent LLM agents can often execute these steps when the approach is correct, yet they still fail by choosing the wrong strategy. This paper argues that strategy, rather than the full action trajectory, is the right learning unit for such SE agents: it is compact enough to op
The rapid advancement and adoption of LLM agents across various software engineering tasks necessitates improved strategies for complex operations like vulnerability reproduction.
Improving the strategic capabilities of AI agents in cybersecurity directly impacts software security, reducing human effort and enhancing defensive postures at scale.
The focus shifts from full action trajectories to strategy as the core learning unit for AI agents in critical engineering tasks, potentially leading to more robust and scalable solutions.
- · Cybersecurity firms
- · Software developers
- · AI agent developers
- · Organizations with large code bases
- · Malicious actors exploiting vulnerabilities manually
- · Legacy vulnerability assessment tools
More efficient and automated discovery and remediation of software vulnerabilities.
A significant reduction in zero-day exploits due to proactive AI-driven vulnerability reproduction.
The democratization of advanced vulnerability research, allowing smaller teams to achieve enterprise-grade security.
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Read at arXiv cs.AI