Data-Centric Benchmarking of Exploit Generation in LLMs: Understanding the Impact of Fine-Tuning
arXiv:2606.15123v1 Announce Type: cross Abstract: We study the task of CVE-conditioned exploit generation, where a model drafts proof-of-concept (PoC) exploits given software vulnerability context. We adopt a data-centric approach, constructing a high-quality dataset via multi-stage preprocessing and introducing a scalable evaluation framework with LLM-as-judge and fine-grained rubrics. Under this unified setup, we benchmark 17 large language models across 8 evaluation criteria, providing systematic insights into their zero-shot capabilities. We further show that a compact 8B open-weight model
The rapid advancement and widespread deployment of large language models are concurrently exposing their potential for dual-use applications, prompting urgent research into their security implications and misuse capabilities.
This research provides a structured method for benchmarking the exploit generation capabilities of LLMs, which is critical for developing robust cybersecurity defenses and responsible AI practices, impacting both enterprise security and national defense.
The systematic evaluation framework will shift how exploit generation capabilities in LLMs are understood and measured, leading to more targeted security mitigations and potentially accelerating red-teaming efforts and defensive AI development.
- · Cybersecurity firms
- · AI safety researchers
- · Organizations with strong defensive AI capabilities
- · Open-source AI foundations improving model safety
- · Organizations with weak cybersecurity postures
- · AI developers ignoring dual-use risks
- · Vulnerable software vendors
- · Bad actors relying on unsophisticated attack vectors
The ability of LLMs to generate exploits will become a standard benchmark in AI security, similar to traditional CVE databases.
This will drive significant investment in defensive AI technologies specifically designed to counter LLM-generated threats, potentially accelerating the AI arms race between offensive and defensive capabilities.
The democratization of exploit generation through highly capable LLMs could fundamentally alter the cyber threat landscape, making nation-state level offensive capabilities accessible to a broader range of actors.
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Read at arXiv cs.LG