Tianneng Shi

Tianneng Shi, Jingxuan He, Zhun Wang et al.

LLM agents utilize Large Language Models as central components with diverse tools to complete various user tasks, but face significant security risks when interacting with external environments. Attackers can exploit these agents through various vectors, including indirect prompt injection, memory/knowledge base poisoning, and malicious tools, tricking agents into performing dangerous actions such as unauthorized financial transactions or data leakage. The core problem that enables attacks to succeed lies in over-privileged tool access. We introduce Progent, the first privilege control framework to secure LLM agents. Progent enforces security at the tool level by restricting agents to performing tool calls necessary for user tasks while blocking potentially malicious ones. Progent features a domain-specific language that allows for expressing fine-grained policies for controlling tool privileges, flexible fallback actions when calls are blocked, and dynamic policy updates to adapt to changing agent states. The framework operates deterministically at runtime, providing provable security guarantees. Thanks to our modular design, integrating Progent does not alter agent internals and only requires minimal changes to the existing agent implementation, enhancing its practicality and potential for widespread adoption. Our extensive evaluation across various agent use cases, using benchmarks like AgentDojo, ASB, and AgentPoison, demonstrates that Progent reduces attack success rates to 0%, while preserving agent utility and speed. Additionally, we show that LLMs can automatically generate effective policies, highlighting their potential for automating the process of writing Progent's security policies.

Yujin Potter, Wenbo Guo, Zhun Wang et al.

The impact of frontier AI in cybersecurity is rapidly increasing. In this paper, we comprehensively analyze this trend through three distinct lenses: a quantitative benchmark analysis, a literature review, and an expert survey. We find that while AI is already widely used in attacks, its application in defense remains limited, especially in remediation and deployment. Aligned with these analyses, experts expect AI to continue favoring attackers over defenders, though the gap will gradually narrow. These findings underscore the urgent need to mitigate frontier AI's risks while closely monitoring emerging capabilities. We provide concrete calls-to-action regarding: the construction of new cybersecurity benchmarks, the development of AI agents for defense, the design of provably secure AI agents, the improvement of pre-deployment security testing and transparency, and the strengthening of user-oriented education and defenses. Our paper summary and blog are available at https://rdi.berkeley.edu/frontier-ai-impact-on-cybersecurity/.

Zhun Wang, Tianneng Shi, Jingxuan He et al.

AI agents have significant potential to reshape cybersecurity, making a thorough assessment of their capabilities critical. However, existing evaluations fall short, because they are based on small-scale benchmarks and only measure static outcomes, failing to capture the full, dynamic range of real-world security challenges. To address these limitations, we introduce CyberGym, a large-scale benchmark featuring 1,507 real-world vulnerabilities across 188 software projects. Adjustable to different vulnerability analysis settings, CyberGym primarily tasks agents with generating a proof-of-concept test that reproduces a vulnerability, given only its text description and the corresponding codebase. Our extensive evaluation highlights that CyberGym effectively differentiates agents' and models' cybersecurity capabilities. Even the top-performing combinations only achieve a ~20% success rate, demonstrating the overall difficulty of CyberGym. Beyond static benchmarking, we show that CyberGym leads to the discovery of 35 zero-day vulnerabilities and 17 historically incomplete patches. These results underscore that CyberGym is not only a robust benchmark for measuring AI's progress in cybersecurity but also a platform for creating direct, real-world security impact.